Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on t...In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming(GEP).Firstly,the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion,so as to eliminate outliers.The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient(MIC).Secondly,the explicit prediction models of Ys and Ts are established using GEP.Subsequently,the model results based on GEP are compared with those based on the support vector regression(SVR)and the back propagation neural network(BPNN).Finally,the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property.It is shown that the errors of Ys and Ts based on GEP are less than 4%,and the coefficient of determination(R^(2))of Ys and Ts based on GEP is above 0.9,which has strong prediction performance.The prediction accuracy of GEP can achieve the same level with SVR and BPNN.It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition,production process,and mechanical property of strip steel,but also occupy high prediction accuracy,which can make reliable reference for strip steel product design and optimisation.展开更多
Ebis is the intelligent environmental biotechnological informatics software developed for judging the effectiveness of the microorganism strain in the industrial wastewater treatment system(IWTS) at the optimal status...Ebis is the intelligent environmental biotechnological informatics software developed for judging the effectiveness of the microorganism strain in the industrial wastewater treatment system(IWTS) at the optimal status. The parameter, as the objective function for the judgment, is the minimum reactor volume( V _ min ) calculated by Ebis for microorganism required in wastewater treatment. The rationality and the universality of Ebis were demonstrated in the domestic sewage treatment system(DSTS) with the data published in USA and China at first,then Fhhh strain's potential for treating the purified terephthalic acid(PTA) was proved. It suggests that Ebis would be useful and universal for predicating the technique effectiveness in both DSTS and IWTS.展开更多
It has been known that the productivity of artesian wells is strongly dependent on the rheological properties of crude oils. This work targets two deep artesian wells(>5000 m) that are producing heavy crude oil. Th...It has been known that the productivity of artesian wells is strongly dependent on the rheological properties of crude oils. This work targets two deep artesian wells(>5000 m) that are producing heavy crude oil. The impacts of well conditions including temperature, pressure and shear rate, on the crude oil rheology were comprehensively investigated and correlated using several empirical rheological models. The experimental data indicate that this heavy oil is very sensitive to temperature as result of microstructure change caused by hydrogen bonding. The rheological behavior of the heavy oil is also significantly impacted by the imposed pressure, i.e., the viscosity flow activation energy(Eμ) gently increases with the increasing pressure. The viscosity–shear rate data are well fitted to the power law model at low temperature. However, due to the transition of fluid feature at high temperature(Newtonian fluid), the measured viscosity was found to slightly deviate from the fitting data. Combining the evaluated correlations, the viscosity profile of the heavy crude oil in these two deep artesian wells as a function of well depth was predicted using the oilfield producing data.展开更多
Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacoki...Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.展开更多
By analyzing the metallogenic conditions and prospecting marks of F 8 fault belt in Shiujingtun Gold Mine, the geochemical samples were collected along F 8 fault belt and prospecting profile normal to the F 8 fault be...By analyzing the metallogenic conditions and prospecting marks of F 8 fault belt in Shiujingtun Gold Mine, the geochemical samples were collected along F 8 fault belt and prospecting profile normal to the F 8 fault belt. Gold and its indicator elements were tested with X ray fluorescence spectrometry and the content distribution diagram of Au, Ag, Hg and As along the F 8 fault belt was performed. The geochemical primary halo model and the Grey system model of F 8 fault belt are established. With these element distribution features and models, the blind ore bodies in the F 8 fault belt were predicted. Engineering prospect shows that the industrial orebodies have been discovered and the prediction results are dependable.展开更多
The vapor-liquid equilibrium of Dimethyl Carbonate-Methanol-Furfural under atmospheric pressure from DMC-CH 3OH,DMCC 5H 4O 2,CH 3OH-C 5H 4O 2 binary systematic VLE data is calculated,by using C ++(VC6.0) pr...The vapor-liquid equilibrium of Dimethyl Carbonate-Methanol-Furfural under atmospheric pressure from DMC-CH 3OH,DMCC 5H 4O 2,CH 3OH-C 5H 4O 2 binary systematic VLE data is calculated,by using C ++(VC6.0) programming language and Wilson equation.It provided important VLE data to set up mathematic models of extraction-rectifying separation of DMC and methanol by using furfural as extraction reagent.So the results can be used for chemical engineering calculation.展开更多
