Reliability Prediction Method Research Articles

Stripping torques in human bone can be reliably predicted prior to screw insertion with optimum tightness being found between 70% and 80% of the maximum.

AimsTo devise a method to quantify and optimize tightness when inserting cortical screws, based on bone characterization and screw geometry.MethodsCortical human cadaveric diaphyseal tibiae screw holes (n = 20) underwent destructive testing to firstly establish the relationship between cortical thickness and experimental stripping torque (Tstr), and secondly to calibrate an equation to predict Tstr. Using the equation’s predictions, 3.5 mm screws were inserted (n = 66) to targeted torques representing 40% to 100% of Tstr, with recording of compression generated during tightening. Once the target torque had been achieved, immediate pullout testing was performed.ResultsCortical thickness predicted Tstr (R2 = 0.862; p < 0.001) as did an equation based on tensile yield stress, bone-screw friction coefficient, and screw geometries (R2 = 0.894; p < 0.001). Compression increased with screw tightness up to 80% of the maximum (R2 = 0.495; p < 0.001). Beyond 80%, further tightening generated no increase in compression. Pullout force did not change with variations in submaximal tightness beyond 40% of Tstr (R2 = 0.014; p = 0.175).ConclusionScrew tightening between 70% and 80% of the predicted maximum generated optimum compression and pullout forces. Further tightening did not considerably increase compression, made no difference to pullout, and increased the risk of the screw holes being stripped. While further work is needed for development of intraoperative methods for accurate and reliable prediction of the maximum tightness for a screw, this work justifies insertion torque being considerably below the maximum.Cite this article: Bone Joint Res 2020;9(8):493–500.

Research on Equipment Life Cycle Cost Prediction Based on GA-LSSVM

Penalty parameter γ and kernel parameter σ 2 are two important parameters of LSSVM. In this paper, the GA-LSSVM prediction model is constructed, which the genetic algorithm was used to optimize the parameters. Comparing with LSSVM model, the GA-LSSVM model improves the disadvantage that the parameters can only obtained by experience. Also in order to check model prediction accuracy, the posterior difference method(PDM) was used. The calculation results show that the GA-LSSVM model has higher prediction accuracy than other models, and it is a feasible and reliable method for equipment life cycle cost prediction, also the model can be extended to other prediction fields.

Web service reliability prediction based on machine learning

Web service reliability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition (such as latency). Due to the features of web dynamics, traditional reliability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these new challenges, in this paper, we propose a new web service reliability prediction method based on machine learning considering user, web service and web condition. First we solve the web condition parameter sparsity problem, then we use the k-means clustering method to aggregate past invocation data, incorporate user, service, and web condition parameters to build a reliability feedback matrix, at last we predict web service reliability by considering specific web condition environments. The experiment shows that our machine learning method is able to solve the data sparsity problem and improve accurate web service reliability prediction, and we discuss how data sparsity and the number of feedback clusters to affect web service reliability prediction.

Crop Photosynthetic Performance Monitoring Based on a Combined System of Measured and Modelled Chloroplast Electron Transport Rate in Greenhouse Tomato.

Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO2 assimilation rate (AL, µmol CO2 m-2 s-1) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between AL and light-quantum yield of photosystem II (Φ2), we defined three objectives: (1) to explore the relationship between climate variables and AL, as well as Φ2; (2) create a simple and reliable method for real‐time prediction of AL with continuously Φ2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in AL modelling. Due to practical obstacles in measuring CO2 gas-exchange in commercial production, we explored a method to predict AL by measuring Φ2 of leaves in a commercial hydroponic greenhouse tomato crop (“Pureza”). We calculated AL with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict AL with large uncertainty (R2 0.64; RMSE 2.21), while using Φ2 as input to Model II could be used to improve the prediction accuracy of AL (R2 0.71; RMSE 1.98). Our results suggests that (1) Φ2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate (J) and AL. The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.

Prediction of heart disease and classifiers\u2019 sensitivity analysis

BackgroundHeart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases.ResultsIt was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases.ConclusionDifferent classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.

FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.

Growing evidence indicates that the development and progression of multiple complex diseases are influenced by microRNA (miRNA). Identifying more miRNAs as biomarkers for clinical diagnosis, treatment and prognosis is vital to promote the development of bioinformatics and medicine. Considering that the traditional biological experimental methods are generally time-consuming and expensive, high-efficient computational methods are encouraged to uncover potential disease-related miRNAs. In this paper, FCGCNMDA is presented to predict latent miRNA-disease associations by utilizing fully connected graph convolutional networks. Specially, our method first constructs a fully connected graph in which edge weights represent correlation coefficient between any two pairs of miRNA-disease pair, and then feeds this fully connected graph along with miRNA-disease pairs feature matrix into a two-layer graph convolutional networks (GCN) for training. At last, we utilize the trained network to predict the scores for unknown miRNA-disease pairs. As a result, FCGCNMDA achieves AUC value of [Formula: see text] and AUPRC value of [Formula: see text] in HMDD v2.0 based on five-fold cross validation. Moreover, case studies on Lymphoma, Breast Neoplasms and Prostate Neoplasms shown that 98%, 98%, 98% of the top 50 selected miRNAs were validated by recent experimental evidence. From above results, we can deduce that FCGCNMDA can be regarded as reliable method for potential miRNA-disease associations prediction.

Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.

Reliability prediction of further transit service based on support vector machine

The requirement for transit reliability grows with the increase of pace of life since unstable bus arrivals can raise the anxiety of waiting passengers. This paper proposes a reliability assessment method to evaluate the reliability of each bus stop on the route and the reliability of bus routes. In reliability prediction, the prediction target is locked by rolling horizon to reduce the interference of other information. In addition, a prediction method of the reliability of further transit service using the accurate online support vector machine is proposed. This prediction can provide more accurate and stable data for the arrival of buses and reduce unnecessary waiting of passengers. Finally, the reliability prediction method proposed is tested with the real data of a bus route in Dalian, China. The results show that the accurate online support vector machine with reasonable parameters can predict the reliability of transit service accurately.

Influence of Kinematic Analysis Parameters of Drag Anchor Trajectory Prediction Using Yield Envelope Method

Drag anchor is widely applied in offshore engineering for offshore mooring systems. The prediction of the invisible trajectory during its drag-in installation is challenging for anchor design in determining the anchor final position for ensuring sufficient holding capacity. The yield envelope method based on deep anchor failure for kinematic analysis was proposed as a promising trajectory prediction method for drag anchor. However, there is a lack of analysis on the effects of the parameters applied in the kinematic analysis. The current work studies the effects of the yield envelope parameters, anchor line bearing capacity factor and the anchor/soil interface friction. It is found that the accuracy of the yield envelope parameters has large impact on the prediction results based on deep yield envelopes. Analyses of cases with smooth fluke predict deeper embedment depth than that from analyses of cases with rough fluke. The decrease of the capacity factor results in the increase of the anchor embedment depth, the anchor line load, the anchor chain angle and the stable value of the normalized horizontal load component for the same drag length, while the stable value of the normalized vertical load component decreases when the capacity factor decreases. This illustrates the importance in applying reasonable parameters and improving the method for more reliable prediction of the anchor trajectory.

Applicability of the Øie-Tozer model to predict three types of distribution volume (Vd) in humans: Vd in central compartment, Vd at steady state, and Vd at beta phase.

