WorldWideScience

Sample records for parameters predicting failure

  1. Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures

    Science.gov (United States)

    Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.

    2012-12-01

    Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

  2. Advancement Flap for Treatment of Complex Cryptoglandular Anal Fistula: Prediction of Therapy Success or Failure Using Anamnestic and Clinical Parameters.

    Science.gov (United States)

    Boenicke, Lars; Karsten, Eduard; Zirngibl, Hubert; Ambe, Peter

    2017-09-01

    Multiple new procedures for treatment of complex anal fistula have been described in the past decades, but an ideal single technique has yet not been identified. Factors that predict the outcome are required to identify the best procedure for each individual patient. The aim of this study was to find those predictors for advancement flap at midterm follow-up. From 2012 to 2015 in a tertiary university clinic, all patients who underwent advancement flap for treatment of complex cryptoglandular fistula were prospectively enrolled. Pre- and postoperatively standardized anamnestic and clinical examinations were performed. Predictive factors for therapy failure were identified using univariate and multivariate analysis. Out of 65 patients, 61 (93%) completed all examinations and were included in the study. Therapy failure after a mean follow-up period of 25 months occurred in total n = 11 patients (18%). There was no significant disturbance of continence among the entire study cohort as shown by the incontinence score (preop 0.34 ± 0.91 pts., postop 0.37 ± 0.97 pts.; p = 0.59). Univariate analysis for risk factors for therapy failure revealed age (p = 0.004), history of surgical abscess drainage (p = 0.04), BMI (p = 0.002), suprasphincteric fistula (p = 0.019) and horseshoe abscess (p = 0.036) as independent parameters for therapy failure. During multivariate analysis, only history of surgical abscess drainage (OR = 8.09, p = 0.048, 95% CI 0.98-64.96), suprasphincteric fistula (OR = 6.83, p = 0.032, 95% CI 1.17-6.83) and BMI (OR = 1.23, p = 0.017, 95% CI 1.03-1.46) were independent parameters for therapy failure. Advancement flap for treatment of complex fistula is effective and has low risk of disturbed continence. BMI, suprasphincteric fistula and history of surgical abscess drainage are predictors for therapy failure.

  3. Failure probability under parameter uncertainty.

    Science.gov (United States)

    Gerrard, R; Tsanakas, A

    2011-05-01

    In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location-scale families (including the log-normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications. © 2010 Society for Risk Analysis.

  4. Failure Prediction And Detection In Cloud Datacenters

    Directory of Open Access Journals (Sweden)

    Purvil Bambharolia

    2017-09-01

    Full Text Available Cloud computing is a novel technology in the field of distributed computing. Usage of Cloud computing is increasing rapidly day by day. In order to serve the customers and businesses satisfactorily fault occurring in datacenters and servers must be detected and predicted efficiently in order to launch mechanisms to tolerate the failures occurred. Failure in one of the hosted datacenters may propagate to other datacenters and make the situation worse. In order to prevent such situations one can predict a failure proliferating throughout the cloud computing system and launch mechanisms to deal with it proactively. One of the ways to predict failures is to train a machine to predict failure on the basis of messages or logs passed between various components of the cloud. In the training session the machine can identify certain message patterns relating to failure of data centers. Later on the machine can be used to check whether a certain group of message logs follow such patterns or not. Moreover each cloud server can be defined by a state which indicates whether the cloud is running properly or is facing some failure. Parameters such as CPU usage memory usage etc. can be maintained for each of the servers. Using this parameters we can add a layer of detection where in we develop a decision tree based on these parameters which can classify whether the passed in parameters to the decision tree indicate failure state or proper state.

  5. Diagnosis of Nonischemic Stage B Heart Failure in Type 2 Diabetes Mellitus: Optimal Parameters for Prediction of Heart Failure.

    Science.gov (United States)

    Wang, Ying; Yang, Hong; Huynh, Quan; Nolan, Mark; Negishi, Kazuaki; Marwick, Thomas H

    2018-05-11

    This study sought to identify whether impaired global longitudinal strain (GLS), diastolic dysfunction (DD), or left atrial enlargement (LAE) should be added to stage B heart failure (SBHF) criteria in asymptomatic patients with type 2 diabetes mellitus. SBHF is a precursor to clinical heart failure (HF), and its recognition justifies initiation of cardioprotective therapy. However, original definitions of SBHF were based on LV hypertrophy and impaired ejection fraction. Patients with asymptomatic type 2 diabetes mellitus ≥65 years-of-age (age 71 ± 4 years; 55% men) with preserved ejection fraction and no ischemic heart disease were recruited from a community-based population. All underwent a standard clinical evaluation, and a comprehensive echocardiogram, including assessment of left ventricular hypertrophy (LVH), LAE, DD (abnormal E/e'), and GLS (<16%). Over a median follow-up of 1.5 years (range 0.5 to 3), 20 patients were lost to follow-up, and 290 individuals were entered into the final analyses. In this asymptomatic group, LV dysfunction was identified in 30 (10%) by DD, 68 (23%) by LVH, 102 (35%) by LAE, and 68 (23%) by impaired GLS. New-onset HF developed in 45 patients and 4 died, giving an event rate of 112/1,000 person-years. Survival free of the composite endpoint (HF and death) was about 1.5-fold higher in patients without a normal, compared with an abnormal echocardiogram. LVH, LAE, and GLS <16% were associated with increased risk of the composite endpoint, independent of ARIC risk score and glycosylated hemoglobin, but abnormal E/e' was not. The addition of left atrial volume and GLS provided incremental value to the current standard of clinical risk (ARIC score) and LVH. In a competing-risks regression analysis, LVH (hazard ratio: 2.90; p < 0.001) and GLS <16% (hazard ratio: 2.26; p = 0.008), but not DD and LAE were associated with incident HF. Subclinical left ventricular systolic dysfunction is prevalent in asymptomatic elderly patients

  6. Predictable anomalies of process parameters on failure mode of internal structures in RPV by transient thermal-hydraulic analysis

    International Nuclear Information System (INIS)

    Maki, Akira; Mori, Michitsugu; Kanemoto, Shigeru; Konishi, Hideo

    1997-01-01

    A study has been conducted to evaluate how process parameters will exhibit the change in the event of the troubles related to reactor internal by using transient thermal-hydraulic analysis codes (RETRAN3D-MOD002, etc.). In the present study, the following six events are analytically investigated: 1) a leak from the upper plenum; 2) a leak from the middle part of a shroud; 3) a leak from the lower plenum; 4) a leak from the riser pipe for the jet-pump; 5) the blockage of the jet-pump nozzle; and 6) a leak from the jet-pump diffuser. The results by analyses indicated that the leak from the upper plenum resulted in increasing in the inlet temperature of primary loop recirculation (PLR) and in the differential pressure at the core support plate, and decreasing in the neutron flux (reactor power). Similar analyses were made for the five other events to identify the pattern of relevant process parameter variation in each event. (author)

  7. Detecting failure of climate predictions

    Science.gov (United States)

    Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

    2016-01-01

    The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

  8. Echocardiographic assessment of right ventricular function in routine practice: Which parameters are useful to predict one-year outcome in advanced heart failure patients with dilated cardiomyopathy?

    Science.gov (United States)

    Kawata, Takayuki; Daimon, Masao; Kimura, Koichi; Nakao, Tomoko; Lee, Seitetsu L; Hirokawa, Megumi; Kato, Tomoko S; Watanabe, Masafumi; Yatomi, Yutaka; Komuro, Issei

    2017-10-01

    Right ventricular (RV) function has recently gained attention as a prognostic predictor of outcome even in patients who have left-sided heart failure. Since several conventional echocardiographic parameters of RV systolic function have been proposed, our aim was to determine if any of these parameters (tricuspid annular plane systolic excursion: TAPSE, tissue Doppler derived systolic tricuspid annular motion velocity: S', fractional area change: FAC) are associated with outcome in advanced heart failure patients with dilated cardiomyopathy (DCM). We retrospectively enrolled 68 DCM patients, who were New York Heart Association (NYHA) Class III or IV and had a left ventricular (LV) ejection fraction functional class IV, plasma brain natriuretic peptide concentration, intravenous inotrope use, left atrial volume index, and FAC were associated with outcome, whereas TAPSE and S' were not. Receiver-operating characteristic curve analysis showed that the optimal FAC cut-off value to identify patients with an event was rights reserved.

  9. Parameters governing the failure of steel components

    International Nuclear Information System (INIS)

    Schmitt, W.

