Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to...Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to optimize the rail corrugation evolution trend prediction model of the least squares support vector machine (IPCA-ELWOA-LSSVM). The elite reverse learning combined with the Lévy flight strategy is introduced to improve the whale optimization algorithm. The improved WOA (ELWOA) algorithm is used to continuously optimize the kernel parameter σ and the normalization parameter γ in the LSSVM model. Finally, the improved prediction model is validated using data from a domestic heavy-duty railway experimental line database and compared with the prediction model before optimization and the other commonly used models. The experimental results show that the ELWOA-LSSVM prediction model has the highest accuracy, which proves that the proposed method has high accuracy in predicting the rail corrugation evolution trend.展开更多
This work addresses the critical challenge of ensuring reliable communication in vehicular ad hoc networks (VANETs) and drone networks (FANETs) under dynamic and high-mobility conditions. Current methods often fail to...This work addresses the critical challenge of ensuring reliable communication in vehicular ad hoc networks (VANETs) and drone networks (FANETs) under dynamic and high-mobility conditions. Current methods often fail to adequately predict rapid channel variations, leading to increased packet loss and degraded Quality of Service (QoS). To bridge this gap, we propose a novel cross-layer framework that integrates physical channel prediction into the Medium Access Control (MAC) layer to optimize network performance. Our framework employs an ARIMA (1, 0, 1) model for real-time channel prediction and dynamically adjusts MAC layer parameters to enhance throughput and reliability. Simulations demonstrate a 25% improvement in useful throughput and a 30% reduction in packet loss rates compared to baseline methods. These improvements enable practical applications in intelligent transportation systems and the efficient management of autonomous drones. Key contributions include: 1) Development of a cross-layer framework that integrates channel prediction and MAC optimization. 2) Demonstration of the framework’s effectiveness through Monte Carlo simulations in high-mobility scenarios. 3) Quantitative validation of enhanced throughput and reliability, highlighting the system’s potential for real-world deployment.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ2 tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
BACKGROUND Despite being the gold standard,the use of glycated hemoglobin(HbA1c)and fasting plasma glucose(FPG)for diagnosing dysglycemia is imperfect.In particular,a low level of agreement between HbA1c and FPG in de...BACKGROUND Despite being the gold standard,the use of glycated hemoglobin(HbA1c)and fasting plasma glucose(FPG)for diagnosing dysglycemia is imperfect.In particular,a low level of agreement between HbA1c and FPG in detecting prediabetes and diabetes has led to difficulties in clinical interpretation.Glycated albumin(GA)and 1,5-anhydroglucitol(1,5-AG)may potentially serve as biomarkers for the detection and prediction of diabetes,as well as glycemic monitoring.AIM To explore the diagnostic performance of GA and 1,5-AG for screening dysglycemia;assess whether they can be used for glycemic monitoring in Chinese morbidly-obese patients;and examine their predictive ability for incident diabetes in a Chinese community-based cohort.METHODS GA and 1,5-AG concentrations were measured in 462 morbidly-obese patients from the Obese Chinese Cohort(OCC).A sub-group of diabetes subjects(n=24)was prospectively followed-up after bariatric surgery.Differences between baseline and post-surgery biomarker values were converted to percentage change from baseline to assess the response to glycemic control.Predictive ability of the biomarkers was assessed in 132 incident diabetes cases and 132 matched non-diabetes controls in the community-based Cardiovascular Risk Factor Prevalence Study(CRISPS).A prediction model was developed and compared with clinical models based on conventional risk factors.RESULTS GA exhibited an excellent diagnostic value with an area under the receiver operating characteristic curve(AUC)of 0.919(95%CI:0.884-0.955)for identifying diabetes and a high agreement in the classification of diabetes with both FPG and HbA1c in the OCC.GA demonstrated the fastest response to glycemic control.In CRISPS,the‘B3A’prediction model,which consisted of body mass index(BMI)and 3 biomarkers(HbA1c,GA and 1,5-AG),achieved a comparable predictive value[AUC(95%CI):0.793(0.744-0.843)]to that of a clinical model comprising BMI,HbA1c,FPG and 2-hour glucose(2hG)[AUC(95%CI):0.783(0.733-0.834);DeLong P value=0.736].The‘B3A’was significantly superior to a clinical model including BMI,HbA1c,FPG and triglycerides[AUC(95%CI):0.729(0.673-0.784);DeLong P value=0.027].CONCLUSION GA and 1,5-AG have the potential to act as robust biomarkers for the screening and risk prediction of diabetes.FPG and 2hG may be replaced by GA and 1,5-AG in future diabetes predictions.展开更多
