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.

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.

Application of the value of nonlinear parameters H and ΔH in strong earthquake prediction

In this paper, the relation that the curves of nonlinear parameter H and its difference Δ H bear with strong earthquakes in North China has been studied. First, the RSH algorithm has been applied to the North China region; the schemes of six quantitative prediction indexes have been studied in detail and then tested by tracing back predictions. The result shows that all the six prediction schemes are of certain prediction efficiency and have passed the test. Among the six schemes, A and E are of the best effect, with correlation coefficients R of 0.47 and 0.48 respectively. We recommend these two schemes for practical use in prediction in the future. Furthermore, the relation between the curve of Δ H (the difference of H) and strong earthquake has been studied. Based on the above results, the RSΔH algorithm that uses the Δ H value to predict strong earthquake has been put forward and applied to predict strong earthquakes in North China. The correlation coefficient R of tracing back prediction by this method is 0.45; this means that this method is also of better prediction efficiency. A combined application of these two algorithms has also been proposed. By the combined method, the time length spanned by false predictions can be shortened and thus the R value can be raised.

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.

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.

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.

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.

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