    1977-01-01

    The most important feature of any component is the ability to carry safely the load it is designed for. The strength of the component is influenced mainly by three groups of parameters: 1. The loading of the structure; Here the possible loading cases are: normal operation, testing, emergency and faulted conditions; the kinds of loading can be divided into: internal pressure, external forces and moments, temperature loading. 2. The defects in the structure: cavities and inclusions, pores, flaws or cracks. 3. The material properties: Young's modulus, Yield - and ultimate strength, absorbed charpy energy, fracture toughness, etc. For different failure modes one has to take into account different material properties, the loading and the defects are assumed to be within certain deterministic bounds, from which deterministic safety factors can be determined with respect to any failure mode and failure criterion. However, since all parameters have a certain scatter about a mean value, there is a probability to exceed the given bounds. From the extrapolation of the distribution a value for the failure probability can be estimated. (orig.) [de

  10. Toward full-chip prediction of yield-limiting contact patterning failure: correlation of simulated image parameters to advanced contact metrology metrics

    Science.gov (United States)

    Sturtevant, John L.; Chou, Dyiann

    2006-03-01

    to manufacturing. This paper will focus on 130 nm node contact patterning, and will correlate SEM Profile Grade output to the extensive suite of model-based image tags from the Calibre TM opc-verification engine. With an understanding of which image parameters are most highly correlated to the occurrence of incomplete contact formation for a given process, the process model can be used to automatically direct inspection metrology to those layout instances that pose the highest risk of patterning failure through the lithographic process window. Such an approach maximizes the value content of in-line metrology.

  11. Failure Prediction for Autonomous Driving

    OpenAIRE

    Hecker, Simon; Dai, Dengxin; Van Gool, Luc

    2018-01-01

    The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is important that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. We conjecture that failures do not occur randomly. For instance, driving models may fail more likely at places ...

  12. Predicting survival in heart failure

    DEFF Research Database (Denmark)

    Pocock, Stuart J; Ariti, Cono A; McMurray, John J V

    2012-01-01

    AimsUsing a large international database from multiple cohort studies, the aim is to create a generalizable easily used risk score for mortality in patients with heart failure (HF).Methods and resultsThe MAGGIC meta-analysis includes individual data on 39 372 patients with HF, both reduced...

  13. The function and failure of sensory predictions.

    Science.gov (United States)

    Bansal, Sonia; Ford, Judith M; Spering, Miriam

    2018-04-23

    Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions. We review studies supporting a prediction-failure hypothesis of perceptual and cognitive disturbances. We also outline neural correlates underlying prediction function and failure, highlighting similarities across the visual, auditory, and somatosensory systems. In linking basic psychophysical and psychophysiological evidence of visual, auditory, and somatosensory prediction failures to neuropsychiatric symptoms, our review furthers our understanding of disease mechanisms. © 2018 New York Academy of Sciences.

  14. Strain limit criteria to predict failure

    International Nuclear Information System (INIS)

    Flanders, H.E.

    1995-01-01

    In recent years extensive effort has been expended to qualify existing structures for conditions that are beyond the original design basis. Determination of the component failure load is useful for this type of evaluation. This paper presents criteria based upon strain limits to predict the load at failure. The failure modes addressed are excessive plastic deformations, localized plastic strains, and structural instability. The effects of analytical method sophistication, as built configurations, material properties degradation, and stress state are addressed by the criteria

  15. Predictive Manufacturing: A Classification Strategy to Predict Product Failures

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Kulahci, Murat

    2018-01-01

    manufacturing analytics model that employs a big data approach to predicting product failures; third, we illustrate the issue of high dimensionality, along with statistically redundant information; and, finally, our proposed method will be compared against the well-known classification methods (SVM, K......-nearest neighbor, artificial neural networks). The results from real data show that our predictive manufacturing analytics approach, using genetic algorithms and Voronoi tessellations, is capable of predicting product failure with reasonable accuracy. The potential application of this method contributes...... to accurately predicting product failures, which would enable manufacturers to reduce production costs without compromising product quality....

  16. Uncertainties in container failure time predictions

    International Nuclear Information System (INIS)

    Williford, R.E.

    1990-01-01

    Stochastic variations in the local chemical environment of a geologic waste repository can cause corresponding variations in container corrosion rates and failure times, and thus in radionuclide release rates. This paper addresses how well the future variations in repository chemistries must be known in order to predict container failure times that are bounded by a finite time period within the repository lifetime. Preliminary results indicate that a 5000 year scatter in predicted container failure times requires that repository chemistries be known to within ±10% over the repository lifetime. These are small uncertainties compared to current estimates. 9 refs., 3 figs

  17. Biochemical parameters in chronic renal failure.

    Science.gov (United States)

    Hakim, R M; Lazarus, J M

    1988-03-01

    We analyzed biochemical data derived from 911 patients with renal insufficiency observed at our institution for periods up to 7 years. During early renal failure (RF) (creatinine less than 5 mg/dL), the rate of change of hematocrit, total CO2 (tCO2) and urea per unit change of creatinine was significantly higher than during moderate (creatinine between 5 and 10 mg/dL) or advanced (creatinine greater than 10 mg/dL) RF. For example, the rate of change of hematocrit (%, volume/volume [v/v]) was (mean +/- SEM) -2.15 +/- 0.15% for each 1 mg/dL increase in creatinine in the range of creatinine less than 5 mg/dL, whereas for the range of creatinine greater than 10 mg/dL, the rate of change was only -0.48 +/- 0.06% (P less than 0.001). Similarly, the rate of change of tCO2 was -1.68 +/- 0.09 mEq/L for each 1 mg/dL increment in creatinine concentration during early RF, and -0.19 +/- 0.09 mEq/L per unit increase in creatinine during advanced RF (P less than 0.001). Chloride concentration initially increased as a function of creatinine in early RF, but decreased in advanced RF, whereas the anion gap increased throughout the course of RF. Mean serum phosphate concentration also increased steadily, but remained below the upper range of normal (4.7 mg/dL) during early RF without the use of phosphate binders. These data suggest that different biochemical parameters change at different rates as a function of the severity of renal dysfunction, and that although phosphate retention may occur, hyperphosphatemia is not a hallmark of early RF.

  18. The prediction problems of VVER fuel element cladding failure theory

    International Nuclear Information System (INIS)

    Pelykh, S.N.; Maksimov, M.V.; Ryabchikov, S.D.

    2016-01-01

    Highlights: • Fuel cladding failure forecasting is based on the fuel load history and the damage distribution. • The limit damage parameter is exceeded, though limit stresses are not reached. • The damage parameter plays a significant role in predicting the cladding failure. • The proposed failure probability criterion can be used to control the cladding tightness. - Abstract: A method for forecasting of VVER fuel element (FE) cladding failure due to accumulation of deformation damage parameter, taking into account the fuel assembly (FA) loading history and the damage parameter distribution among FEs included in the FA, has been developed. Using the concept of conservative FE groups, it is shown that the safety limit for damage parameter is exceeded for some FA rearrangement, though the limits for circumferential and equivalent stresses are not reached. This new result contradicts the wide-spread idea that the damage parameter value plays a minor role when estimating the limiting state of cladding. The necessary condition of rearrangement algorithm admissibility and the criterion for minimization of the probability of cladding failure due to damage parameter accumulation have been derived, for using in automated systems controlling the cladding tightness.

  19. BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION

    Directory of Open Access Journals (Sweden)

    Taha Zaghdoudi

    2013-04-01

    Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.

  20. Application of Machine Learning for Dragline Failure Prediction

    Directory of Open Access Journals (Sweden)

    Taghizadeh Amir

    2017-01-01

    Full Text Available Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines’ unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i implementation of K-Nearest Neighbors, ii implementation of Multi-Layer Perceptron, and iii implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines’ failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K- Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines’ reliability subject for first time.

  1. Methods, apparatus and system for notification of predictable memory failure

    Energy Technology Data Exchange (ETDEWEB)

    Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-01-03

    A method for providing notification of a predictable memory failure includes the steps of: obtaining information regarding at least one condition associated with a memory; calculating a memory failure probability as a function of the obtained information; calculating a failure probability threshold; and generating a signal when the memory failure probability exceeds the failure probability threshold, the signal being indicative of a predicted future memory failure.

  2. Parameter prediction for microwave garnets

    International Nuclear Information System (INIS)

    Ramer, R.

    1996-01-01

    Full text: Linearity of the microwave parameters (resonance linewidth ΔH and effective linewidth ΔH eff ) is demonstrated and their use in the Computer-aided design (CAD)/Computer-aided manufacturing (CAM) of new microwave garnets is proposed. Such an approach would combine a numerical database of microwave data and several computational programs. The model is an applied formulation of the analysis of a wide range of microwave garnets

  3. Failure analysis of parameter-induced simulation crashes in climate models

    Science.gov (United States)

    Lucas, D. D.; Klein, R.; Tannahill, J.; Ivanova, D.; Brandon, S.; Domyancic, D.; Zhang, Y.