According to the characteristic of Beidou Double-star positing system (for short: Double-star position), the optimal predication model of Double-star position/SINS integrated system is put forward, which can make use ...According to the characteristic of Beidou Double-star positing system (for short: Double-star position), the optimal predication model of Double-star position/SINS integrated system is put forward, which can make use of the delayed position in-formation from Double-star positioning system to predicate optimally for the integrated system, and then to correct SINS, and af-fords integrated results of some navigation parameters. In order to validate the consistency of the filter, the criteria for consistency of a filter is also studied, and the tested statistics are given, the experiment based on practical measured data shows that the filtering method is consistent with the integrated system.展开更多
The purified terephthalic acid （PTA） petrochemical wastewater molecular toxicity detected by use of Mouse Genome 430A 2.0 GeneChip was conducted in this research. The toxic dose to male mice was 0.03 g/（kg, d） of ...The purified terephthalic acid （PTA） petrochemical wastewater molecular toxicity detected by use of Mouse Genome 430A 2.0 GeneChip was conducted in this research. The toxic dose to male mice was 0.03 g/（kg, d） of PTA in the wastewater. The mice liver total RNA was isolated as the temple for synthesis of eDNA and then the cDNA as the temple for synthesis of cRNA. Hybridizing the cRNA with the target genes on the gene chip, there were 232 genes expression levels up-regulated and 74 genes down-regulated discovered obviously. The foremost 40 genes for both the highest and the lowest expression levels involved endogenetic steroid and hormone metabolism, immune system, the leukocyte activity and inflammation, detoxification in liver, reproduction and growth hormone, regulation immune factors of anti-tumor and anti-infection and cancer to the mice sampled. The data suggest the PTA wastewater contained over 5 aromatics and their toxicities integrated were much higher than the pure chemical PTA. And the pure chemical PTA toxicities data cannot be used to evaluate the toxicity of the PTA wastewater instead.展开更多
1.IntroductionThe research of metal forming theoryand technology always takes an importantplace in materials science and engineering.However,in a long period,the developmentof technology goes ahead of the theoreticals...1.IntroductionThe research of metal forming theoryand technology always takes an importantplace in materials science and engineering.However,in a long period,the developmentof technology goes ahead of the theoreticalstudy.Most design principles used in prac-展开更多
Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component...Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.展开更多
Aiming at Double-Star positioning system's shortcomings of delayed position information and easy exposition of the user as well as the error increase of the SINS with the accumulation of time, the integration of D...Aiming at Double-Star positioning system's shortcomings of delayed position information and easy exposition of the user as well as the error increase of the SINS with the accumulation of time, the integration of Double-Star positioning system and the SINS is one of the developing directions for an integrated navigation system. This paper puts forward an optimal predication method of Double-Star/SINS integrated system based on discrete integration, which can make use of the delayed position information of Double-Star positioning system to optimally predicate the integrated system, and then corrects the SINS. The experimental results show that this method can increase the user's concealment under the condition of assuring the system's accuracy.展开更多
Plato＇s lifelong confrontation with Parmenides and his metaphysical mire of believing that nothing （το μη ǒν） does not actually exist, gradually in the Sophist comes into finish, insofar as the philosopher a...Plato＇s lifelong confrontation with Parmenides and his metaphysical mire of believing that nothing （το μη ǒν） does not actually exist, gradually in the Sophist comes into finish, insofar as the philosopher after facing the foe and having the last laugh simmers down. In this paper after giving an interpretation of what Parmenides says, I shall present an analysis of Plato＇s drastic answer to him （Sophist, 259 e4-6） to see how Plato opens the impasse way created by the Eleatic philosopher. Here the intercommunion of Forms is regarded as the final answer by