Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(...Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(RI)and Abbe number of polymers.Using the LorentzLorenz equation,the frequency-dependent polarizability and molecular volume were calculated to estimate RI.Wavelength-dependent RI values were used to derive the Abbe numbers.Our results show a strong correlation with experimental data,with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number,enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results.By categorizing polymers into classes such as poly(methyl methacrylate)(PMMA)-,polyethylene(PE)-,polycarbonate(PC)-,polyimide(PI)-,and polyurethane(PU)-based materials,this method enables precise predictions and reduces discrepancies using linear correction functions.This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials.展开更多
Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the ...Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a“rapid predictive index”of myopia.展开更多
Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article revi...Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a"rapid predictive index"of myopia.展开更多
The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment ...The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.展开更多
Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate co...Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.展开更多
Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index(HPI) was developed to predict intraoper...Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index(HPI) was developed to predict intraoperative hypotension(mean arterial pressure <65 mmHg) in real time. However, pressure autoregulation also plays an important role in maintaining adequate organ perfusion/oxygenation during hypotension. A cerebral oxygenation monitor provides clinicians with the values of organ oxygenation. We reported a case that the cerebral oxygenation monitor was used together with HPI to guide intraoperative blood pressure management. We found that cerebral oxygenation was maintained in the event of hypotension during surgery. The patient had no intraoperative or postoperative adverse outcomes despite the hypotension. We believe this can provide an individualized intraoperative blood pressure management to avoid over-or under-treating hypotension.展开更多
Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. ...Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.展开更多
To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle ...To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.展开更多
Objective:To investigate the potential relationships among the ovarian response prediction index(ORPI),follicle-oocyte index(FOI),and clinical pregnancy rate(CPR)in women undergoing their first in vitro fertilization/...Objective:To investigate the potential relationships among the ovarian response prediction index(ORPI),follicle-oocyte index(FOI),and clinical pregnancy rate(CPR)in women undergoing their first in vitro fertilization/intracytoplasmic sperm injectionembryo transfer(IVF/ICSI-ET)fresh cycle transfer.Methods:In this retrospective cohort study,we included 12,218 women who underwent their first IVF/ICSI-ET cycle between December 2014 and January 2021.The primary and secondary outcomes of our study were CPR and cumulative live birth rate(CLBR),respectively.The data were divided into three groups according to the ORPI and FOI tertiles.Multivariate logistic regression analyses,stratification analyses,interaction,restricted cubic splines,and receiver operating characteristic(ROC)curves were constructed to identify the relationships among ORPI,FOI,and CPR.Results:A statistically significant increase in CPR was detected from the lowest to the highest tertile group(ORPI:48.12%,54.07%,and 53.47%,P<0.001;FOI:49.99%,52.95%,and 52.71%,P=0.012).A higher CLBR was observed in the high group(ORPI:38.63%,44.62%,and 44.19%,P<0.001;FOI:41.02%,43.78%,and 42.59%,P=0.039).Multivariate logistic regression analysis revealed no statistically significant differences between ORPI,FOI,and neither CPR(odds ratio[OR][95%confidence interval{CI}],0.99[0.97–1.00]vs.[1.02{0.84–1.24}])nor CLBR(OR[95%CI],0.99[0.97–1.01]vs.0.99[0.81–1.20]).No significant association was found among FOI,ORPI,and CPR,even in the subgroups.Restricted cubic spline analyses indicated the existence of a non-linear relationship across the entire range of FOI and ORPI.The ORPI and FOI variables had poor predictive ability(AUC<0.60)for CPR.Conclusions:Both ORPI and FOI are not reliable predictors of clinical pregnancy or live birth outcomes in fresh ETs.Clinicians and researchers should avoid using FOI and ORPI to assess pregnancy outcomes after fresh ET because of their limited relevance and predictive value.展开更多
Objective To evaluate the predictive value and impact for the index of microcirculatory resistance(IMR)in myocardial infarction(MI)patients with elective percutaneous coronary intervention(PCI)for treating coronary ar...Objective To evaluate the predictive value and impact for the index of microcirculatory resistance(IMR)in myocardial infarction(MI)patients with elective percutaneous coronary intervention(PCI)for treating coronary artery occlusion.Methods A total of 34 patients with STEMI or non-STEMI treated after 12h time window展开更多