This study aimed to investigate the applicability of the Øie-Tozer model to predict human distribution volume (Vd) in the central compartment (V1 ), Vd at steady state (Vdss ), and Vd at beta phase (Vdβ ) based on animal Vd. Twenty compounds that have a human V1 /Vdss of 0.053-0.66 were selected from the literature. After intravenous administration of the compounds at 0.1 mg/kg to rats, dogs, and monkeys, plasma concentrations were determined, and pharmacokinetic parameters were obtained by one/two-compartmental analyses. The human V1 , Vdss , and Vdβ were predicted from animal Vd using the Øie-Tozer model, and the predictability was compared with that using proportionality and simple allometry. The Øie-Tozer model was the most reliable method for the overall prediction of Vd and applicable for accurately predicting human V1 , Vdss , and Vdβ (89%, 85%, and 68% of the compounds within a 3-fold error, respectively) when data of monkey for V1 and data of three animal species for Vdss and Vdβ were used. Additionally, the predicted human Vd with the two-compartment model was applicable for predicting pharmacokinetic profiles/parameters in humans after intravenous administration of 18 compounds [except for valproic acid (monophasic elimination profile) and chlorpromazine (deviation: Vdss < V1 )]. The prediction was more accurate than that using the predicted Vdss with the one-compartment model (e.g., underestimation of maximum plasma concentrations: 2 vs 8 compounds within a 3-fold error, respectively). In summary, the Øie-Tozer model was applicable for predicting human V1 , Vdss , and Vdβ , and their predicted Vd with the two-compartment model can lead to accurate pharmacokinetic prediction of compounds that show biphasic elimination.

Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system

In-depth knowledge of the customers and a better understanding of their heat use is the basis of accurate heating load prediction model, it is also the cornerstone for realizing the intelligent and multi-energy development of the heating system. Most of existing heating load prediction models were established mainly based on outdoor meteorological parameter, while few of them took indoor temperature and user behavior into account. This paper took the wireless on-off control system as an example, and the heat user behavior was analyzed by the open valve number. Different heat users have different heat behaviors in different time periods, which are reflected in the change of open valve number. The number of open valve was first time introduced into the heating load prediction model (Model-2) in this paper. The results showed that the prediction accuracy of Model-2 was significantly improved compared with the traditional model (Model-1) including multiple linear regression (MLR) and support vector machine (SVM). K-means algorithm was used to identify and cluster the heat user behavior. Comparing the heating load prediction accuracy of MLR and SVM before and after clustering (Model-2 and Model-3), the results showed that Model-3 had a much higher accuracy. The rank of the models was Model-3, Model-2 and Model-1 in turn, in terms of the performance coefficients for model training and testing. The Model-3 proposed in this paper provides a reliable heating load prediction method for wireless on-off control system.

Simulation of Reliability Prediction Based on Multiple Factors for Spinning Machine

Abstract Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.

Variable Responses to Corneal Grafts: Insights from Immunology and Systems Biology.

Corneal grafts interact with their hosts via complex immunobiological processes that sometimes lead to graft failure. Prediction of graft failure is often a tedious task due to the genetic and nongenetic heterogeneity of patients. As in other areas of medicine, a reliable prediction method would impact therapeutic decision-making in corneal transplantation. Valuable insights into the clinically observed heterogeneity of host responses to corneal grafts have emerged from multidisciplinary approaches, including genomics analyses, mechanical studies, immunobiology, and theoretical modeling. Here, we review the emerging concepts, tools, and new biomarkers that may allow for the prediction of graft survival.

Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures.

Drug-induced liver injury (DILI) is one of the major reasons for termination of drug development. Due to the importance of predicting DILI in early phases of drug development, diverse in silico models have been developed to filter out DILI-causing candidates before clinical study. However, no computational models have achieved sufficient prediction power for screening DILI in early phases because 1) drugs often cause liver injury through reactive metabolites, 2) different clinical outcomes of DILI have different mechanisms, and 3) the DILI label on drugs is not clearly defined. In this study, we developed binary classification models to predict drug-induced cholestasis, cirrhosis, hepatitis, and steatosis based on the structure of drugs and their metabolites. DILI-positive data was obtained from post-market reports of drugs and DILI-negative data from DILIrank, a database curated by the Food and Drug Administration (FDA). Support vector machine (SVM) and random forest (RF) were used in developing models with nine fingerprints and one 2D molecular descriptor calculated from drug (152 DILI-positives and 102 DILI-negatives) and drug metabolite (192 DILI-positives and 126 DILI-negatives) structures. Models were developed according to Organisation for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationship (QSAR) validation. Internal and external validation was performed with a randomization test in order to thoroughly examine model predictability and avoid random correlation between structural features and adverse outcomes. The applicability domain was defined with a leverage method for reliable prediction of new chemicals. The best models for each liver disease were selected based on external validation results from drugs (cholestasis: 70%, cirrhosis: 90%, hepatitis: 83%, and steatosis: 85%) and drug metabolites (cholestasis: 86%, cirrhosis: 88%, hepatitis: 86%, and steatosis: 83%) with applicability domain analysis. Compiled data sets were further exploited to derive privileged substructures that were more frequent in DILI-positive sets compared to DILI-negative sets and in drug metabolite structures compared to drug structures with a Morgan fingerprint level 2.

