BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleedi...Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre...To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primar...The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.展开更多
Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study...Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.展开更多
BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR...BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.展开更多
BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),w...BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT h...Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft g...In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft gives rise to a dynamic and real-time game,complicated further by the presence of perturbation.The primary challenge lies in crafting control strategies that are both efficient and applicable to real-time game problems within a nonlinear system.To overcome this challenge,we introduce the model prediction and iterative correction technique proposed in model predictive static programming,enabling the generation of strategies in analytical iterative form for nonlinear systems.Subsequently,we proceed by integrating this model predictive framework into a simplified Stackelberg equilibrium formulation,tailored to address the practical complexities of leader–follower pursuit-evasion scenarios.Simulation results validate the effectiveness and exceptional efficiency of the proposed solution within a receding horizon framework.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.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
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
基金Construction and Application of Frailty Trajectory Prediction Model for Middle-aged and Elderly Patients with Upper Gastrointestinal Bleeding,Project Source:Sichuan Vocational College of Nursing(Project No.:2024ZRY25)。
文摘Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金Funded by State Railway Administration Research Project(No.2023JS007)National Natural Science Foundation of China(No.52438002)+1 种基金Research and Development Programs for Science and Technology of China Railways Corporation(No.J2023G003)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金supported by the National Natural Science Foundation of China(Nos.52278461,52308467,and 52425213).
文摘The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.
基金financially supported by the National Key Research and Development Program of China(No.2022YFB3706901)the National Natural Science Foundation of China(No.52090041)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC 001).
文摘Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.
文摘BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.
基金Supported by Shenzhen High-level Hospital Construction Fund.
文摘BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
基金supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-JKCS-23)the Special Research Fund for Central Universities,Peking Union Medical College[No.2022-I2M-C&T-A-014,CAMS Innovation Fund for Medical Sciences(CIFMS)]。
文摘Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金supported,in part,by the National Natural Science Foundation of China(Nos.12372050 and 62088101)the Zhejiang Provincial Natural Science Foundation of China(No.LR20F030003).
文摘In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft gives rise to a dynamic and real-time game,complicated further by the presence of perturbation.The primary challenge lies in crafting control strategies that are both efficient and applicable to real-time game problems within a nonlinear system.To overcome this challenge,we introduce the model prediction and iterative correction technique proposed in model predictive static programming,enabling the generation of strategies in analytical iterative form for nonlinear systems.Subsequently,we proceed by integrating this model predictive framework into a simplified Stackelberg equilibrium formulation,tailored to address the practical complexities of leader–follower pursuit-evasion scenarios.Simulation results validate the effectiveness and exceptional efficiency of the proposed solution within a receding horizon framework.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
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.