which Plato devastates Parmenides infamous thesis. Since hitherto no in-depth analysis is given by the scholars who are puzzled with the subject, I have tried to analyze the intercommunion of Forms philosophically. Plato＇s Eleatic challenge has always been crucial in Plato himself and philosophical development after him. As while as Parmenides thesis （Sph., 238 a8-9） provides the sophists opportunity to reject the falsehood, Plato＇s theory of Forms in contrast in order to cross off the extremely sly sophists tries to make Parmenides come down. In my opinion, the intercommunion of Forms, as the last step of the theory of Forms, basically determines Plato＇s late ontology tightly knitted with logic. Vindicating this proposal depends on true understanding of the intercommunion of Forms. Since Plato＇s late ontology, in my opinion, is closed to Frege＇s ontology and discussion of language, we are armed to interpret the intercommunion of Forms with recent recent logico-philosophicus achievements, I think. In this respect, this is what I have done in my paper： analyzing sentence from Plato＇s logico-metaphysical point of view. Ultimately, I have tried to show how the aim of the intercommunion of Forms, which Plato himself states, is demonstrating the possibility of dialogue and discourse. This statement explicitly sets forward that the discussion is bound up with several logical approaches, according to which finally full bright light is shed on different implications of the subject such as universals.展开更多
Vladimir Markin proposes a certain construction---a generalisation of syllogistic--in which he uses the constant @ with indef＇mite arity. The atomic formulae are of the following sort： S1S2 ...Sm@P1P2...Pn, where re...Vladimir Markin proposes a certain construction---a generalisation of syllogistic--in which he uses the constant @ with indef＇mite arity. The atomic formulae are of the following sort： S1S2 ...Sm@P1P2...Pn, where re＋n〉0. The standard syllogistic functors are here interpreted as follows： SAP=： S@P SeP=： SP@ SIP=： -SP@ SOP=： ~S@P Markin constructs a system of Fundamental Syllogistic （FS） with constant @ in an axiomatic way. Based on Markin＇s idea, we propose two constructions, which are formulations of the system of sequential predication built upon the quantifier-less calculus of names. The first one includes the FS system. The second one is enriched with individual variables and, among other things, allows including sequences of individual names in which one has to do with enumerative functors. The counterpart of Hao Wang＇s algorithm holds in the first system extended with negative terms.展开更多
In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more preci...In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more precise. Constrained sparse spike inversion(CSSI) has advantage in oil and gas reservoir predication because it does not rely on the original model. By analyzing the specific algorithm of CSSI,the accuracy of inversion is controlled. Oriente Basin in South America has the low amplitude in geological structure and complex lithologic trap. The well predication is obtained by the application of CSSI.展开更多
This paper analyzes errors in college students' oral English in class. There are three types of errors: interferences errors, intralingual errors and developmental errors. All the three kinds of errors can be foun...This paper analyzes errors in college students' oral English in class. There are three types of errors: interferences errors, intralingual errors and developmental errors. All the three kinds of errors can be found when students speak English. Then the paper introduces strategies for error correction from three aspects: before students' speech, during their speech and after their speech.展开更多
Microplastics(MPs)in river ecosystems significantly affect sediment microbial communities and biogeochemical cycling.However,the specific microbial responses to distinct MPs and their subsequent effects on greenhouse ...Microplastics(MPs)in river ecosystems significantly affect sediment microbial communities and biogeochemical cycling.However,the specific microbial responses to distinct MPs and their subsequent effects on greenhouse gas(GHG)emissions remain poorly understood.In this study,the effects of conventional polyvinyl chloride(PVC)and biodegradable polylactic acid(PLA)on sediment microbial communities and GHG emissions were compared.Both PVC and PLA increased sediment total organic carbon(STOC)by 31.3%and 36.7%,respectively,leading to an increase in sediment bacterial abundance.Notably,compared with PLA,PVC reduced bacterial richness more significantly.Community composition and FAPROTAX function prediction analysis revealed that PVC-enriched taxa involved in nitrate reduction(e.g.,Desulfuromonas,Hydrogenophage,Azospira)resulted in a significant decrease in NO3−contents.In contrast,PLA increased the abundance of microorganisms associated with organic matter degradation(Chitinophagaceae,Comamonadaceae,and Caulobacteraceae).Both PVC and PLA significantly reduced the relative abundance of the mcrA gene,leading to decreased CH4 fluxes,likely due to competition from enriched acetate-utilizing bacteria(Desulfuromonas,Pseudomonas,and Azospira).Additionally,PLA significantly reduced the abundance of the nirK gene.This study systematically elucidates the differential effects of PVC and PLA on microbial community structure and GHG emissions,providing new insights into the ecological impacts of MPs.展开更多