A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily av...A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.展开更多
BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperat...BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.展开更多
Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
BACKGROUND Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality.However,traditional scoring systems can be time consuming.We hypothesized that the use of machine lea...BACKGROUND Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality.However,traditional scoring systems can be time consuming.We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.AIM To assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.METHODS This retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023.Demographic and surgery-related data were collected and used to develop nomogram,decision-tree,random-forest,gradient-boosting,support vector machine,and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery.Models were assessed using receiver operating characteristic curves and compared using the DeLong test.RESULTS Of the 230 included patients,52 died and 178 survived.Models were developed using the training cohort(n=161)and assessed using the validation cohort(n=68).The areas under the receiver operating characteristic curves for the nomogram,decision-tree,random-forest,gradient-boosting tree,support vector machine,and naïve Bayesian models were 0.908[95%confidence interval(CI):0.824-0.992],0.874(95%CI:0.785-0.963),0.928(95%CI:0.869-0.987),0.907(95%CI:0.837-0.976),0.983(95%CI:0.959-1.000),and 0.807(95%CI:0.702-0.911),respectively.CONCLUSION Nomogram,random-forest,gradient-boosting tree,and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.展开更多
The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand bo...The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fi ne details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fi ne sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identifi cation,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.展开更多
BACKGROUND Heart rate variability(HRV)represents efferent vagus nerve activity,which is suggested to be related to fundamental mechanisms of tumorigenesis and to be a predictor of prognosis in various cancers.Therefor...BACKGROUND Heart rate variability(HRV)represents efferent vagus nerve activity,which is suggested to be related to fundamental mechanisms of tumorigenesis and to be a predictor of prognosis in various cancers.Therefore,this study hypothesized that HRV monitoring could predict perioperative complication(PC)in colorectal cancer(CRC)patients.AIM To investigate the prognostic value of HRV in hospitalized CRC patients.METHODS The observational studies included 87 patients who underwent CRC surgical procedures under enhanced recovery after surgery programs in a first-class hospital.The HRV parameters were compared between the PC group and the non PC(NPC)group from preoperative day 1 to postoperative day(Pod)3.In addition,inflammatory biomarkers and nutritional indicators were also analyzed.RESULTS The complication rate was 14.9%.HRV was markedly abnormal after surgery,especially in the PC group.The frequency-domain parameters(including pNN50)and time-domain parameters[including high-frequency(HF)]of HRV were significantly different between the two groups postoperatively.The pNN50 was significantly greater at Pod1 in the PC group than that in the NPC group and returned to baseline at Pod2,suggesting that patients with complications exhibited autonomic nerve dysfunction in the early postoperative period.In the PC group,HFs were also enhanced from Pod1 and were significantly higher than in the NPC group;inflammatory biomarkers were significantly elevated at Pod2 and Pod3;the levels of nutritional indicators were significantly lower at Pod1 and Pod2;and the white blood cell count was slightly elevated at Pod3.CONCLUSION HRV is independently associated with postoperative complications in patients with CRC.Abnormal HRV could predicted an increased risk of postoperative complications in CRC patients.Continuous HRV could be used to monitor complications in patients with CRC during the perioperative period.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the nume...Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.展开更多
Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The ma...Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.展开更多