    2013-08-01

    Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.

  4. Estimation of common cause failure parameters for diesel generators

    International Nuclear Information System (INIS)

    Tirira, J.; Lanore, J.M.

    2002-10-01

    This paper presents a summary of some results concerning the feedback analysis of French Emergency diesel generator (EDG). The database of common cause failure for EDG has been updated. The data collected covers a period of 10 years. Several latent common cause failure (CCF) events counting in tens are identified. In fact, in this number of events collected, most are potential CCF. From events identified, 15% events are characterized as complete CCF. The database is organised following the structure proposed by 'International Common Cause Data Exchange' (ICDE project). Events collected are analyzed by failure mode and degree of failure. Qualitative analysis of root causes, coupling factors and corrective actions are studied. The exercise of quantitative analysis is in progress for evaluating CCF parameters taking into account the average impact vector and the rate of the independent failures. The interest of the average impact vector approach is that it makes it possible to take into account a wide experience feedback, not limited to complete CCF but including also many events related to partial or potential CCF. It has to be noted that there are no finalized quantitative conclusions yet to be drawn and analysis is in progress for evaluating diesel CCF parameters. In fact, the numerical coding CCF representation of the events uses a part of subjective analysis, which requests a complete and detailed event examination. (authors)

  5. Prediction of failure modes for concrete nuclear-containment buildings

    International Nuclear Information System (INIS)

    Butler, T.A.

    1982-01-01

    The failure modes and associated failure pressures for two common generic types of PWR containments are predicted. One building type is a lightly reinforced, posttensioned structure represented by the Zion nuclear reactor containment. The other is the normally reinforced Indian Point containment. Two-dimensional models of the buildings developed using the finite element method are used to predict the failure modes and failure pressures. Predicted failure modes for both containments involve loss of structural integrity at the intersection of the cylindrical sidewall with the base slab

  6. Prediction of failures in linear systems with the use of tolerance ranges

    International Nuclear Information System (INIS)

    Gadzhiev, Ch.M.

    1993-01-01

    The problem of predicting the technical state of an object can be stated in a general case as that of predicting potential failures on the basis of a quantitative evaluation of the predicted parameters in relation to the set of tolerances on these parameters. The main stages in the prediction are collecting and preparing source data on the prehistory of the predicted phenomenon, forming a mathematical model of this phenomenon, working out the algorithm for the prediction, and adopting a solution from the prediction results. The final two stages of prediction are considered in this article. The prediction algorithm is proposed based on construction of the tolerance range for the signal of error between output coordinates of the system and its mathematical model. A solution regarding possible occurrence of failure in the system is formulated as a result of comparison of the tolerance range and the found confidence interval. 5 refs

  7. [Prediction of mortality in patients with acute hepatic failure].

    Science.gov (United States)

    Eremeeva, L F; Berdnikov, A P; Musaeva, T S; Zabolotskikh, I B

    2013-01-01

    The article deals with a study of 243 patients (from 18 to 65 years old) with acute hepatic failure. Purpose of the study was to evaluate the predictive capability of severity scales APACHE III, SOFA, MODS, Child-Pugh and to identify mortality predictors in patients with acute hepatic failure. Results; The best predictive ability in patients with acute hepatic failure and multiple organ failure had APACHE III and SOFA scales. The strongest mortality predictors were: serum creatinine > 132 mmol/L, fibrinogen < 1.4 g/L, Na < 129 mmol/L.

  8. Estimation of common cause failure parameters with periodic tests

    Energy Technology Data Exchange (ETDEWEB)

    Barros, Anne [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France)], E-mail: anne.barros@utt.fr; Grall, Antoine [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France); Vasseur, Dominique [Electricite de France, EDF R and D - Industrial Risk Management Department 1, av. du General de Gaulle- 92141 Clamart (France)

    2009-04-15

    In the specific case of safety systems, CCF parameters estimators for standby components depend on the periodic test schemes. Classically, the testing schemes are either staggered (alternation of tests on redundant components) or non-staggered (all components are tested at the same time). In reality, periodic tests schemes performed on safety components are more complex and combine staggered tests, when the plant is in operation, to non-staggered tests during maintenance and refueling outage periods of the installation. Moreover, the CCF parameters estimators described in the US literature are derived in a consistent way with US Technical Specifications constraints that do not apply on the French Nuclear Power Plants for staggered tests on standby components. Given these issues, the evaluation of CCF parameters from the operating feedback data available within EDF implies the development of methodologies that integrate the testing schemes specificities. This paper aims to formally propose a solution for the estimation of CCF parameters given two distinct difficulties respectively related to a mixed testing scheme and to the consistency with EDF's specific practices inducing systematic non-simultaneity of the observed failures in a staggered testing scheme.

  9. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    Science.gov (United States)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  10. Bayesian state prediction of wind turbine bearing failure

    DEFF Research Database (Denmark)

    Herp, Jürgen; Ramezani, Mohammad H.; Bach-Andersen, Martin

    2017-01-01

    A statistical approach to abstract and predict turbine states in an online manner has been developed. Online inference is performed on temperature measurement residuals to predict the failure state δn steps ahead of time. In this framework a case study is performed showing the ability to predict...

  11. Predicting mortality in patients with heart failure : a pragmatic approach

    NARCIS (Netherlands)

    Bouvy, ML; Heerdink, ER; Leufkens, HGM; Hoes, AW

    Objective: To develop a comprehensive and easily applicable prognostic model predicting mortality risk in patients with moderate to severe heart failure. Design: Prospective follow up study. Setting: Seven general hospitals in the Netherlands. Patients: 152 outpatients with heart failure or patients

  12. Predictors of incident heart failure in patients after an acute coronary syndrome: The LIPID heart failure risk-prediction model.

    Science.gov (United States)

    Driscoll, Andrea; Barnes, Elizabeth H; Blankenberg, Stefan; Colquhoun, David M; Hunt, David; Nestel, Paul J; Stewart, Ralph A; West, Malcolm J; White, Harvey D; Simes, John; Tonkin, Andrew

    2017-12-01

    Coronary heart disease is a major cause of heart failure. Availability of risk-prediction models that include both clinical parameters and biomarkers is limited. We aimed to develop such a model for prediction of incident heart failure. A multivariable risk-factor model was developed for prediction of first occurrence of heart failure death or hospitalization. A simplified risk score was derived that enabled subjects to be grouped into categories of 5-year risk varying from 20%. Among 7101 patients from the LIPID study (84% male), with median age 61years (interquartile range 55-67years), 558 (8%) died or were hospitalized because of heart failure. Older age, history of claudication or diabetes mellitus, body mass index>30kg/m 2 , LDL-cholesterol >2.5mmol/L, heart rate>70 beats/min, white blood cell count, and the nature of the qualifying acute coronary syndrome (myocardial infarction or unstable angina) were associated with an increase in heart failure events. Coronary revascularization was associated with a lower event rate. Incident heart failure increased with higher concentrations of B-type natriuretic peptide >50ng/L, cystatin C>0.93nmol/L, D-dimer >273nmol/L, high-sensitivity C-reactive protein >4.8nmol/L, and sensitive troponin I>0.018μg/L. Addition of biomarkers to the clinical risk model improved the model's C statistic from 0.73 to 0.77. The net reclassification improvement incorporating biomarkers into the clinical model using categories of 5-year risk was 23%. Adding a multibiomarker panel to conventional parameters markedly improved discrimination and risk classification for future heart failure events. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  13. Review on failure prediction techniques of composite single lap joint

    International Nuclear Information System (INIS)

    Ab Ghani, A.F.; Rivai, Ahmad

    2016-01-01

    Adhesive bonding is the most appropriate joining method in construction of composite structures. The use of reliable design and prediction technique will produce better performance of bonded joints. Several papers from recent papers and journals have been reviewed and synthesized to understand the current state of the art in this area. It is done by studying the most relevant analytical solutions for composite adherends with start of reviewing the most fundamental ones involving beam/plate theory. It is then extended to review single lap joint non linearity and failure prediction and finally on the failure prediction on composite single lap joint. The review also encompasses the finite element modelling part as tool to predict the elastic response of composite single lap joint and failure prediction numerically.