A model for performance prediction of multistage centrifugal compressor is proposed. The model allows the users to predict the compressor performance, e.g. pressure ratio, efficiency and losses using the compressor ge...A model for performance prediction of multistage centrifugal compressor is proposed. The model allows the users to predict the compressor performance, e.g. pressure ratio, efficiency and losses using the compressor geometric information and speed by a stage stacking calculation based on the characteristics of each stage. To develop the compressor elemental stage charac- teristics, the compressor losses, such as incidence losses and friction losses, are mathematically modeled. For a composite sys- tems, for instance a gas turbine power plant, the performance of the multistage centrifugal compressor can be evaluated. Since some important parameters of the compressor model, e.g., the slip factor or, shock loss coefficient （and reference diameter DI, are hard to be determined by empirical laws, a genetic algorithm （GA） is used to solve the parameter estimation problem of the proposed model, and in turn the compressor performance analysis and parameters study are performed. The surge line for the multistage centrifugal compressor can also be determined from the simulation results. Furthermore, the model presented here provides a valuable tool for evaluating the multistage centrifugal compressor performance as a function of various operation parameters.展开更多
Gaofen-3(GF-3)is the first Chinese spaceborne multi-polarization synthetic aperture radar(SAR)instrument at C-band(5.43 GHz).In this paper,we use data collected from GF-3 to observe Super Typhoon Lekima(2019)in the Ea...Gaofen-3(GF-3)is the first Chinese spaceborne multi-polarization synthetic aperture radar(SAR)instrument at C-band(5.43 GHz).In this paper,we use data collected from GF-3 to observe Super Typhoon Lekima(2019)in the East China Sea.Using a VH-polarized wide ScanSAR(WSC)image,ocean surface wind speeds at 100m horizontal resolution are obtained at 21:56:59 UTC on 8 August 2019,with the maximum wind speed,38.9 m·s^(-1).Validating the SAR-retrieved winds with buoymeasured wind speeds,we find that the root mean square error(RMSE)is 1.86 m·s^(-1),and correlation coefficient,0.92.This suggests that wind speeds retrieved from GF-3 SAR are reliable.Both the European Centre for MediumRange Weather Forecasts(ECMWF)fine grid operational forecast products with spatial resolution,and China Global/Regional Assimilation and Prediction Enhance System(GRAPES)have good performances on surface wind prediction under weak wind speed condition(<24 m·s^(-1)),but underestimate the maximum wind speed when the storm is intensified as a severe tropical storm(>24m·s^(-1)).With respect to SAR-retrieved wind speeds,the RMSEs are 5.24 m·s^(–1) for ECMWF and 5.17 m·s^(–1) for GRAPES,with biases of 4.16 m·s^(–1) for ECMWF and 3.84 m·s^(-1)for GRAPES during Super Typhoon Lekima(2019).展开更多
Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(M...Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is limited.This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.Methods This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,China.The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September 2004.We collected and analyzed mortality data up to December 2018.Medical records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial infarction.Using the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline method.Results The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,and PXGBoost<0.05).Adding bPEP and bET to our feature set significantly improved the performance of the algorithm,leading to an absolute increase in C-statistic of up to 0.03(C-statistic,0.83 for random forest,0.82 for AdaBoost,and 0.80 for XGBoost,vs.0.74 for LR)(PRF<0.001,PAdaBoost<0.001,PXGBoost<0.05).Conclusion The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases.This advancement may enable better treatment prioritization for high-risk individuals.展开更多
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.52074187 and 52274388)Liaoning Province Artificial Intelligence Innovation and Development Plan Project(Major Science and Technology Project)(2023JH26-10100002)the National Key Research and Development Program of China(No.2022YFB3304800).