The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and pr...The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.展开更多
The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To...The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.展开更多
The selection of loss models has a significant effect on the one-dimensional mean streamline analysis for obtaining the performance of centrifugal compressors.In this study,a set of optimized loss models is proposed b...The selection of loss models has a significant effect on the one-dimensional mean streamline analysis for obtaining the performance of centrifugal compressors.In this study,a set of optimized loss models is proposed based on the classical loss models suggested by Aungier,Coppage,and Jansen.The proportions and variation laws of losses predicted by the three sets of models are discussed on the NASA Low-Speed-Centrifugal-Compressor(LSCC)under the mass flow of 22 kg/s to 36 kg/s.The results indicate that the weights of Skin friction loss,Diffusion loss,Disk friction loss,Clearance loss,Blade loading loss,Recirculation loss,and Vaneless diffuser loss are greater than 10%,which is dominant for performance prediction.Therefore,these losses are considered in the composition of new loss models.In addition,the multi-objective optimization method with the Genetic Algorithm(GA)is applied to the correction of loss coefficients to obtain the final optimization loss models.Compared with the experimental data,the maximum relative error of adiabatic the three classical models is 7.22%,while the maximum relative error calculated by optimized loss models is 1.22%,which is reduced by 6%.Similarly,compared with the original model,the maximum relative error of the total pressure ratio is also reduced.As a result,the present optimized models provide more reliable performance prediction in both tendency and accuracy than the classical loss models.展开更多
This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative predict...This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma(HBV-HCC).The study’s use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC,potentially transforming patient outcomes through more personalized treatment strategies.This editorial commends the study's contribution to precision medicine and discusses its implic-ations for future research and clinical practice.展开更多
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
文摘Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to optimize the rail corrugation evolution trend prediction model of the least squares support vector machine (IPCA-ELWOA-LSSVM). The elite reverse learning combined with the Lévy flight strategy is introduced to improve the whale optimization algorithm. The improved WOA (ELWOA) algorithm is used to continuously optimize the kernel parameter σ and the normalization parameter γ in the LSSVM model. Finally, the improved prediction model is validated using data from a domestic heavy-duty railway experimental line database and compared with the prediction model before optimization and the other commonly used models. The experimental results show that the ELWOA-LSSVM prediction model has the highest accuracy, which proves that the proposed method has high accuracy in predicting the rail corrugation evolution trend.
文摘This work addresses the critical challenge of ensuring reliable communication in vehicular ad hoc networks (VANETs) and drone networks (FANETs) under dynamic and high-mobility conditions. Current methods often fail to adequately predict rapid channel variations, leading to increased packet loss and degraded Quality of Service (QoS). To bridge this gap, we propose a novel cross-layer framework that integrates physical channel prediction into the Medium Access Control (MAC) layer to optimize network performance. Our framework employs an ARIMA (1, 0, 1) model for real-time channel prediction and dynamically adjusts MAC layer parameters to enhance throughput and reliability. Simulations demonstrate a 25% improvement in useful throughput and a 30% reduction in packet loss rates compared to baseline methods. These improvements enable practical applications in intelligent transportation systems and the efficient management of autonomous drones. Key contributions include: 1) Development of a cross-layer framework that integrates channel prediction and MAC optimization. 2) Demonstration of the framework’s effectiveness through Monte Carlo simulations in high-mobility scenarios. 3) Quantitative validation of enhanced throughput and reliability, highlighting the system’s potential for real-world deployment.
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ2 tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
基金Supported by the Hong Kong Research Grants Council Area of Excellence,No.AoE/M/707-18.
