Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients

BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)patients was limited.AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.METHODS A total of 286 patients from the Surveillance,Epidemiology,and End Results database were divided into the training set and validation set at a ratio of 8:2.92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set.Cox regression analysis was used to explore the relationship between LNR and disease-specific survival(DSS)of gastric NEN patients.Random survival forest(RSF)algorithm and Cox proportional hazards(CoxPH)analysis were applied to develop models to predict DSS respectively,and compared with the 8th edition American Joint Committee on Cancer(AJCC)tumornode-metastasis(TNM)staging.RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death.The RSF model exhibited the best performance in predicting DSS,with the C-index in the test set being 0.769[95%confidence interval(CI):0.691-0.846]outperforming the CoxPH model(0.744,95%CI:0.665-0.822)and the 8th edition AJCC TNM staging(0.723,95%CI:0.613-0.833).The calibration curves and decision curve analysis(DCA)demonstrated the RSF model had good calibration and clinical benefits.Furthermore,the RSF model could perform risk stratification and individual prognosis prediction effectively.CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients.The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set,showing potential in clinical practice.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography

BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Development of a new Cox model for predicting long-term survival in hepatitis cirrhosis patients underwent transjugular intrahepatic portosystemic shunts

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Path-Following Based on Nonlinear Model Predictive Control with Adaptive Path Preview

This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.

Enhancing Safety in Autonomous Vehicle Navigation:An Optimized Path Planning Approach Leveraging Model Predictive Control

This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Optimizing prediction models for pancreatic fistula after pancreatectomy:Current status and future perspectives

Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography

BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.

Development of a new irradiation-embrittlement prediction model for reactor pressure-vessel steels

Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction models may fail to properly describe the embrittlement trend curves of Chinese domestic RPV steels with relatively low Cu content.Based on the screened surveillance data of Chinese domestic and similar international RPV steels,we have developed a new fluencedependent model for predicting the irradiation-embrittlement trend.The fast neutron fluence(E>1 MeV)exhibited the highest correlation coefficient with the measured TTS data;thus,it is a crucial parameter in the prediction model.The chemical composition has little relevance to the TTS residual calculated by the fluence-dependent model.The results show that the newly developed model with a simple power-law functional form of the neutron fluence is suitable for predicting the irradiation-embrittlement trend of Chinese domestic RPVs,regardless of the effect of the chemical composition.