  14. Review on failure prediction techniques of composite single lap joint

    Energy Technology Data Exchange (ETDEWEB)

    Ab Ghani, A.F., E-mail: ahmadfuad@utem.edu.my; Rivai, Ahmad, E-mail: ahmadrivai@utem.edu.my [Faculty of Mechanical Engineering, Locked Bag 1200, Hang Tuah Jaya, 75450 Ayer Keroh, Melaka (Malaysia)

    2016-03-29

    Adhesive bonding is the most appropriate joining method in construction of composite structures. The use of reliable design and prediction technique will produce better performance of bonded joints. Several papers from recent papers and journals have been reviewed and synthesized to understand the current state of the art in this area. It is done by studying the most relevant analytical solutions for composite adherends with start of reviewing the most fundamental ones involving beam/plate theory. It is then extended to review single lap joint non linearity and failure prediction and finally on the failure prediction on composite single lap joint. The review also encompasses the finite element modelling part as tool to predict the elastic response of composite single lap joint and failure prediction numerically.

  15. Optimal design criteria - prediction vs. parameter estimation

    Science.gov (United States)

    Waldl, Helmut

    2014-05-01

    G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

  16. Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure

    NARCIS (Netherlands)

    Ouwerkerk, Wouter; Voors, Adriaan A.; Zwinderman, Aeilko H.

    2014-01-01

    The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure

  17. Evolutionary neural network modeling for software cumulative failure time prediction

    International Nuclear Information System (INIS)

    Tian Liang; Noore, Afzel

    2005-01-01

    An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches

  18. Performance of immunological response in predicting virological failure.

    Science.gov (United States)

    Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

    2013-03-01

    In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

  19. Failure mitigation in software defined networking employing load type prediction

    KAUST Repository

    Bouacida, Nader

    2017-07-31

    The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.

  20. Weibull Parameters Estimation Based on Physics of Failure Model

    DEFF Research Database (Denmark)

    Kostandyan, Erik; Sørensen, John Dalsgaard

    2012-01-01

    Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...

  1. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    NARCIS (Netherlands)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A. M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of

  2. Brittle Creep Failure, Critical Behavior, and Time-to-Failure Prediction of Concrete under Uniaxial Compression

    Directory of Open Access Journals (Sweden)

    Yingchong Wang

    2015-01-01

    Full Text Available Understanding the time-dependent brittle deformation behavior of concrete as a main building material is fundamental for the lifetime prediction and engineering design. Herein, we present the experimental measures of brittle creep failure, critical behavior, and the dependence of time-to-failure, on the secondary creep rate of concrete under sustained uniaxial compression. A complete evolution process of creep failure is achieved. Three typical creep stages are observed, including the primary (decelerating, secondary (steady state creep regime, and tertiary creep (accelerating creep stages. The time-to-failure shows sample-specificity although all samples exhibit a similar creep process. All specimens exhibit a critical power-law behavior with an exponent of −0.51 ± 0.06, approximately equal to the theoretical value of −1/2. All samples have a long-term secondary stage characterized by a constant strain rate that dominates the lifetime of a sample. The average creep rate expressed by the total creep strain over the lifetime (tf-t0 for each specimen shows a power-law dependence on the secondary creep rate with an exponent of −1. This could provide a clue to the prediction of the time-to-failure of concrete, based on the monitoring of the creep behavior at the steady stage.

  3. Calculation of parameter failure probability of thermodynamic system by response surface and importance sampling method

    International Nuclear Information System (INIS)

    Shang Yanlong; Cai Qi; Chen Lisheng; Zhang Yangwei

    2012-01-01

    In this paper, the combined method of response surface and importance sampling was applied for calculation of parameter failure probability of the thermodynamic system. The mathematics model was present for the parameter failure of physics process in the thermodynamic system, by which the combination arithmetic model of response surface and importance sampling was established, then the performance degradation model of the components and the simulation process of parameter failure in the physics process of thermodynamic system were also present. The parameter failure probability of the purification water system in nuclear reactor was obtained by the combination method. The results show that the combination method is an effective method for the calculation of the parameter failure probability of the thermodynamic system with high dimensionality and non-linear characteristics, because of the satisfactory precision with less computing time than the direct sampling method and the drawbacks of response surface method. (authors)

  4. A physical probabilistic model to predict failure rates in buried PVC pipelines

    International Nuclear Information System (INIS)

    Davis, P.; Burn, S.; Moglia, M.; Gould, S.

    2007-01-01

    For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical

  5. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    Science.gov (United States)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A.M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Results Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Conclusion Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions. PMID:25287823

  6. Predicting the Failure of Dental Implants Using Supervised Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chia-Hui Liu

    2018-05-01

    Full Text Available Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients’ body conditions, surgeons’ experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients’ systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT, support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741; the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

  7. Early Shear Failure Prediction in Incremental Sheet Forming Process Using FEM and ANN

    Science.gov (United States)

    Moayedfar, Majid; Hanaei, Hengameh; Majdi Rani, Ahmad; Musa, Mohd Azam Bin; Sadegh Momeni, Mohammad

    2018-03-01

    The application of incremental sheet forming process as a rapid forming technique is rising in variety of industries such as aerospace, automotive and biomechanical purposes. However, the sheet failure is a big challenge in this process which leads wasting lots of materials. Hence, this study tried to propose a method to predict the early sheet failure in this process using mathematical solution. For the feasibility of the study, design of experiment with the respond surface method is employed to extract a set of experiments data for the simulation. The significant forming parameters were recognized and their integration was used for prediction system. Then, the results were inserted to the artificial neural network as input parameters to predict a vast range of applicable parameters avoiding sheet failure in ISF. The value of accuracy R2 ∼0.93 was obtained and the maximum sheet stretch in the depth of 25mm were recorded. The figures generate from the trend of interaction between effective parameters were provided for future studies.

  8. Prediction of hospital failure: a post-PPS analysis.

    Science.gov (United States)

    Gardiner, L R; Oswald, S L; Jahera, J S

    1996-01-01

    This study investigates the ability of discriminant analysis to provide accurate predictions of hospital failure. Using data from the period following the introduction of the Prospective Payment System, we developed discriminant functions for each of two hospital ownership categories: not-for-profit and proprietary. The resulting discriminant models contain six and seven variables, respectively. For each ownership category, the variables represent four major aspects of financial health (liquidity, leverage, profitability, and efficiency) plus county marketshare and length of stay. The proportion of closed hospitals misclassified as open one year before closure does not exceed 0.05 for either ownership type. Our results show that discriminant functions based on a small set of financial and nonfinancial variables provide the capability to predict hospital failure reliably for both not-for-profit and proprietary hospitals.

  9. Study on real-time elevator brake failure predictive system

    Science.gov (United States)

    Guo, Jun; Fan, Jinwei

    2013-10-01

    This paper presented a real-time failure predictive system of the elevator brake. Through inspecting the running state of the coil by a high precision long range laser triangulation non-contact measurement sensor, the displacement curve of the coil is gathered without interfering the original system. By analyzing the displacement data using the diagnostic algorithm, the hidden danger of the brake system can be discovered in time and thus avoid the according accident.

  10. Heart rate variability enhances the prognostic value of established parameters in patients with congestive heart failure.

    Science.gov (United States)

    Krüger, C; Lahm, T; Zugck, C; Kell, R; Schellberg, D; Schweizer, M W F; Kübler, W; Haass, M

    2002-12-01

    This prospective study evaluated whether heart rate variability (HRV) assessed from Holter ECG has prognostic value in addition to established parameters in patients with congestive heart failure (CHF). The study included 222 patients with CHF due to dilated or ischemic cardiomyopathy (left ventricular ejection fraction LVEF 21+/-1%; mean+/-SEM). During a mean follow-up of 15+/-1 months, 38 (17%) patients died and 45 (20%) were hospitalized due to worsening of CHF. The HRV parameter SDNN (standard deviation of all intervals between normal beats) was significantly lower in non-surviving or hospitalized than in event-free patients (118+/-6 vs 142+/-5 ms), as were LVEF (18+/-1 vs 23+/-1%), and peak oxygen uptake during exercise (peak VO(2)) (12.8+/-0.5 vs 15.6+/-0.5 ml/min/kg). While each of these parameters was a risk predictor in univariate analysis, multivariate analysis revealed that HRV provides both independent and additional prognostic information with respect to the risk 'cardiac mortality or deterioration of CHF'. It is concluded that the determination of HRV enhances the prognostic power given by the most widely used parameters LVEF and peak VO(2) in the prediction of mortality or deterioration of CHF and thus enables to improve risk stratification.

