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
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
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
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
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
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 study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals noth...This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.展开更多
Purpose The purpose of this study is to explore deep learning methods for processing high-throughput small-angle X-ray scattering(SAXS)experimental data.Methods The deep learning algorithm was trained and validated us...Purpose The purpose of this study is to explore deep learning methods for processing high-throughput small-angle X-ray scattering(SAXS)experimental data.Methods The deep learning algorithm was trained and validated using simulated SAXS data,which were generated in batches based on the theoretical SAXS formula using Python code.Our self-developed SAXSNET,a convolutional neural network based on PyTorch,was employed to classify SAXS data for various shapes of nanoparticles.Additionally,we conducted comparative analysis of classification algorithms including ResNet-18,ResNet-34 and Vision Transformer.Random Forest and XGboost regression algorithms were used for the nanoparticle size prediction.Finally,we evaluated the aforementioned shape classification and numerical regression methods using actual experimental data.A pipeline segment is established for the processing of SAXS data,incorporating deep learning classification algorithms and numerical regression algorithms.Results After being trained with simulated data,the four deep learning algorithms achieved a prediction accuracy of over 96%on the validation set.The fine-tuned deep learning model demonstrated robust generalization capabilities for predicting the shapes of experimental data,enabling rapid and accurate identification of morphological changes in nanoparticles during experiments.The Random Forest and XGboost regression algorithms can simultaneously provide faster and more accurate predictions of nanoparticle size.Conclusion The pipeline segment constructed in this study,integrating deep learning classification and regression algorithms,enables real-time processing of high-throughput SAXS data.It aims to effectively mitigates the impact of human factors on data processing results and enhances the standardization,automation,and intelligence of synchrotron radiation experiments.展开更多
This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a t...This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.展开更多
"非 X 不可"格式产生于先秦时期,发展至今经历了结构语法化和功能语法化。其结构语法化包括格式的凝固化、有界化和 X 的谓词化,其功能语法化起始于清代末年,而广泛发生于20世纪上半叶,"非……不可"由命题谓语语法..."非 X 不可"格式产生于先秦时期,发展至今经历了结构语法化和功能语法化。其结构语法化包括格式的凝固化、有界化和 X 的谓词化,其功能语法化起始于清代末年,而广泛发生于20世纪上半叶,"非……不可"由命题谓语语法化为表示情态意义的高位谓语,并由此导致了情态副词"非"的产生。展开更多
The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive st...The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.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
English and Chinese belong to different language families,employing two distinct syntactic systems.English is subject-prominent,following the pattern of subject first,then predicate;while Chinese is topic-prominent,sh...English and Chinese belong to different language families,employing two distinct syntactic systems.English is subject-prominent,following the pattern of subject first,then predicate;while Chinese is topic-prominent,showing much flexibility in word arrangement as well as the necessity of subject and predicate.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 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
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
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
In this paper we describe our research work in GSM half-rate coding system for the pan-Europeandigital mobile cellular radio system. The system consists of a speech coder and a channel coder. An overview of thespeech ...In this paper we describe our research work in GSM half-rate coding system for the pan-Europeandigital mobile cellular radio system. The system consists of a speech coder and a channel coder. An overview of thespeech coding algorithm is given. The channel coder uses CRC check and a convolutional code. Interleaving is usedto randomize the channel bursts. The proposed half-rate codec is implemented with a single TMS320C30. Theinformal tests (based on MOS score) prove that our speech coder is of higher quality.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.
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.
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.
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.
基金National Key Research and Development Program of China(2021YFB3101402)National Natural Science Foundation of China(62202294)。
文摘This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.
基金supported by the Innovation Program of the Institute of High Energy Physics,CAS(Grant Number 2023000034)the National Natural Science Foundation of China(Grant Numbers 22273013,12275300)National Key R&D Program of China(Grant Numbers 2022YFA1603802,2017YFA0403000).
文摘Purpose The purpose of this study is to explore deep learning methods for processing high-throughput small-angle X-ray scattering(SAXS)experimental data.Methods The deep learning algorithm was trained and validated using simulated SAXS data,which were generated in batches based on the theoretical SAXS formula using Python code.Our self-developed SAXSNET,a convolutional neural network based on PyTorch,was employed to classify SAXS data for various shapes of nanoparticles.Additionally,we conducted comparative analysis of classification algorithms including ResNet-18,ResNet-34 and Vision Transformer.Random Forest and XGboost regression algorithms were used for the nanoparticle size prediction.Finally,we evaluated the aforementioned shape classification and numerical regression methods using actual experimental data.A pipeline segment is established for the processing of SAXS data,incorporating deep learning classification algorithms and numerical regression algorithms.Results After being trained with simulated data,the four deep learning algorithms achieved a prediction accuracy of over 96%on the validation set.The fine-tuned deep learning model demonstrated robust generalization capabilities for predicting the shapes of experimental data,enabling rapid and accurate identification of morphological changes in nanoparticles during experiments.The Random Forest and XGboost regression algorithms can simultaneously provide faster and more accurate predictions of nanoparticle size.Conclusion The pipeline segment constructed in this study,integrating deep learning classification and regression algorithms,enables real-time processing of high-throughput SAXS data.It aims to effectively mitigates the impact of human factors on data processing results and enhances the standardization,automation,and intelligence of synchrotron radiation experiments.
基金supported by the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(No.U19A20106)the Science and Technology Major Projects of Anhui Province(No.202203f07020003)the Science and Technology Project of State Grid Corporation of China(No.52120522000F).
文摘This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.
基金The National Basic Research Program of China (973Program)(No2000CB610703)
文摘The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.
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.
文摘English and Chinese belong to different language families,employing two distinct syntactic systems.English is subject-prominent,following the pattern of subject first,then predicate;while Chinese is topic-prominent,showing much flexibility in word arrangement as well as the necessity of subject and predicate.
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.
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.
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.
文摘In this paper we describe our research work in GSM half-rate coding system for the pan-Europeandigital mobile cellular radio system. The system consists of a speech coder and a channel coder. An overview of thespeech coding algorithm is given. The channel coder uses CRC check and a convolutional code. Interleaving is usedto randomize the channel bursts. The proposed half-rate codec is implemented with a single TMS320C30. Theinformal tests (based on MOS score) prove that our speech coder is of higher quality.