文摘In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming(GEP).Firstly,the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion,so as to eliminate outliers.The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient(MIC).Secondly,the explicit prediction models of Ys and Ts are established using GEP.Subsequently,the model results based on GEP are compared with those based on the support vector regression(SVR)and the back propagation neural network(BPNN).Finally,the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property.It is shown that the errors of Ys and Ts based on GEP are less than 4%,and the coefficient of determination(R^(2))of Ys and Ts based on GEP is above 0.9,which has strong prediction performance.The prediction accuracy of GEP can achieve the same level with SVR and BPNN.It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition,production process,and mechanical property of strip steel,but also occupy high prediction accuracy,which can make reliable reference for strip steel product design and optimisation.
文摘Ebis is the intelligent environmental biotechnological informatics software developed for judging the effectiveness of the microorganism strain in the industrial wastewater treatment system(IWTS) at the optimal status. The parameter, as the objective function for the judgment, is the minimum reactor volume( V _ min ) calculated by Ebis for microorganism required in wastewater treatment. The rationality and the universality of Ebis were demonstrated in the domestic sewage treatment system(DSTS) with the data published in USA and China at first,then Fhhh strain's potential for treating the purified terephthalic acid(PTA) was proved. It suggests that Ebis would be useful and universal for predicating the technique effectiveness in both DSTS and IWTS.
基金Supported by the National Key Science&Technology Projects during 13th Five-Year Plan(2016ZX05053-003)Young Scholars Development fund of SWPU(201499010121)
文摘It has been known that the productivity of artesian wells is strongly dependent on the rheological properties of crude oils. This work targets two deep artesian wells(>5000 m) that are producing heavy crude oil. The impacts of well conditions including temperature, pressure and shear rate, on the crude oil rheology were comprehensively investigated and correlated using several empirical rheological models. The experimental data indicate that this heavy oil is very sensitive to temperature as result of microstructure change caused by hydrogen bonding. The rheological behavior of the heavy oil is also significantly impacted by the imposed pressure, i.e., the viscosity flow activation energy(Eμ) gently increases with the increasing pressure. The viscosity–shear rate data are well fitted to the power law model at low temperature. However, due to the transition of fluid feature at high temperature(Newtonian fluid), the measured viscosity was found to slightly deviate from the fitting data. Combining the evaluated correlations, the viscosity profile of the heavy crude oil in these two deep artesian wells as a function of well depth was predicted using the oilfield producing data.
基金Project(31200748)supported by the National Natural Science Foundation of China
文摘Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.
基金TheOutstandingYoungScientistsFoundation !(No496 2 5304)andtheKeyProgramofMinistryofScienceandTechnologyofChina !(No95 pre 3
文摘By analyzing the metallogenic conditions and prospecting marks of F 8 fault belt in Shiujingtun Gold Mine, the geochemical samples were collected along F 8 fault belt and prospecting profile normal to the F 8 fault belt. Gold and its indicator elements were tested with X ray fluorescence spectrometry and the content distribution diagram of Au, Ag, Hg and As along the F 8 fault belt was performed. The geochemical primary halo model and the Grey system model of F 8 fault belt are established. With these element distribution features and models, the blind ore bodies in the F 8 fault belt were predicted. Engineering prospect shows that the industrial orebodies have been discovered and the prediction results are dependable.
