Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

Construction of A Prediction Model for Atrial Fibrillation in Patients with Dilated Cardiomyopathy and Heart Failure

Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

Development and validation of a predictive model for acute-onchronic liver failure after transjugular intrahepatic portosystemic shunt

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a cause of acute-onchronic liver failure(ACLF).AIM To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model.METHODS In total,379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort,and 123 patients from Nanfang Hospital were included in the external validation cohort.Univariate and multivariate logistic regression analyses were performed to identify independent predictors.The prediction model was established based on the Akaike information criterion.Internal and external validation were conducted to assess the performance of the model.RESULTS Age and total bilirubin(TBil)were independent risk factors for the incidence of ACLF within 1 year after TIPS.We developed a prediction model comprising age,TBil,and serum sodium,which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort.CONCLUSION Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis.Our model showed satisfying predictive value.

Drill bit wear monitoring and failure prediction for mining automation

This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes.Virtual Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and availability.On the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the system.In VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations.In this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud environment.The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction.To accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM failures.At the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy consumption.The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model.The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving environments.The pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow applications.Since the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered performance.Though a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected drastically.Hence rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workflows.This work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efficient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications.The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows.The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

The inquiry process of traditional medical equipment maintenance management is complex,which has a negative impact on the efficiency and accuracy of medical equipment maintenance management and results in a significant amount of wasted time and resources.To properly predict the failure of medical equipment,a method for failure life cycle prediction of medical equipment was developed.The system is divided into four modules:the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage,the middle stage,and the later stage;the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module;and the main function of the fault diagnosis module is based on the normal value of the relevant sensitive data in the whole life cycle management module.The inference machine diagnoses the operation data of the equipment;the fault prediction module constructs a fine prediction system based on the least square support vector machine algorithm and uses the AFS-ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters;the optimal model is then used to predict the failure of medical equipment.Comparative experiments are designed to determine whether or not the design system is effective.The results demonstrate that the suggested system accurately predicts the breakdown of ECG diagnostic equipment and incubators and has a high level of support and dependability.The design system has the minimum prediction error and the quickest program execution time compared to the comparison system.Hence,the design system is able to accurately predict the numerous causes and types of medical device failure.

Assessing myocardial indices and inflammatory factors to determine anxiety and depression severity in patients with chronic heart failure

BACKGROUND Patients with chronic heart failure(CHF)have a progressive disease that is associated with poor quality of life and high mortality.Many patients experience anxiety and depression(A&D)symptoms,which can further accelerate disease progression.We hypothesized that indicators of myocardial function and inflammatory stress may reflect the severity of A&D symptoms in patients with CHF.Changes in these biomarkers could potentially predict whether A&D symptoms will deteriorate further in these individuals.AIM To measure changes in cardiac and inflammatory markers in patients with CHF to determine A&D severity and predict outcomes.METHODS We retrospectively analyzed 233 patients with CHF treated at the Jingzhou Hospital,Yangtze University between 2018-2022 and grouped them according to Self-Rating Anxiety Scale(SAS)and Self-Rating Depression Scale(SDS)scores.We compared clinical data in the no-A&D,mild-A&D,moderate-A&D,and severe-A&D groups,the SAS and SDS scores with the New York Heart Association(NYHA)functional classification,and cardiac markers and inflammatory factors between the no/mild-A&D and moderate/severe-A&D groups.Regression analysis was performed on the markers with P<0.05 to determine their ability to predict A&D severity in patients and the area under the receiver operating characteristic curve(AUROC)was used to evaluate their accuracy.RESULTS In the inter-group comparison,the following variables had an effect on A&D severity in patients with CHF:NYHA class,left ventricular ejection fraction(LVEF),left ventricular end-diastolic diameter,N-terminal pro-brain natriuretic peptide(NT-proBNP),interleukin-6(IL-6),and tumor necrosis factor-alpha(P<0.05).Other variables did not differ significantly between the A&D groups(P>0.05).In addition,we found that higher NYHA classes were associated with higher the SAS and SDS scores(P<0.05).Regression analysis showed that LVEF,NTproBNP,and IL-6 were independent risk factors for A&D severity(P<0.05).Among them,NT-proBNP had the best predictive ability as a single indicator(AUROC=0.781).Furthermore,the combination of these three indicators exhibited a good predictive effect toward discriminating the extent of A&D severity among patients(AUROC=0.875).CONCLUSION Cardiac and inflammatory biomarkers,such as LVEF,NT-proBNP,and IL-6,are correlated with A&D severity in patients with CHF and have predictive value.

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory

The research on the rock burst prediction was made on the basis of seismology,rock mechanics and the data from Dongguashan Copper Mine(DCM) ,the deepest metal mine in China.The seismic responses to mining in DCM were investigated through the analyses of the spatio-temporal distribution of hypocenters,apparent stress and displacement of seismic events,and the process of the generation of hazardous seismicity in DCM was studied in the framework of the theory of asperity in the seismic source mechanism.A method of locating areas with hazardous seismicity and a conceptual model of hazardous seismic nucleation in DCM were proposed.A criterion of rockburst prediction was analyzed theoretically in the framework of unstable failure theories,and consequently,the rate of change in the ratio of the seismic stiffness of rock in a seismic nucleation area to that in surrounding area,dS/dt,is defined as an index of the rockburst prediction.The possibility of a rockburst will increase if dS/dt>0,and the possibility of rock burst will decrease if dS/dt<0.The correctness of these methods is demonstrated by analyses of rock failure cases in DCM.

