Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

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.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

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.

Hypotension prediction index together with cerebral oxygenation in guiding intraoperative hemodynamic management: a case report

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.

Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

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.

Effects of ovarian response prediction index and follicle-oocyte index on pregnancy outcomes:a retrospective cohort study of 12,218 fresh transfer cycles

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.

Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

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.

Numerical simulation-based correction of relative permeability hysteresis in water-invaded underground gas storage during multi-cycle injection and production

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.

Choroidal blood perfusion as a potential"rapid predictive index"for myopia development and progression

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.

Choroidal blood perfusion as a potential“rapid predictive index”for myopia development and progression

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.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

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.

Predictive value and impact analysis for the index of microcirculatory resistance in MI patients with elective percutaneous coronary intervention

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