文摘The vapor-liquid equilibrium of Dimethyl Carbonate-Methanol-Furfural under atmospheric pressure from DMC-CH 3OH,DMCC 5H 4O 2,CH 3OH-C 5H 4O 2 binary systematic VLE data is calculated,by using C ++(VC6.0) programming language and Wilson equation.It provided important VLE data to set up mathematic models of extraction-rectifying separation of DMC and methanol by using furfural as extraction reagent.So the results can be used for chemical engineering calculation.
文摘According to the characteristic of Beidou Double-star positing system (for short: Double-star position), the optimal predication model of Double-star position/SINS integrated system is put forward, which can make use of the delayed position in-formation from Double-star positioning system to predicate optimally for the integrated system, and then to correct SINS, and af-fords integrated results of some navigation parameters. In order to validate the consistency of the filter, the criteria for consistency of a filter is also studied, and the tested statistics are given, the experiment based on practical measured data shows that the filtering method is consistent with the integrated system.
基金The Ph. D Fund of the National Education Ministry of China (No. 20030284038) and the Hi-Tech Research and DevelopmentProgram(863) of China(No. 2001AA214191)
文摘The purified terephthalic acid （PTA） petrochemical wastewater molecular toxicity detected by use of Mouse Genome 430A 2.0 GeneChip was conducted in this research. The toxic dose to male mice was 0.03 g/（kg, d） of PTA in the wastewater. The mice liver total RNA was isolated as the temple for synthesis of eDNA and then the cDNA as the temple for synthesis of cRNA. Hybridizing the cRNA with the target genes on the gene chip, there were 232 genes expression levels up-regulated and 74 genes down-regulated discovered obviously. The foremost 40 genes for both the highest and the lowest expression levels involved endogenetic steroid and hormone metabolism, immune system, the leukocyte activity and inflammation, detoxification in liver, reproduction and growth hormone, regulation immune factors of anti-tumor and anti-infection and cancer to the mice sampled. The data suggest the PTA wastewater contained over 5 aromatics and their toxicities integrated were much higher than the pure chemical PTA. And the pure chemical PTA toxicities data cannot be used to evaluate the toxicity of the PTA wastewater instead.
文摘1.IntroductionThe research of metal forming theoryand technology always takes an importantplace in materials science and engineering.However,in a long period,the developmentof technology goes ahead of the theoreticalstudy.Most design principles used in prac-
文摘Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.
基金the National Defence Pre-research Foundation (Grant No.413090303)Special Fund for Author of Countrywide Excellent Doctor Disserta-tion (Grant No.2000036)
文摘Aiming at Double-Star positioning system's shortcomings of delayed position information and easy exposition of the user as well as the error increase of the SINS with the accumulation of time, the integration of Double-Star positioning system and the SINS is one of the developing directions for an integrated navigation system. This paper puts forward an optimal predication method of Double-Star/SINS integrated system based on discrete integration, which can make use of the delayed position information of Double-Star positioning system to optimally predicate the integrated system, and then corrects the SINS. The experimental results show that this method can increase the user's concealment under the condition of assuring the system's accuracy.
文摘Plato＇s lifelong confrontation with Parmenides and his metaphysical mire of believing that nothing （το μη ǒν） does not actually exist, gradually in the Sophist comes into finish, insofar as the philosopher after facing the foe and having the last laugh simmers down. In this paper after giving an interpretation of what Parmenides says, I shall present an analysis of Plato＇s drastic answer to him （Sophist, 259 e4-6） to see how Plato opens the impasse way created by the Eleatic philosopher. Here the intercommunion of Forms is regarded as the final answer by which Plato devastates Parmenides infamous thesis. Since hitherto no in-depth analysis is given by the scholars who are puzzled with the subject, I have tried to analyze the intercommunion of Forms philosophically. Plato＇s Eleatic challenge has always been crucial in Plato himself and philosophical development after him. As while as Parmenides thesis （Sph., 238 a8-9） provides the sophists opportunity to reject the falsehood, Plato＇s theory of Forms in contrast in order to cross off the extremely sly sophists tries to make Parmenides come down. In my opinion, the intercommunion of Forms, as the last step of the theory of Forms, basically determines Plato＇s late ontology tightly knitted with logic. Vindicating this proposal depends on true understanding of the intercommunion of Forms. Since Plato＇s late ontology, in my opinion, is closed to Frege＇s ontology and discussion of language, we are armed to interpret the intercommunion of Forms with recent recent logico-philosophicus achievements, I think. In this respect, this is what I have done in my paper： analyzing sentence from Plato＇s logico-metaphysical point of view. Ultimately, I have tried to show how the aim of the intercommunion of Forms, which Plato himself states, is demonstrating the possibility of dialogue and discourse. This statement explicitly sets forward that the discussion is bound up with several logical approaches, according to which finally full bright light is shed on different implications of the subject such as universals.