  11. Microproteinuria Predicts Organ Failure in Patients Presenting with Acute Pancreatitis

    DEFF Research Database (Denmark)

    Bertilsson, Sara; Swärd, Per; Håkansson, Anders

    2016-01-01

    patients were included (14 % with organ failure; 6 % with severe AP). The α1-microglobulin-, albumin-, and IgG/creatinine ratios correlated with high-sensitivity C-reactive protein 48 h after admission (r = 0.47–0.61, p .... Urine samples were collected upon admission, 12–24 h after admission, and 3 months post-discharge for calculation of urine α1-microglobulin-, albumin-, IgG-, and IgM/creatinine ratios. Data regarding AP etiology, severity, and development of organ failure were registered. Results: Overall, 92 AP...... organ failure (p creatinine ratio upon admission predicted organ failure [adjusted odds ratio 1.286, 95 % confidence interval (CI) 1.024–1.614] with similar accuracy (AUROC 0.81, 95 % CI 0.69–0.94) as the more complex APACHE II score (AUROC 0.86, 95 % CI 0...

  12. Predicting Plan Failure by Monitoring Action Sequences and Duration

    Directory of Open Access Journals (Sweden)

    Giovani Parente FARIAS

    2017-07-01

    Full Text Available An agent can attempt to achieve multiple goals and each goal can be achieved by applying various different plans. Anticipating failures in agent plan execution is important to enable an agent to develop strategies to avoid or circumvent such failures, allowing the agent to achieve its goal. Plan recognition can be used to infer which plans are being executed from observations of sequences of activities being performed by an agent. Symbolic Plan Recognition is an algorithm that represents knowledge about the agents under observation in the form of a plan library. In this work, we use this symbolic algorithm to find out which plan the agent is performing and we develop a failure prediction system, based on information available in the plan library and in a simplified calendar which manages the goals the agent has to achieve. This failure predictor is able to monitor the sequence of agent actions and detects if an action is taking too long or does not match the plan that the agent was expected to be performing. We have successfully employed this approach in a health-care prototype system.

  13. Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

    Science.gov (United States)

    Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

    2009-09-01

    Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P advanced HF.

  14. Dst Prediction Based on Solar Wind Parameters

    Directory of Open Access Journals (Sweden)

    Yoon-Kyung Park

    2009-12-01

    Full Text Available We reevaluate the Burton equation (Burton et al. 1975 of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q and the decay time (tau of the equation, we examine the relationships between Dst* and VB_s, Delta Dst* and VB_s, and Delta Dst* and Dst* during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to the solar wind forcing. The injection term is found to be Q({nT}/h=-3.56VB_s for VB_s>0.5mV/m and Q({nT}/h=0 for VB_s leq0.5mV/m. The tau (hour is estimated as 0.060 Dst* + 16.65 for Dst*>-175nT and 6.15 hours for Dst* leq -175nT. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted Dst* is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975 and O'Brien & McPherron (2000a. The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms (Dst* lesssim -200nT.

  15. Prediction of room acoustical parameters (A)

    DEFF Research Database (Denmark)

    Gade, Anders Christian

    1991-01-01

    -averaged acoustical data. The results are presented in the form of linear, multiple regression formulas that may be used to predict the values of the newer measures of level, clarity, spaciousness, and musicians' conditions on the orchestra platform in halls with given RT and geometry....

  16. System Predicts Critical Runway Performance Parameters

    Science.gov (United States)

    Millen, Ernest W.; Person, Lee H., Jr.

    1990-01-01

    Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

  17. Biochemical and neurophysiological parameters in hemodialyzed patients with chronic renal failure

    NARCIS (Netherlands)

    Schoots, A.C.; Vries, de P.M.J.M.; Thiemann, R.C.J.; Hazejager, W.A.; Visser, S.L.; Oe, P.L.

    1989-01-01

    Serum concentrations of accumulated solutes, standard clinical biochemistry, and parameters of clinical neuropathy, were determined in hemodialyzed patients with chronic renal failure. Analyses by high-performance liquid chromatography included creatinine, pseudouridine, urate, p-hydroxyhippuric

  18. Prediction of failure strain and burst pressure in high yield-to-tensile strength ratio linepipe

    International Nuclear Information System (INIS)

    Law, M.; Bowie, G.

    2007-01-01

    Failure pressures and strains were predicted for a number of burst tests as part of a project to explore failure strain in high yield-to-tensile strength ratio linepipe. Twenty-three methods for predicting the burst pressure and six methods of predicting the failure strain are compared with test results. Several methods were identified which gave accurate and reliable estimates of burst pressure. No method of accurately predicting the failure strain was found, though the best was noted

  19. Prediction of failure strain and burst pressure in high yield-to-tensile strength ratio linepipe

    Energy Technology Data Exchange (ETDEWEB)

    Law, M. [Institute of Materials and Engineering Science, Australian Nuclear Science and Technology Organisation (ANSTO), Lucas Heights, NSW (Australia)]. E-mail: mlx@ansto.gov.au; Bowie, G. [BlueScope Steel Ltd., Level 11, 120 Collins St, Melbourne, Victoria 3000 (Australia)

    2007-08-15

    Failure pressures and strains were predicted for a number of burst tests as part of a project to explore failure strain in high yield-to-tensile strength ratio linepipe. Twenty-three methods for predicting the burst pressure and six methods of predicting the failure strain are compared with test results. Several methods were identified which gave accurate and reliable estimates of burst pressure. No method of accurately predicting the failure strain was found, though the best was noted.

  20. Prediction of Brittle Failure for TBM Tunnels in Anisotropic Rock: A Case Study from Northern Norway

    Science.gov (United States)

    Dammyr, Øyvind

    2016-06-01

    Prediction of spalling and rock burst is especially important for hard rock TBM tunneling, because failure can have larger impact than in a drill and blast tunnel and ultimately threaten excavation feasibility. The majority of research on brittle failure has focused on rock types with isotropic behavior. This paper gives a review of existing theory and its application before a 3.5-m-diameter TBM tunnel in foliated granitic gneiss is used as a case to study brittle failure characteristics of anisotropic rock. Important aspects that should be considered in order to predict brittle failure in anisotropic rock are highlighted. Foliation is responsible for considerable strength anisotropy and is believed to influence the preferred side of v-shaped notch development in the investigated tunnel. Prediction methods such as the semi- empirical criterion, the Hoek- Brown brittle parameters, and the non-linear damage initiation and spalling limit method give reliable results; but only as long as the angle between compression axis and foliation in uniaxial compressive tests is relevant, dependent on the relation between tunnel trend/plunge, strike/dip of foliation, and tunnel boundary stresses. It is further demonstrated that local in situ stress variations, for example, due to the presence of discontinuities, can have profound impact on failure predictions. Other carefully documented case studies into the brittle failure nature of rock, in particular anisotropic rock, are encouraged in order to expand the existing and relatively small database. This will be valuable for future TBM planning and construction stages in highly stressed brittle anisotropic rock.

  1. Clinical presentation at first heart failure hospitalization does not predict recurrent heart failure admission.

    Science.gov (United States)

    Kosztin, Annamaria; Costa, Jason; Moss, Arthur J; Biton, Yitschak; Nagy, Vivien Klaudia; Solomon, Scott D; Geller, Laszlo; McNitt, Scott; Polonsky, Bronislava; Merkely, Bela; Kutyifa, Valentina

    2017-11-01

    There are limited data on whether clinical presentation at first heart failure (HF) hospitalization predicts recurrent HF events. We aimed to assess predictors of recurrent HF hospitalizations in mild HF patients with an implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator. Data on HF hospitalizations were prospectively collected for patients enrolled in MADIT-CRT. Predictors of recurrent HF hospitalization (HF2) after the first HF hospitalization were assessed using Cox proportional hazards regression models including baseline covariates and clinical presentation or management at first HF hospitalization. There were 193 patients with first HF hospitalization, and 156 patients with recurrent HF events. Recurrent HF rate after the first HF hospitalization was 43% at 1 year, 52% at 2 years, and 55% at 2.5 years. Clinical signs and symptoms, medical treatment, or clinical management of HF at first HF admission was not predictive for HF2. Baseline covariates predicting recurrent HF hospitalization included prior HF hospitalization (HR = 1.59, 95% CI: 1.15-2.20, P = 0.005), digitalis therapy (HR = 1.58, 95% CI: 1.13-2.20, P = 0.008), and left ventricular end-diastolic volume >240 mL (HR = 1.62, 95% CI: 1.17-2.25, P = 0.004). Recurrent HF events are frequent following the first HF hospitalization in patients with implanted implantable cardioverter defibrillator or cardiac resynchronization therapy with defibrillator. Neither clinical presentation nor clinical management during first HF admission was predictive of recurrent HF. Prior HF hospitalization, digitalis therapy, and left ventricular end-diastolic volume at enrolment predicted recurrent HF hospitalization, and these covariates could be used as surrogate markers for identifying a high-risk cohort. © 2017 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology.

  2. Failures and suggestions in Earthquake forecasting and prediction

    Science.gov (United States)

    Sacks, S. I.