High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the ap...High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.展开更多
By conducting relative permeability experiments of multi-cycle gas-water displacement and imbibition on natural cores,we discuss relative permeability hysteresis effect in underground gas storage during multi-cycle in...By conducting relative permeability experiments of multi-cycle gas-water displacement and imbibition on natural cores,we discuss relative permeability hysteresis effect in underground gas storage during multi-cycle injection and production.A correction method for relative permeability hysteresis in numerical simulation of water-invaded gas storage has been worked out using the Carlson and Killough models.A geologic model of water-invaded sandstone gas storage with medium-low permeability is built to investigate the impacts of relative permeability hysteresis on fluid distribution and production performance during multi-cycle injection and production of the gas storage.The study shows that relative permeability hysteresis effect occurs during high-speed injection and production in gas storage converted from water-invaded gas reservoir,and leads to increase of gas-water transition zone width and thickness,shrinkage of the area of high-efficiency gas storage,and decrease of the peak value variation of pore volume containing gas,and then reduces the storage capacity,working gas volume,and high-efficiency operation span of the gas storage.Numerical simulations exhibit large prediction errors of performance indexes if this hysteresis effect is not considered.Killough and Carlson methods can be used to correct the relative permeability hysteresis effect in water-invaded underground gas storage to improve the prediction accuracy.The Killough method has better adaptability to the example model.展开更多
After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon ...After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.展开更多
基金financially supported by the Shenzhen Science and Technology Project(Nos.JCYJ20210324095210028,JSGGZD20220822095201003)the National Natural Science Foundation of China(U21A2087)。
文摘Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(RI)and Abbe number of polymers.Using the LorentzLorenz equation,the frequency-dependent polarizability and molecular volume were calculated to estimate RI.Wavelength-dependent RI values were used to derive the Abbe numbers.Our results show a strong correlation with experimental data,with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number,enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results.By categorizing polymers into classes such as poly(methyl methacrylate)(PMMA)-,polyethylene(PE)-,polycarbonate(PC)-,polyimide(PI)-,and polyurethane(PU)-based materials,this method enables precise predictions and reduces discrepancies using linear correction functions.This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials.
文摘Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a“rapid predictive index”of myopia.
文摘Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a"rapid predictive index"of myopia.
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions,grant number 2023QN082,awarded to Cheng ZhaoThe National Natural Science Foundation of China also provided funding,grant number 61902349,awarded to Cheng Zhao.
文摘The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.
文摘Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.
基金Department of Anesthesiology and Pain Medicine, University of California Davis Health, and NIH grant No. of UL1 TR001860 of University of California Davis Health。
文摘Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index(HPI) was developed to predict intraoperative hypotension(mean arterial pressure <65 mmHg) in real time. However, pressure autoregulation also plays an important role in maintaining adequate organ perfusion/oxygenation during hypotension. A cerebral oxygenation monitor provides clinicians with the values of organ oxygenation. We reported a case that the cerebral oxygenation monitor was used together with HPI to guide intraoperative blood pressure management. We found that cerebral oxygenation was maintained in the event of hypotension during surgery. The patient had no intraoperative or postoperative adverse outcomes despite the hypotension. We believe this can provide an individualized intraoperative blood pressure management to avoid over-or under-treating hypotension.
基金Financial support for this work, provided by the National Basic Research Program of China (No.2011CB201204)the National Youth Science Foundation Program (No.50904068)+1 种基金the Heilongjiang Science & Technology Scientific Research Foundation Program for the Eighth Introduction of Talent (No.06-26)the National Engineering Research Center for Coal Gas Control
文摘Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.
基金supported by the by the National Natural Science Foundation(No.60874069,60634020)the National High Technology Research and Development Programme of China(No.2009AA04Z124)Hunan Provincial Natural Science Foundation of China(No.09JJ3122)
文摘To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.