文摘Vladimir Markin proposes a certain construction---a generalisation of syllogistic--in which he uses the constant @ with indef＇mite arity. The atomic formulae are of the following sort： S1S2 ...Sm@P1P2...Pn, where re＋n〉0. The standard syllogistic functors are here interpreted as follows： SAP=： S@P SeP=： SP@ SIP=： -SP@ SOP=： ~S@P Markin constructs a system of Fundamental Syllogistic （FS） with constant @ in an axiomatic way. Based on Markin＇s idea, we propose two constructions, which are formulations of the system of sequential predication built upon the quantifier-less calculus of names. The first one includes the FS system. The second one is enriched with individual variables and, among other things, allows including sequences of individual names in which one has to do with enumerative functors. The counterpart of Hao Wang＇s algorithm holds in the first system extended with negative terms.
基金Supported by the Fundamental Research Funds for the Central Universities(No.2011PY0186)
文摘In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more precise. Constrained sparse spike inversion(CSSI) has advantage in oil and gas reservoir predication because it does not rely on the original model. By analyzing the specific algorithm of CSSI,the accuracy of inversion is controlled. Oriente Basin in South America has the low amplitude in geological structure and complex lithologic trap. The well predication is obtained by the application of CSSI.
文摘This paper analyzes errors in college students' oral English in class. There are three types of errors: interferences errors, intralingual errors and developmental errors. All the three kinds of errors can be found when students speak English. Then the paper introduces strategies for error correction from three aspects: before students' speech, during their speech and after their speech.
基金supported by the Fund for National Key R&D Program of China(2021YFC3200401)the National Natural Science Foundation of China(Grant Nos.52170024 and 52039001).
文摘Microplastics(MPs)in river ecosystems significantly affect sediment microbial communities and biogeochemical cycling.However,the specific microbial responses to distinct MPs and their subsequent effects on greenhouse gas(GHG)emissions remain poorly understood.In this study,the effects of conventional polyvinyl chloride(PVC)and biodegradable polylactic acid(PLA)on sediment microbial communities and GHG emissions were compared.Both PVC and PLA increased sediment total organic carbon(STOC)by 31.3%and 36.7%,respectively,leading to an increase in sediment bacterial abundance.Notably,compared with PLA,PVC reduced bacterial richness more significantly.Community composition and FAPROTAX function prediction analysis revealed that PVC-enriched taxa involved in nitrate reduction(e.g.,Desulfuromonas,Hydrogenophage,Azospira)resulted in a significant decrease in NO3−contents.In contrast,PLA increased the abundance of microorganisms associated with organic matter degradation(Chitinophagaceae,Comamonadaceae,and Caulobacteraceae).Both PVC and PLA significantly reduced the relative abundance of the mcrA gene,leading to decreased CH4 fluxes,likely due to competition from enriched acetate-utilizing bacteria(Desulfuromonas,Pseudomonas,and Azospira).Additionally,PLA significantly reduced the abundance of the nirK gene.This study systematically elucidates the differential effects of PVC and PLA on microbial community structure and GHG emissions,providing new insights into the ecological impacts of MPs.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61174130,61004083,61074074)the National Basic Research Program of China ("973" Program) (Grant No.2009CB320601)Fundamental Research Funds for the Central Universities (Grant No. N100604008)
文摘A model for performance prediction of multistage centrifugal compressor is proposed. The model allows the users to predict the compressor performance, e.g. pressure ratio, efficiency and losses using the compressor geometric information and speed by a stage stacking calculation based on the characteristics of each stage. To develop the compressor elemental stage charac- teristics, the compressor losses, such as incidence losses and friction losses, are mathematically modeled. For a composite sys- tems, for instance a gas turbine power plant, the performance of the multistage centrifugal compressor can be evaluated. Since some important parameters of the compressor model, e.g., the slip factor or, shock loss coefficient （and reference diameter DI, are hard to be determined by empirical laws, a genetic algorithm （GA） is used to solve the parameter estimation problem of the proposed model, and in turn the compressor performance analysis and parameters study are performed. The surge line for the multistage centrifugal compressor can also be determined from the simulation results. Furthermore, the model presented here provides a valuable tool for evaluating the multistage centrifugal compressor performance as a function of various operation parameters.