    2013-12-01

    Seismologists have had poor success in earthquake prediction. However, wide ranging observations from earlier great earthquakes show that precursory data can exist. In particular, two aspects seem promising. In agreement with simple physical modeling, b-values decrease in highly loaded fault zones for years before failure. Potentially more usefully, in high stress regions, breakdown of dilatant patches leading to failure can yield expelled water-related observations. The volume increase (dilatancy) caused by high shear stresses decreases the pore pressure. Eventually, water flows back in restoring the pore pressure, promoting failure and expelling the extra water. Of course, in a generally stressed region there may be many small patches that fail, such as observed before the 1975 Haicheng earthquake. Only a few days before the major event will most of the dilatancy breakdown occur in the fault zone itself such as for the Tangshan, 1976 destructive event. Observations of 'water release' effects have been observed before the 1923 great Kanto earthquake, the 1984 Yamasaki event, the 1975 Haicheng and the 1976 Tangshan earthquakes and also the 1995 Kobe earthquake. While there are obvious difficulties in water release observations, not least because there is currently no observational network anywhere, historical data does suggest some promise if we broaden our approach to this difficult subject.

  3. Factors predicting the outcome of acute renal failure in pregnancy

    International Nuclear Information System (INIS)

    Khana, N.; Akhtar, F.

    2010-01-01

    To determine the factors predicting renal outcome in patients developing acute renal failure in pregnancy. Study Design: Descriptive cohort study. Place and Duration of Study: Study was conducted at Nephrology Unit of Sindh Institute of Urology and Transplantation, Karachi, from October 2006 to March 2007. Methodology: Patients with acute renal failure due to complications of pregnancy, with normal size of both the kidneys on ultrasound were enrolled, and followed for a period of 60 days or until recovery of renal function. Patient's age and parity, presence of antenatal care, type of complication of pregnancy, foetal outcome and duration of oliguria were compared between patients who remained dialysis dependent and those who recovered renal function. Chi-square/Fisher's exact test and student's t-test, were used for determining the association of categorical and continuous variables with dialysis dependency. Results: The mean age was 29 +- 6 years. Most patients came from rural areas of interior Sindh. Sixty eight percent did not have antenatal checkups. Antepartum haemorrhage (p=0.002) and prolonged duration of oliguria (35 +- 15.7 days, p= < 0.001) were associated with dialysis dependency, which was observed in 50% of the study group. Conclusion: Ante-partum haemorrhage and prolonged oliguria were strong predictors of irreversible renal failure. This highlights the need for early recognition and referral, and the importance of trained birth attendants and antenatal care. (author)

  4. Rational temporal predictions can underlie apparent failures to delay gratification

    Science.gov (United States)

    McGuire, Joseph T.; Kable, Joseph W.

    2013-01-01

    An important category of seemingly maladaptive decisions involves failure to postpone gratification. A person pursuing a desirable long-run outcome may abandon it in favor of a short-run alternative that has been available all along. Here we present a theoretical framework in which this seemingly irrational behavior emerges from stable preferences and veridical judgments. Our account recognizes that decision makers generally face uncertainty regarding the time at which future outcomes will materialize. When timing is uncertain, the value of persistence depends crucially on the nature of a decision-maker’s prior temporal beliefs. Certain forms of temporal beliefs imply that a delay’s predicted remaining length increases as a function of time already waited. In this type of situation, the rational, utility-maximizing strategy is to persist for a limited amount of time and then give up. We show empirically that people’s explicit predictions of remaining delay lengths indeed increase as a function of elapsed time in several relevant domains, implying that temporal judgments offer a rational basis for limiting persistence. We then develop our framework into a simple working model and show how it accounts for individual differences in a laboratory task (the well-known “marshmallow test”). We conclude that delay-of-gratification failure, generally viewed as a manifestation of limited self-control capacity, can instead arise as an adaptive response to the perceived statistics of one’s environment. PMID:23458085

  5. Prediction of the time-dependent failure rate for normally operating components taking into account the operational history

    International Nuclear Information System (INIS)

    Vrbanic, I.; Simic, Z.; Sljivac, D.

    2008-01-01

    The prediction of the time-dependent failure rate has been studied, taking into account the operational history of a component used in applications such as system modeling in a probabilistic safety analysis in order to evaluate the impact of equipment aging and maintenance strategies on the risk measures considered. We have selected a time-dependent model for the failure rate which is based on the Weibull distribution and the principles of proportional age reduction by equipment overhauls. Estimation of the parameters that determine the failure rate is considered, including the definition of the operational history model and likelihood function for the Bayesian analysis of parameters for normally operating repairable components. The operational history is provided as a time axis with defined times of overhauls and failures. An example for demonstration is described with prediction of the future behavior for seven different operational histories. (orig.)

  6. A New Material Constitutive Model for Predicting Cladding Failure

    Energy Technology Data Exchange (ETDEWEB)

    Rashid, Joe; Dunham, Robert [ANATECH Corp., San Diego, CA (United States); Rashid, Mark [University of California Davis, Davis, CA (United States); Machiels, Albert [EPRI, Palo Alto, CA (United States)

    2009-06-15

    An important issue in fuel performance and safety evaluations is the characterization of the effects of hydrides on cladding mechanical response and failure behavior. The hydride structure formed during power operation transforms the cladding into a complex multi-material composite, with through-thickness concentration profile that causes cladding ductility to vary by more than an order of magnitude between ID and OD. However, current practice of mechanical property testing treats the cladding as a homogeneous material characterized by a single stress-strain curve, regardless of its hydride morphology. Consequently, as irradiation conditions and hydrides evolution change, new material property testing is required, which results in a state of continuous need for valid material property data. A recently developed constitutive model, treats the cladding as a multi-material composite in which the metal and the hydride platelets are treated as separate material phases with their own elastic-plastic and fracture properties and interacting at their interfaces with appropriate constraint conditions between them to ensure strain and stress compatibility. An essential feature of the model is a multi-phase damage formulation that models the complex interaction between the hydride phases and the metal matrix and the coupled effect of radial and circumferential hydrides on cladding stress-strain response. This gives the model the capability of directly predicting cladding failure progression during the loading event and, as such, provides a unique tool for constructing failure criteria analytically where none could be developed by conventional material testing. Implementation of the model in a fuel behavior code provides the capability to predict in-reactor operational failures due to PCI or missing pellet surfaces (MPS) without having to rely on failure criteria. Even, a stronger motivation for use of the model is in the transportation accidents analysis of spent fuel

  7. Failure analysis for ultrasound machines in a radiology department after implementation of predictive maintenance method

    Directory of Open Access Journals (Sweden)

    Greg Chu

    2018-01-01

    Full Text Available Objective: The objective of the study was to perform quantitative failure and fault analysis to the diagnostic ultrasound (US scanners in a radiology department after the implementation of the predictive maintenance (PdM method; to study the reduction trend of machine failure; to understand machine operating parameters affecting the failure; to further optimize the method to maximize the machine clinically service time. Materials and Methods: The PdM method has been implemented to the 5 US machines since 2013. Log books were used to record machine failures and their root causes together with the time spent on repair, all of which were retrieved, categorized, and analyzed for the period between 2013 and 2016. Results: There were a total of 108 cases of failure occurred in these 5 US machines during the 4-year study period. The average number of failure per month for all these machines was 2.4. Failure analysis showed that there were 33 cases (30.5% due to software, 44 cases (40.7% due to hardware, and 31 cases (28.7% due to US probe. There was a statistically significant negative correlation between the time spent on regular quality assurance (QA by hospital physicists with the time spent on faulty parts replacement over the study period (P = 0.007. However, there was no statistically significant correlation between regular QA time and total yearly breakdown case (P = 0.12, although there has been a decreasing trend observed in the yearly total breakdown. Conclusion: There has been a significant improvement on the machine failure of US machines attributed to the concerted effort of sonographers and physicists in our department to practice the PdM method, in that system component repair time has been reduced, and a decreasing trend in the number of system breakdown has been observed.