Dynamic reliability prediction for the steel box girder based on multivariate Bayesian dynamic Gaussian copula model and SHM extreme stress data

This article presents the dynamic reliability prediction method of the existing steel box girder considering the time-variant nonlinear correlations among the performance functions for the failure modes at the multiple control monitoring points. Firstly, the multivariate Bayesian dynamic linear model (MBDLM) considering the nonlinear correlations among the multiple variables is built to predict the extreme stresses at the different control monitoring points; secondly, based on the predicted covariance matrix of MBDLM, the dynamic correlation coefficients between any two performance functions can be accurately predicted; and finally, multivariate Bayesian dynamic Gaussian copula model through combining MBDLM with Gaussian copula technique is proposed to predict the dynamic reliability of the steel box girder, and the monitoring extreme stress data of an actual bridge are provided to illustrate the feasibility and application of the proposed method. The results show that predicted dynamic reliability of the bridge girder with considering the time-variant nonlinear correlation of failure modes at the multiple control monitoring points is bigger than that without considering the time-dependent nonlinear correlation. It is illustrated that the predicted results without considering the dynamic nonlinear correlation of failure modes at the multiple control monitoring points are more conservative.

Research on Short-term Ionospheric Prediction Combining with EOF and ARIMA Model Over Guangxi Area

Abstract. According to the empirical orthogonal function (EOF), the non-stationary time series data are decomposed into time function and space function, so this mathematical method can simplify the non-stationary time series and eliminate redundant information, thus it performs well in non-stationary time series analysis. The ionospheric Vertical Total Electron Content (VTEC) is a non-stationary time series, which has non-stationary and seasonal variation and the activity of VTEC is more active in low latitudes. Guangxi is located in the middle and low latitudes of the Northern Hemisphere with abundant sunshine in summer and autumn. The energy released by solar radiation makes the ionospheric activity in this region more complex than that in the high latitudes. However, no expert or scholar has used EOF analysis method to conduct a comprehensive study of the low latitudes. The International GNSS Service (IGS) provided by high precision Global Ionospheric Maps (GIM) center in Guangxi are used in the modeling data, the GIM data of the first 10 days of different seasons are decomposed by EOF, and then the time function is predicted by ARIMA model. VTEC values for the next five days are obtained through reconstruction, and relative accuracy and standard deviation are used as accuracy evaluation criteria. The results of EOF-ARIMA model are compared with those of ARIMA model, and the prediction accuracy of EOF-ARIMA model at the equatorial anomaly is analyzed in order to explore the reliability of the model in the more complex region of ionospheric activity. The results show that the average relative precision of EOF-ARIMA model is 84.0, the average standard deviation is 7.45TECu, the average relative precision of ARIMA model is 81.5, the average standard deviation is 8.29TECu, and the precision of EOF-ARIMA model is higher than that of ARIMA model.; There is no significant seasonal difference in the prediction accuracy of EOF-ARIMA model, and the prediction accuracy of ARIMA model in autumn is lower than that of other seasons, which indicates that the prediction results of EOF-ARIMA model are more reliable; The prediction accuracy of the EOF-ARIMA model at the equatorial anomaly is not affected, and it is consistent with the accuracy of the high latitude area in Guangxi. It is shown that the EOF-ARIMA model has high accuracy and stability in the short-term ionospheric prediction in Guangxi at low latitudes of China, and provides a new and reliable method for ionospheric prediction at low latitudes.