基金supported in part by the Natural Science Foundation of Zhejiang Province(No.LQ21D060001)the Fengyun Application Pioneering Project(No.FY-APP-2021.0105)+8 种基金the Science and Technology Project of Zhejiang Meteorological Bureau(No.2021YB07)the Innovation and Development Project of China Meteorological Administration(No.CXFZ2022J040)the National Key R&D Program of China(No.2018YFC1506404)the Basic Public Welfare Research Program of Zhejiang Province(No.LGF18D050001)the Climate Change Special Program of China Meteorological Administration(No.CCSF202036)the Key Research and Development Program of Zhejiang Province(No.2021C02036)the Research Program from Science and the Technology Committee of Shanghai(No.19dz1200101)the Shanghai Typhoon Institute(No.2021JB05)the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography,MNR(No.QNHX2012).
文摘Gaofen-3(GF-3)is the first Chinese spaceborne multi-polarization synthetic aperture radar(SAR)instrument at C-band(5.43 GHz).In this paper,we use data collected from GF-3 to observe Super Typhoon Lekima(2019)in the East China Sea.Using a VH-polarized wide ScanSAR(WSC)image,ocean surface wind speeds at 100m horizontal resolution are obtained at 21:56:59 UTC on 8 August 2019,with the maximum wind speed,38.9 m·s^(-1).Validating the SAR-retrieved winds with buoymeasured wind speeds,we find that the root mean square error(RMSE)is 1.86 m·s^(-1),and correlation coefficient,0.92.This suggests that wind speeds retrieved from GF-3 SAR are reliable.Both the European Centre for MediumRange Weather Forecasts(ECMWF)fine grid operational forecast products with spatial resolution,and China Global/Regional Assimilation and Prediction Enhance System(GRAPES)have good performances on surface wind prediction under weak wind speed condition(<24 m·s^(-1)),but underestimate the maximum wind speed when the storm is intensified as a severe tropical storm(>24m·s^(-1)).With respect to SAR-retrieved wind speeds,the RMSEs are 5.24 m·s^(–1) for ECMWF and 5.17 m·s^(–1) for GRAPES,with biases of 4.16 m·s^(–1) for ECMWF and 3.84 m·s^(-1)for GRAPES during Super Typhoon Lekima(2019).
文摘Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies.Machine learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is limited.This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.Methods This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,China.The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September 2004.We collected and analyzed mortality data up to December 2018.Medical records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial infarction.Using the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline method.Results The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,and PXGBoost<0.05).Adding bPEP and bET to our feature set significantly improved the performance of the algorithm,leading to an absolute increase in C-statistic of up to 0.03(C-statistic,0.83 for random forest,0.82 for AdaBoost,and 0.80 for XGBoost,vs.0.74 for LR)(PRF<0.001,PAdaBoost<0.001,PXGBoost<0.05).Conclusion The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases.This advancement may enable better treatment prioritization for high-risk individuals.