文摘Objective:To investigate the potential relationships among the ovarian response prediction index(ORPI),follicle-oocyte index(FOI),and clinical pregnancy rate(CPR)in women undergoing their first in vitro fertilization/intracytoplasmic sperm injectionembryo transfer(IVF/ICSI-ET)fresh cycle transfer.Methods:In this retrospective cohort study,we included 12,218 women who underwent their first IVF/ICSI-ET cycle between December 2014 and January 2021.The primary and secondary outcomes of our study were CPR and cumulative live birth rate(CLBR),respectively.The data were divided into three groups according to the ORPI and FOI tertiles.Multivariate logistic regression analyses,stratification analyses,interaction,restricted cubic splines,and receiver operating characteristic(ROC)curves were constructed to identify the relationships among ORPI,FOI,and CPR.Results:A statistically significant increase in CPR was detected from the lowest to the highest tertile group(ORPI:48.12%,54.07%,and 53.47%,P<0.001;FOI:49.99%,52.95%,and 52.71%,P=0.012).A higher CLBR was observed in the high group(ORPI:38.63%,44.62%,and 44.19%,P<0.001;FOI:41.02%,43.78%,and 42.59%,P=0.039).Multivariate logistic regression analysis revealed no statistically significant differences between ORPI,FOI,and neither CPR(odds ratio[OR][95%confidence interval{CI}],0.99[0.97–1.00]vs.[1.02{0.84–1.24}])nor CLBR(OR[95%CI],0.99[0.97–1.01]vs.0.99[0.81–1.20]).No significant association was found among FOI,ORPI,and CPR,even in the subgroups.Restricted cubic spline analyses indicated the existence of a non-linear relationship across the entire range of FOI and ORPI.The ORPI and FOI variables had poor predictive ability(AUC<0.60)for CPR.Conclusions:Both ORPI and FOI are not reliable predictors of clinical pregnancy or live birth outcomes in fresh ETs.Clinicians and researchers should avoid using FOI and ORPI to assess pregnancy outcomes after fresh ET because of their limited relevance and predictive value.
文摘Objective To evaluate the predictive value and impact for the index of microcirculatory resistance(IMR)in myocardial infarction(MI)patients with elective percutaneous coronary intervention(PCI)for treating coronary artery occlusion.Methods A total of 34 patients with STEMI or non-STEMI treated after 12h time window
基金Supported by the Philosophy and Social Science Fund of Higher Institutions of Jiangsu Province(2017SJB0234)Natural Science Foundation of Higher Education Institutions of Jiangsu Province(17KJB120004)+2 种基金MOE Layout Foundation of Humanities and Social Sciences(17YJA790101)the National Natural Science Foundation of China(71471081,71501088,71671082)MOE Project of Humanities and Social Sciences(17YJC630128)
文摘High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.
基金Supported by the Petro China Science and Technology Major Project(2015E-4002)。
文摘By conducting relative permeability experiments of multi-cycle gas-water displacement and imbibition on natural cores,we discuss relative permeability hysteresis effect in underground gas storage during multi-cycle injection and production.A correction method for relative permeability hysteresis in numerical simulation of water-invaded gas storage has been worked out using the Carlson and Killough models.A geologic model of water-invaded sandstone gas storage with medium-low permeability is built to investigate the impacts of relative permeability hysteresis on fluid distribution and production performance during multi-cycle injection and production of the gas storage.The study shows that relative permeability hysteresis effect occurs during high-speed injection and production in gas storage converted from water-invaded gas reservoir,and leads to increase of gas-water transition zone width and thickness,shrinkage of the area of high-efficiency gas storage,and decrease of the peak value variation of pore volume containing gas,and then reduces the storage capacity,working gas volume,and high-efficiency operation span of the gas storage.Numerical simulations exhibit large prediction errors of performance indexes if this hysteresis effect is not considered.Killough and Carlson methods can be used to correct the relative permeability hysteresis effect in water-invaded underground gas storage to improve the prediction accuracy.The Killough method has better adaptability to the example model.
基金the New Technology Extension Project of China Meteorological Administration under Grant No.GMATG2008M49the National Natural Science Foundation of China under Grant No.40675023
文摘After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.