文摘BACKGROUND Despite being the gold standard,the use of glycated hemoglobin(HbA1c)and fasting plasma glucose(FPG)for diagnosing dysglycemia is imperfect.In particular,a low level of agreement between HbA1c and FPG in detecting prediabetes and diabetes has led to difficulties in clinical interpretation.Glycated albumin(GA)and 1,5-anhydroglucitol(1,5-AG)may potentially serve as biomarkers for the detection and prediction of diabetes,as well as glycemic monitoring.AIM To explore the diagnostic performance of GA and 1,5-AG for screening dysglycemia;assess whether they can be used for glycemic monitoring in Chinese morbidly-obese patients;and examine their predictive ability for incident diabetes in a Chinese community-based cohort.METHODS GA and 1,5-AG concentrations were measured in 462 morbidly-obese patients from the Obese Chinese Cohort(OCC).A sub-group of diabetes subjects(n=24)was prospectively followed-up after bariatric surgery.Differences between baseline and post-surgery biomarker values were converted to percentage change from baseline to assess the response to glycemic control.Predictive ability of the biomarkers was assessed in 132 incident diabetes cases and 132 matched non-diabetes controls in the community-based Cardiovascular Risk Factor Prevalence Study(CRISPS).A prediction model was developed and compared with clinical models based on conventional risk factors.RESULTS GA exhibited an excellent diagnostic value with an area under the receiver operating characteristic curve(AUC)of 0.919(95%CI:0.884-0.955)for identifying diabetes and a high agreement in the classification of diabetes with both FPG and HbA1c in the OCC.GA demonstrated the fastest response to glycemic control.In CRISPS,the‘B3A’prediction model,which consisted of body mass index(BMI)and 3 biomarkers(HbA1c,GA and 1,5-AG),achieved a comparable predictive value[AUC(95%CI):0.793(0.744-0.843)]to that of a clinical model comprising BMI,HbA1c,FPG and 2-hour glucose(2hG)[AUC(95%CI):0.783(0.733-0.834);DeLong P value=0.736].The‘B3A’was significantly superior to a clinical model including BMI,HbA1c,FPG and triglycerides[AUC(95%CI):0.729(0.673-0.784);DeLong P value=0.027].CONCLUSION GA and 1,5-AG have the potential to act as robust biomarkers for the screening and risk prediction of diabetes.FPG and 2hG may be replaced by GA and 1,5-AG in future diabetes predictions.
文摘A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.
基金Supported by National Key Research and Development Program,No.2022YFC2407304Major Research Project for Middle-Aged and Young Scientists of Fujian Provincial Health Commission,No.2021ZQNZD013+2 种基金The National Natural Science Foundation of China,No.62275050Fujian Province Science and Technology Innovation Joint Fund Project,No.2019Y9108Major Science and Technology Projects of Fujian Province,No.2021YZ036017.
文摘BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.
文摘Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.
基金Supported by the Shanghai Municipal Health Commission Project,No.20214Y0284.
文摘BACKGROUND Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality.However,traditional scoring systems can be time consuming.We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.AIM To assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.METHODS This retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023.Demographic and surgery-related data were collected and used to develop nomogram,decision-tree,random-forest,gradient-boosting,support vector machine,and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery.Models were assessed using receiver operating characteristic curves and compared using the DeLong test.RESULTS Of the 230 included patients,52 died and 178 survived.Models were developed using the training cohort(n=161)and assessed using the validation cohort(n=68).The areas under the receiver operating characteristic curves for the nomogram,decision-tree,random-forest,gradient-boosting tree,support vector machine,and naïve Bayesian models were 0.908[95%confidence interval(CI):0.824-0.992],0.874(95%CI:0.785-0.963),0.928(95%CI:0.869-0.987),0.907(95%CI:0.837-0.976),0.983(95%CI:0.959-1.000),and 0.807(95%CI:0.702-0.911),respectively.CONCLUSION Nomogram,random-forest,gradient-boosting tree,and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.
基金Collaborative Project Grant from the Exploration and Development Research Institute of SINOPEC Northwest Oilfi eld Company(Grant No.KY2021-S-104).
文摘The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fi ne details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fi ne sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identifi cation,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.
基金Supported by The Natural Science Foundation of Nanjing University of Chinese Medicine,No.XZR2021019The Outstanding Young Doctor Program of Jiangsu Province of Chinese Medicine,No.2023QB0140+1 种基金Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province,No.JD2022SZ18The Natural Science Foundation of Nanjing University of Chinese Medicine,No.KYCX21_1710.