  8. GRACE score predicts heart failure admission following acute coronary syndrome.

    Science.gov (United States)

    McAllister, David A; Halbesma, Nynke; Carruthers, Kathryn; Denvir, Martin; Fox, Keith A

    2015-04-01

    Congestive heart failure (CHF) is a common and preventable complication of acute coronary syndrome (ACS). Nevertheless, ACS risk scores have not been shown to predict CHF risk. We investigated whether the at-discharge Global Registry of Acute Coronary Events (GRACE) score predicts heart failure admission following ACS. Five-year mortality and hospitalization data were obtained for patients admitted with ACS from June 1999 to September 2009 to a single centre of the GRACE registry. CHF was defined as any admission assigned WHO International Classification of Diseases 10 diagnostic code I50. The hazard ratio (HR) for CHF according to GRACE score was estimated in Cox models adjusting for age, gender and the presence of CHF on index admission. Among 1,956 patients, CHF was recorded on index admission in 141 patients (7%), and 243 (12%) were admitted with CHF over 3.8 median years of follow-up. Compared to the lowest quintile, patients in the highest GRACE score quintile had more CHF admissions (116 vs 17) and a shorter time to first admission (1.2 vs 2.0 years, HR 9.87, 95% CI 5.93-16.43). Per standard deviation increment in GRACE score, the instantaneous risk was more than two-fold higher (HR 2.28; 95% CI 2.02-2.57), including after adjustment for CHF on index admission, age and gender (HR 2.49; 95% CI 2.06-3.02). The C-statistic for CHF admission at 1-year was 0.74 (95% CI 0.70-0.79). The GRACE score predicts CHF admission, and may therefore be used to target ACS patients at high risk of CHF with clinical monitoring and therapies. © The European Society of Cardiology 2014.

  9. Failure analysis and seal life prediction for contacting mechanical seals

    Science.gov (United States)

    Sun, J. J.; He, X. Y.; Wei, L.; Feng, X.

    2008-11-01

    Fault tree analysis method was applied to quantitatively investigate the causes of the leakage failure of mechanical seals. It is pointed out that the change of the surface topography is the main reasons causing the leakage of mechanical seals under the condition of constant preloads. Based on the fractal geometry theory, the relationship between the surface topography and working time were investigated by experiments, and the effects of unit load acting on seal face on leakage path in a mechanical seal were analyzed. The model of predicting seal life of mechanical seals was established on the basis of the relationship between the surface topography and working time and allowable leakage. The seal life of 108 mechanical seal operating at the system of diesel fuel storage and transportation was predicted and the problem of the condition monitoring for the long-period operation of mechanical seal was discussed by this method. The research results indicate that the method of predicting seal life of mechanical seals is feasible, and also is foundation to make scheduled maintenance time and to achieve safe-reliability and low-cost operation for industrial devices.

  10. COPD predicts mortality in HF: the Norwegian Heart Failure Registry.

    Science.gov (United States)

    De Blois, Jonathan; Simard, Serge; Atar, Dan; Agewall, Stefan

    2010-03-01

    Chronic obstructive pulmonary disease (COPD) and chronic heart failure (HF) are common clinical conditions that share tobacco as a risk factor. Our aim was to evaluate the prognostic impact of COPD on HF patients. The Norwegian Heart Failure Registry was used. The study included 4132 HF patients (COPD, n = 699) from 22 hospitals (mean follow-up, 13.3 months). COPD patients were older, more often smokers and diabetics, less often on beta-blockers and had a higher heart rate. They were more often in New York Heart Association (NYHA) Class III or IV (COPD, 63%; no COPD, 51%), although left ventricular ejection fraction (LVEF) distribution was similar. COPD independently predicted death (adjusted hazard ratio [HR], 1.188; 95% CI: 1.015 to 1.391; P = 0.03) along with age, creatinine, NYHA Class III/IV (HR, 1.464; 95% CI: 1.286 to 1.667) and diabetes. beta-blockers at baseline were associated with improved survival in patients with LVEF < or =40% independently of COPD. COPD is associated with a poorer survival in HF patients. COPD patients are overrated in terms of NYHA class in comparison with patients with similar LVEF. Nonetheless, NYHA class remains the strongest predictor of death in these patients. Copyright (c) 2010 Elsevier Inc. All rights reserved.

  11. Aberrant GSTP1 promoter methylation predicts short-term prognosis in acute-on-chronic hepatitis B liver failure.

    Science.gov (United States)

    Gao, S; Sun, F-K; Fan, Y-C; Shi, C-H; Zhang, Z-H; Wang, L-Y; Wang, K

    2015-08-01

    Glutathione-S-transferase P1 (GSTP1) methylation has been demonstrated to be associated with oxidative stress induced liver damage in acute-on-chronic hepatitis B liver failure (ACHBLF). To evaluate the methylation level of GSTP1 promoter in acute-on-chronic hepatitis B liver failure and determine its predictive value for prognosis. One hundred and five patients with acute-on-chronic hepatitis B liver failure, 86 with chronic hepatitis B (CHB) and 30 healthy controls (HC) were retrospectively enrolled. GSTP1 methylation level in peripheral mononuclear cells (PBMC) was detected by MethyLight. Clinical and laboratory parameters were obtained. GSTP1 methylation levels were significantly higher in patients with acute-on-chronic hepatitis B liver failure (median 16.84%, interquartile range 1.83-59.05%) than those with CHB (median 1.25%, interquartile range 0.48-2.47%; P chronic hepatitis B liver failure group, nonsurvivors showed significantly higher GSTP1 methylation levels (P chronic hepatitis B liver failure, GSTP1 methylation showed significantly better predictive value than MELD score [area under the receiver operating characteristic curve (AUC) 0.89 vs. 0.72, P chronic hepatitis B liver failure and shows high predictive value for short-term mortality. It might serve as a potential prognostic marker for acute-on-chronic hepatitis B liver failure. © 2015 John Wiley & Sons Ltd.

  12. Predicting material failure using mathematics; Mit Mathematik Materialversagen vorhersagen

    Energy Technology Data Exchange (ETDEWEB)

    Keller, Christian [Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin (Germany)

    2016-06-15

    Numerical simulations provide insights into materials, technical procedures or processes that are hardly possible by means of measurement technology, or require a lot of effort. In BAM's ConDrop research project (Numerical Drop Test Analyses of Steel Sheet Containers for the Konrad Repository), scientists are developing a method to predict the deformation and failure behaviour of containers for low- and intermediate- level radioactive waste for the Konrad repository. [German] Numerische Simulationen erlauben Einblicke in Materialien, technische Verfahren oder Prozesse, die mit Mitteln der Messtechnik kaum oder nur unter grossem Aufwand moeglich sind. Im BAM-Forschungsvorhaben ConDrop entwickeln Wissenschaftlerinnen und Wissenschaftler damit eine Methode, um das Verformungs- und Versagensverhalten von Behaeltern fuer schwach- und mittelradioaktive Abfaelle fuer das Endlager Konrad vorherzusagen.

  13. Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

    KAUST Repository

    Sawlan, Zaid A

    2012-01-01

    parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while

  14. Factors predictive of abnormal semen parameters in male partners ...

    African Journals Online (AJOL)

    analysis was used to determine the predictive factors associated with abnormal semen parameters. .... for frequency, mean and χ2 with the level of significance set at p<0.05. ... was obtained from each couple participating in the study, following.

  15. Stress evaluation of baffle former bolt for IASCC failure prediction

    International Nuclear Information System (INIS)

    Matsubara, T.; Tsutsui, T.; Kamei, Y.; Kitsu, M.

    2011-01-01

    Baffle structure in PWRs Reactor is quite important assembly for the core safety, and Baffle Former Bolts (BFBs) are fastener members for maintaining Baffle structure. It has been reported worldwide that some of BFBs were cracked due to IASCC (Irradiation Assisted Stress Corrosion Cracking) because BFBs are located at core region under severe environments, high neutron flux, high temperature and high stress. According to the material studies of IASCC on austenitic stainless steel, a crack initiation of IASCC is strongly related with the stress and the neutron fluence. For this reason, it is very important for IASCC failure prediction to simulate the stress of BFBs. However, the stress of BFBs are considered to be influenced by several factors and to be changed complexly as operational time increases, by irradiation creep of Bolt itself, swelling of Baffle structure, and so on. Therefore, it is difficult to estimate the stress histories of BFBs (Bolt stress as a function of operational time) precisely. Then, the author has developed the calculation method of the stress histories of BFBs considering irradiation effects (swelling and irradiation creep). In this method, the stress histories of BFBs are calculated by combining two kinds of FE models, Global model (modeled whole Baffle structure which consists of Baffle plates, Former plates and Core Barrel) and Local model (modeled around BFB finely). The whole Baffle structure deformation changes as a function of heat, swelling and irradiated creep are calculated by Global model, and the stress histories of BFBs are calculated by Local model using the outputs (deformations on driving nodes) of Global model. In the FE analysis of Local model, the stress of BFBs are calculated considering irradiation effects and elastic-plastic characteristics depending on neutron fluence, so this method enables to calculate precisely the stress of extreme small area of BFBs surface. This paper shows the outline of the calculation method

  16. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  17. Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm

    Science.gov (United States)

    Mariajayaprakash, Arokiasamy; Senthilvelan, Thiyagarajan; Vivekananthan, Krishnapillai Ponnambal

    2013-07-01

    The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and fixture height), washing process parameters (total alkalinity, temperature, pH value of rinsing water, and timing), and painting process parameters (flowability, coating thickness, pointage, and temperature). In this paper, the process parameters, namely, painting and washing process parameters, are optimized by Taguchi method. Though the defects are reasonably minimized by Taguchi method, in order to achieve zero defects during the processes, genetic algorithm technique is applied on the optimized parameters obtained by Taguchi method.

