Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before...Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before surgery to 1 day after surgery),and to provide a basis for optimizing clinical nursing plans for elderly cataract surgery.Methods:A retrospective selection of 90 elderly patients(aged≥60 years)who underwent cataract surgery in the Ophthalmology Department of our hospital from August 2024 to December 2024 was conducted.They were divided into an observation group(n=45)and a control group(n=45)using a random number table method.The control group received routine nursing for cataract surgery,while the observation group implemented evidence-based predictive nursing intervention(including the establishment of a multidisciplinary evidence-based team,hierarchical psychological intervention,perioperative environment optimization,intraoperative personalized cooperation,and video-based health education).Psychological stress indicators[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS),General Self-Efficacy Scale(GSES)]on the 1st day before surgery and 1st day after surgery,and fluctuations of physiological indicators[Heart Rate(HR),Systolic Blood Pressure(SBP),Diastolic Blood Pressure(DBP)]on the 1st day before surgery and during surgery were compared between the two groups.Results:Before intervention,there were no statistically significant differences in SAS,SDS,GSES scores,HR,SBP,or DBP between the two groups(p>0.05);after intervention,the SAS score(33.62±5.72)and SDS score(32.14±4.86)of the observation group on the 1st day after surgery were significantly lower than those of the control group[(41.05±5.56),(43.59±4.75)],and the GSES score(31.15±3.28)was significantly higher than that of the control group(24.84±3.52)(all p<0.05);during surgery,the fluctuations of HR(74.0±6.0)beats/min,SBP(127.0±15.8)mmHg,and DBP(75.0±5.9)mmHg in the observation group were significantly smaller than those in the control group(all p<0.05).Conclusion:Evidence-based predictive nursing intervention can effectively alleviate anxiety and depression in elderly cataract patients during the perioperative period,improve self-efficacy,stabilize intraoperative physiological status,and enhance surgical cooperation,which is worthy of clinical promotion.展开更多
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
A case of imported severe falciparum malaria with spontaneous splenic rupture was reported in this paper.The patient,an African migrant worker,developed hemolytic anemia,sepsis,thrombocytopenia,coagulation dysfunction...A case of imported severe falciparum malaria with spontaneous splenic rupture was reported in this paper.The patient,an African migrant worker,developed hemolytic anemia,sepsis,thrombocytopenia,coagulation dysfunction,liver failure,renal insufficiency,electrolyte disturbance and other clinical manifestations after returning to the local area.Plasmodium falciparum was found by peripheral blood smearscopy and was diagnosed as severe falciparum malaria.After standardized anti-malaria treatment,plasma exchange+cytokine adsorption therapy,the establishment of“forewarning-forewarning-prevention-emergency”predictive nursing management model,the establishment of an integrated nursing team,the division of medical care is clear,professional knowledge is complementary,after three months of regular follow-up,the patient has no malaria recurrence,no refire,the function of all organs returned to normal.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect ...While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.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
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
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,t...This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gast...BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.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
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
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
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.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
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
Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorit...Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.展开更多
基金Hospital Quality Management Research Fund Project of China Medical Quality Management Association(Project No.:YLZG202511)。
文摘Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before surgery to 1 day after surgery),and to provide a basis for optimizing clinical nursing plans for elderly cataract surgery.Methods:A retrospective selection of 90 elderly patients(aged≥60 years)who underwent cataract surgery in the Ophthalmology Department of our hospital from August 2024 to December 2024 was conducted.They were divided into an observation group(n=45)and a control group(n=45)using a random number table method.The control group received routine nursing for cataract surgery,while the observation group implemented evidence-based predictive nursing intervention(including the establishment of a multidisciplinary evidence-based team,hierarchical psychological intervention,perioperative environment optimization,intraoperative personalized cooperation,and video-based health education).Psychological stress indicators[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS),General Self-Efficacy Scale(GSES)]on the 1st day before surgery and 1st day after surgery,and fluctuations of physiological indicators[Heart Rate(HR),Systolic Blood Pressure(SBP),Diastolic Blood Pressure(DBP)]on the 1st day before surgery and during surgery were compared between the two groups.Results:Before intervention,there were no statistically significant differences in SAS,SDS,GSES scores,HR,SBP,or DBP between the two groups(p>0.05);after intervention,the SAS score(33.62±5.72)and SDS score(32.14±4.86)of the observation group on the 1st day after surgery were significantly lower than those of the control group[(41.05±5.56),(43.59±4.75)],and the GSES score(31.15±3.28)was significantly higher than that of the control group(24.84±3.52)(all p<0.05);during surgery,the fluctuations of HR(74.0±6.0)beats/min,SBP(127.0±15.8)mmHg,and DBP(75.0±5.9)mmHg in the observation group were significantly smaller than those in the control group(all p<0.05).Conclusion:Evidence-based predictive nursing intervention can effectively alleviate anxiety and depression in elderly cataract patients during the perioperative period,improve self-efficacy,stabilize intraoperative physiological status,and enhance surgical cooperation,which is worthy of clinical promotion.
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“Artificial Liver Special Fund”of Beijing Gan Dan Xiang Zhao Public Welfare Foundation(Project No.:iGandanF-1082024-RGG055)。
文摘A case of imported severe falciparum malaria with spontaneous splenic rupture was reported in this paper.The patient,an African migrant worker,developed hemolytic anemia,sepsis,thrombocytopenia,coagulation dysfunction,liver failure,renal insufficiency,electrolyte disturbance and other clinical manifestations after returning to the local area.Plasmodium falciparum was found by peripheral blood smearscopy and was diagnosed as severe falciparum malaria.After standardized anti-malaria treatment,plasma exchange+cytokine adsorption therapy,the establishment of“forewarning-forewarning-prevention-emergency”predictive nursing management model,the establishment of an integrated nursing team,the division of medical care is clear,professional knowledge is complementary,after three months of regular follow-up,the patient has no malaria recurrence,no refire,the function of all organs returned to normal.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
文摘While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.
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 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 in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
文摘This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
文摘BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.
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 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 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co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
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.
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Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.