文摘BACKGROUND Heart rate variability(HRV)represents efferent vagus nerve activity,which is suggested to be related to fundamental mechanisms of tumorigenesis and to be a predictor of prognosis in various cancers.Therefore,this study hypothesized that HRV monitoring could predict perioperative complication(PC)in colorectal cancer(CRC)patients.AIM To investigate the prognostic value of HRV in hospitalized CRC patients.METHODS The observational studies included 87 patients who underwent CRC surgical procedures under enhanced recovery after surgery programs in a first-class hospital.The HRV parameters were compared between the PC group and the non PC(NPC)group from preoperative day 1 to postoperative day(Pod)3.In addition,inflammatory biomarkers and nutritional indicators were also analyzed.RESULTS The complication rate was 14.9%.HRV was markedly abnormal after surgery,especially in the PC group.The frequency-domain parameters(including pNN50)and time-domain parameters[including high-frequency(HF)]of HRV were significantly different between the two groups postoperatively.The pNN50 was significantly greater at Pod1 in the PC group than that in the NPC group and returned to baseline at Pod2,suggesting that patients with complications exhibited autonomic nerve dysfunction in the early postoperative period.In the PC group,HFs were also enhanced from Pod1 and were significantly higher than in the NPC group;inflammatory biomarkers were significantly elevated at Pod2 and Pod3;the levels of nutritional indicators were significantly lower at Pod1 and Pod2;and the white blood cell count was slightly elevated at Pod3.CONCLUSION HRV is independently associated with postoperative complications in patients with CRC.Abnormal HRV could predicted an increased risk of postoperative complications in CRC patients.Continuous HRV could be used to monitor complications in patients with CRC during the perioperative period.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42122034,42075043,42330609)the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0103)+2 种基金Key Talent Project in Gansu and Central Guidance Fund for Local Science and Technology Development Projects in Gansu(No.24ZYQA031)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021427)West Light Foundation of the Chinese Academy of Sciences(xbzg-zdsys-202215)。
文摘Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.
文摘Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.
文摘The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.
基金supported by the China Railway Corporation Science and Technology Research and Development Program(Grant Nos.K2020G035 and K2021G024)the National Natural Science Foundation of China(Grant No.52378411).
文摘The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.
基金supports of National Natural Science Foundation of China(Grant No.52076079)Natural Science Foundation of Hebei Province,China(Grant No.E2020502013)Fundamental Research Funds for the Central Universities,China(Grant No.2021MS079/Grant No.2022MS081)。
文摘The selection of loss models has a significant effect on the one-dimensional mean streamline analysis for obtaining the performance of centrifugal compressors.In this study,a set of optimized loss models is proposed based on the classical loss models suggested by Aungier,Coppage,and Jansen.The proportions and variation laws of losses predicted by the three sets of models are discussed on the NASA Low-Speed-Centrifugal-Compressor(LSCC)under the mass flow of 22 kg/s to 36 kg/s.The results indicate that the weights of Skin friction loss,Diffusion loss,Disk friction loss,Clearance loss,Blade loading loss,Recirculation loss,and Vaneless diffuser loss are greater than 10%,which is dominant for performance prediction.Therefore,these losses are considered in the composition of new loss models.In addition,the multi-objective optimization method with the Genetic Algorithm(GA)is applied to the correction of loss coefficients to obtain the final optimization loss models.Compared with the experimental data,the maximum relative error of adiabatic the three classical models is 7.22%,while the maximum relative error calculated by optimized loss models is 1.22%,which is reduced by 6%.Similarly,compared with the original model,the maximum relative error of the total pressure ratio is also reduced.As a result,the present optimized models provide more reliable performance prediction in both tendency and accuracy than the classical loss models.
基金Supported by Shandong Province Medical and Health Science and Technology Development Plan Project,No.202203030713Science and Technology Program of Yantai Affiliated Hospital of Binzhou Medical University,No.YTFY2022KYQD06。
文摘This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma(HBV-HCC).The study’s use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC,potentially transforming patient outcomes through more personalized treatment strategies.This editorial commends the study's contribution to precision medicine and discusses its implic-ations for future research and clinical practice.