Application of Seismic Waveform Inversion in Prediction of Tight Sandstone Conglomerate Reservoir in Surenuoer Oilfield

The reservoir of Tongluomiao Temple in Surenuoer Oilfield is dominated by glutenite. The sand body is dense, the buried depth is shallow, the heterogeneity is strong, and the oil-water relationship is complex. The favorable reservoir sand body prediction has always plagued the block exploration and development. Therefore, based on the seismic waveform indication inversion principle, process and key parameter settings, based on the petrophysical analysis in the study area, the reservoir inversion technique is studied, and the well interpolation model and the waveform indication inversion model are proposed. The method generates the inversion prediction model of the study area. On this basis, the threshold value of the layer is used to extract the thickness distribution of the sand body plane, and then the favorable target area is predicted. The actual drilling results show that the thickness of the sand body predicted by the method and the actual drilling The results are in good agreement and the oil and gas shows good, providing a reliable technical method for reservoir prediction.

Reliable energy prediction method for grid connected photovoltaic power plants situated in hot and dry climatic condition

This paper presents a mathematical model to predict the energy generation of photovoltaic power plant in hot and humid climatic condition. This model is based on meteorological data and laboratory tested solar module parameters with twenty-four inputs and one output. In addition the twenty-four inputs drive an equation to calculate final energy generation from photovoltaic power plant. Validation of the proposed model was done by comparing the results of predicted energy generation using proposed model and PVWATT software model for two existing PV power plants of India. Monthly and annual energy production and errors will be the main criteria for the selection of batter model. The result shows that in comparison with PVWATT software proposed model was found to be more efficient and accurate to predict energy generation and proposed model also reduces mean absolute percentage error and root mean square error significantly compared to PVWATT software for hot and humid climatic condition.

Personalized Dosimetry for Liver Cancer Y-90 Radioembolization Using Computational Fluid Dynamics and Monte Carlo Simulation.

Yttrium-90 (Y-90) transarterial radioembolization uses radioactive microspheres injected into the hepatic artery to irradiate liver tumors internally. One of the major challenges is the lack of reliable dosimetry methods for dose prediction and dose verification. We present a patient-specific dosimetry approach for personalized treatment planning based on computational fluid dynamics (CFD) simulations of the microsphere transport combined with Y-90 physics modeling called CFDose. The ultimate goal is the development of a software to optimize the amount of activity and injection point for optimal tumor targeting. We present the proof-of-concept of a CFD dosimetry tool based on a patient's angiogram performed in standard-of-care planning. The hepatic arterial tree of the patient was segmented from the cone-beam CT (CBCT) to predict the microsphere transport using multiscale CFD modeling. To calculate the dose distribution, the predicted microsphere distribution was convolved with a Y-90 dose point kernel. Vessels as small as 0.45mm were segmented, themicrosphere distribution between the liver segments using flow analysis was predicted,the volumetric microsphere and resulting dose distribution in the liver volume were computed. The patient was imaged with positron emission tomography (PET) 2h after radioembolization to evaluate the Y-90 distribution. The dose distribution was found to be consistent with the Y-90 PET images. These results demonstrate the feasibility of developing a complete framework for personalized Y-90 microsphere simulation and dosimetry using patient-specific input parameters.

Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

Phase change memory (PCM) has reached the level of mass production. The first step in mass production is determining the proper pulse conditions of high-resistance (HR) and low-resistance (LR) states to realize the best performance of PCM chips on the basis of longer endurance characteristics. However, due to the neglect of each of the relations as well as the square term of each relationship for pulse conditions, the standard screening method for pulse conditions cannot accurately determine the optimal pulse conditions. A new statistical prediction method based on regression analysis is presented in this work. The method can model and predict the optimal pulse conditions of PCM chips on the basis of longer endurance characteristics. In the method, the parameter estimates, model equations and surface plot are generated by the least-mean-square (LMS) method for the regression analysis; the prediction model is established by monitoring the distributions of the resistance values collected from a 4 Kbit block of the 4Mbit PCM test chips in 40 nm complementary metal oxide semiconductor (CMOS) process.