Experimental investigation on synergetic prediction of granite rockburst using rock failure time and acoustic emission energy

The frequent occurrence of rockburst and the difficulty in predicting were considered in deep engineering and underground engineering.In this work,laboratory experiments on rockburst under true triaxial conditions were carried out with granite samples.Combined with the deformation characteristics of granite,acoustic emission(AE)technology was well applied in revealing the evolution law of micro-cracks in the process of rockburst.Based on the comprehensive analysis of acoustic emission parameters such as impact,ringing and energy,the phased characteristics of crack propagation and damage evolution in granite were obtained,which were consistent with the stages of rock deformation and failure.Subsequently,based on the critical point theory,the accelerated release characteristics of acoustic emission energy during rockburst were analyzed.Based on the damage theory,the damage evolution model of rock under different loading conditions was proposed,and the prediction interval of rock failure time was ascertained concurrently.Finally,regarding damage as an intermediate variable,the synergetic prediction model of rock failure time was constructed.The feasibility and validity of model were verified.

Prediction model of in-hospital mortality in elderly patients with acute heart failure based on retrospective study

Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 years and older from intensive care units of Cardiology De- partment in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system （RPSS）. The area under the curve （AUC） of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines-heart failure （GWTG-HF）. Re- sults By binary logistic regression analysis, heart rate （OR： 1.043, 95% CI： 1.030-1.057, P 〈 0.001）, left ventricular ejection fraction （OR： 0.918, 95% CI： 0.833~）.966, P 〈 0.001）, pH value （OR： 0.001, 95% CI： 0.000-0.002, P 〈 0.001）, renal dysfunction （OR： 0.120, 95% CI： 0.066M）.220, P 〈 0.001） and NT-pro BNP （OR： 3.463, 95% CI： 1.870-6.413, P 〈 0.001） were independent risk factors of in-hospital mortal- ity for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk predic- tion than GWTG-THF （AUC： 0.873 vs. 0.818, P = 0.016）. Conclusions Our risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with A/IF.

Prediction of sudden death in elderly patients with heart failure

Most heart failure （HF） related mortality is due to sudden cardiac death （SCD） and worsening HF, particularly in the case of reduced ejection fraction. Predicting and preventing SCD is an important goal but most works include no or few patients with advanced age, and the prevention of SCD in elderly patients with HF is still controversial. A recent reduction in the annual rate of SCD has been recently described but it is not clear if this is also true in advanced age patients. Age is associated with SCD, although physicians frequently have the perception that elderly patients with HF die mainly of pump failure, underestimating the importance of SCD. Other clinical variables that have been associated to SCD are symptoms, New York Heart Association functional class, ischemic cause, and comorbidities （chronic obstructive pulmonary disease, renal dysfunction and diabetes）. Some test results that should also be considered are left ventricular ejection fraction and diameters, natriuretic peptides, non-sustained ventricular tachycardias and autonomic abnormalities. The combination of all these markers is probably the best option to predict SCD. Different risk scores have been described and, although there are no specific ones for elderly populations, most include age as a risk predictor and some were developed in populations with mean age 〉 65 years. Finally, it is important to stress that these scores should be able to predict any type of SCD as, although most are due to tachyarrhythmias, bradyarrhythmias also play a role, particularly in the case of the elderly.

Mechanism and prediction of failure of diamond films deposited on various substrates by HFCVD

Diamond films were deposited on the WC-Co cemented carbide and Si3N4 ceramic cutting tool substrates by hot-filament-assisted chemical vapour deposition. The adherence property of diamond films was estimated using the critical load (Pcr) in the indentation test. The adhesive strength of diamond films is related to the intermediate layer between the film and the substrate. Poor adhesion of diamond films to polished cemented carbide substrate is owing to the formation of graphite phase in the interface. The adhesion of diamond films deposited on acid etched cemented carbide substrate is improved, and the peeling-off of the films often happens in the loosen layer of WC particles where the cobalt element is nearly removed. The diamond films' adhesion to cemented carbide substrate whose surface layer is decarbonizated is strengthened dramatically because WC phase forms by reaction between the deposited carbon and tungsten in the surface layer of substrates during the deposition of diamond, which results in chemical combination in the film-substrate interface. The adhesion of diamond films to silicon nitride substrate is the firmest due to the formation of chemical combination of the SiC intermediate layer in the interfaces. In the piston-turning application, the diamond-coated Si3N4 ceramic and the cemented carbide cutting tools usually fail in the form of collapsing of edge and cracking or flaking respectively. They have no built-up edge(BUE) as long as coating is intact.As it wears through, BUE develops and the cutting force on it increases 1 - 3 times than that prior to failure. This can predict the failure of diamond-coated cutting tools.

Combined ANN prediction model for failure depth of coal seam floors

Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.

A Novel Method of Heart Failure Prediction Based on DPCNN-XGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors such as doctors’knowledge and experience.The accuracy is difficult to guarantee and has a serious lag.In this paper,a mixture prediction model is proposed for perioperative adverse events of heart failure,which combined with the advantages of the Deep Pyramid Convolutional Neural Networks(DPCNN)and Extreme Gradient Boosting(XGBOOST).The DPCNN was used to automatically extract features from patient’s diagnostic texts,and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients,then the XGBOOST algorithm was used to construct the prediction model of heart failure.An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018.The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3%and 31%compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.

FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.