  18. α-Decomposition for estimating parameters in common cause failure modeling based on causal inference

    International Nuclear Information System (INIS)

    Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi

    2013-01-01

    The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system

  19. A Model to Predict Student Failure in the First Year of the Undergraduate Medical Curriculum

    Directory of Open Access Journals (Sweden)

    Gerard J.A. Baars

    2017-06-01

    Discussion: The earliest moment with the highest specificity to predict student failure in the first-year curriculum seems to be at 6 months. However, additional factors are needed to improve this prediction or to bring forward the predictive moment.

  20. Optimisation of the link volume for weakest link failure prediction in NBG-18 nuclear graphite

    International Nuclear Information System (INIS)

    Hindley, Michael P.; Groenwold, Albert A.; Blaine, Deborah C.; Becker, Thorsten H.

    2014-01-01

    This paper describes the process for approximating the optimal size of a link volume required for weakest link failure calculation in nuclear graphite, with NBG-18 used as an example. As part of the failure methodology, the link volume is defined in terms of two grouping criteria. The first criterion is a factor of the maximum grain size and the second criterion is a function of an equivalent stress limit. A methodology for approximating these grouping criteria is presented. The failure methodology employs finite element analysis (FEA) in order to predict the failure load, at 50% probability of failure. The average experimental failure load, as determined for 26 test geometries, is used to evaluate the accuracy of the weakest link failure calculations. The influence of the two grouping criteria on the failure load prediction is evaluated by defining an error in prediction across all test cases. Mathematical optimisation is used to find the minimum error across a range of test case failure predictions. This minimum error is shown to deliver the most accurate failure prediction across a whole range of components, although some test cases in the range predict conservative failure load. The mathematical optimisation objective function is penalised to account for non-conservative prediction of the failure load for any test case. The optimisation is repeated and a link volume found for conservative failure prediction. The failure prediction for each test case is evaluated, in detail, for the proposed link volumes. Based on the analysis, link design volumes for NBG-18 are recommended for either accurate or conservative failure prediction

  1. A COCAP program for the statistical analysis of common cause failure parameters

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Baehyeuk; Jae, Moosung [Hanyang Univ., Seoul (Korea, Republic of). Dept. of Nuclear Engineering

    2016-03-15

    Probabilistic Safety Assessment (PSA) based applications and regulations are becoming more important in the field of nuclear energy. According to the results of a PSA in Korea, the common cause failure evaluates CDF (Core Damage Frequency) as one of the significant factors affecting redundancy of NPPs. The purpose of the study is to develop a COCAP (Common Cause Failure parameter Analysis for PSA) program for the accurate use of the alpha factor model parameter data provided by other countries and for obtaining the indigenous CCF data of NPPs in Korea through Bayesian updating.

  2. Prediction of failure enthalpy and reliability of irradiated fuel rod under reactivity-initiated accidents by means of statistical approach

    International Nuclear Information System (INIS)

    Nam, Cheol; Choi, Byeong Kwon; Jeong, Yong Hwan; Jung, Youn Ho

    2001-01-01

    During the last decade, the failure behavior of high-burnup fuel rods under RIA has been an extensive concern since observations of fuel rod failures at low enthalpy. Of great importance is placed on failure prediction of fuel rod in the point of licensing criteria and safety in extending burnup achievement. To address the issue, a statistics-based methodology is introduced to predict failure probability of irradiated fuel rods. Based on RIA simulation results in literature, a failure enthalpy correlation for irradiated fuel rod is constructed as a function of oxide thickness, fuel burnup, and pulse width. From the failure enthalpy correlation, a single damage parameter, equivalent enthalpy, is defined to reflect the effects of the three primary factors as well as peak fuel enthalpy. Moreover, the failure distribution function with equivalent enthalpy is derived, applying a two-parameter Weibull statistical model. Using these equations, the sensitivity analysis is carried out to estimate the effects of burnup, corrosion, peak fuel enthalpy, pulse width and cladding materials used

  3. Predictive modelling of fatigue failure in concentrated lubricated contacts.

    Science.gov (United States)

    Evans, H P; Snidle, R W; Sharif, K J; Bryant, M J

    2012-01-01

    Reducing frictional losses in response to the energy agenda will require use of less viscous lubricants causing hydrodynamically-lubricated bearings to operate with thinner films leading to "mixed lubrication" conditions in which a degree of direct interaction occurs between surfaces protected only by boundary tribofilms. The paper considers the consequences of thinner films and mixed lubrication for concentrated contacts such as those occurring between the teeth of power transmission gears and in rolling element bearings. Surface fatigue in gears remains a serious problem in demanding applications, and its solution will become more pressing with the tendency towards thinner oils. The particular form of failure examined here is micropitting, which is identified as a fatigue phenomenon occurring at the scale of the surface roughness asperities. It has emerged recently as a systemic difficulty in the operation of large scale wind turbines where it occurs in both power transmission gears and their support bearings. Predictive physical modelling of these contacts requires a transient mixed lubrication analysis for conditions in which the predicted lubricant film thickness is of the same order or significantly less than the height of surface roughness features. Numerical solvers have therefore been developed which are able to deal with situations in which transient solid contacts occur between surface asperity features under realistic engineering conditions. Results of the analysis, which reveal the detailed time-varying behaviour of pressure and film clearance, have been used to predict fatigue and damage accumulation at the scale of surface asperity features with the aim of improving understanding of the micropitting phenomenon. The possible consequences on fatigue of residual stress fields resulting from plastic deformation of surface asperities is also considered.

  4. A New Energy-Critical Plane Damage Parameter for Multiaxial Fatigue Life Prediction of Turbine Blades

    Directory of Open Access Journals (Sweden)

    Zheng-Yong Yu

    2017-05-01

    Full Text Available As one of fracture critical components of an aircraft engine, accurate life prediction of a turbine blade to disk attachment is significant for ensuring the engine structural integrity and reliability. Fatigue failure of a turbine blade is often caused under multiaxial cyclic loadings at high temperatures. In this paper, considering different failure types, a new energy-critical plane damage parameter is proposed for multiaxial fatigue life prediction, and no extra fitted material constants will be needed for practical applications. Moreover, three multiaxial models with maximum damage parameters on the critical plane are evaluated under tension-compression and tension-torsion loadings. Experimental data of GH4169 under proportional and non-proportional fatigue loadings and a case study of a turbine disk-blade contact system are introduced for model validation. Results show that model predictions by Wang-Brown (WB and Fatemi-Socie (FS models with maximum damage parameters are conservative and acceptable. For the turbine disk-blade contact system, both of the proposed damage parameters and Smith-Watson-Topper (SWT model show reasonably acceptable correlations with its field number of flight cycles. However, life estimations of the turbine blade reveal that the definition of the maximum damage parameter is not reasonable for the WB model but effective for both the FS and SWT models.

  5. The prediction of cyclic proximal humerus fracture fixation failure by various bone density measures.

    Science.gov (United States)

    Varga, Peter; Grünwald, Leonard; Windolf, Markus

    2018-02-22

    Fixation of osteoporotic proximal humerus fractures has remained challenging, but may be improved by careful pre-operative planning. The aim of this study was to investigate how well the failure of locking plate fixation of osteoporotic proximal humerus fractures can be predicted by bone density measures assessed with currently available clinical imaging (realistic case) and a higher resolution and quality modality (theoretical best-case). Various density measures were correlated to experimentally assessed number of cycles to construct failure of plated unstable low-density proximal humerus fractures (N = 18). The influence of density evaluation technique was investigated by comparing local (peri-implant) versus global evaluation regions; HR-pQCT-based versus clinical QCT-based image data; ipsilateral versus contralateral side; and bone mineral content (BMC) versus bone mineral density (BMD). All investigated density measures were significantly correlated with the experimental cycles to failure. The best performing clinically feasible parameter was the QCT-based BMC of the contralateral articular cap region, providing significantly better correlation (R 2  = 0.53) compared to a previously proposed clinical density measure (R 2  = 0.30). BMC had consistently, but not significantly stronger correlations with failure than BMD. The overall best results were obtained with the ipsilateral HR-pQCT-based local BMC (R 2  = 0.74) that may be used for implant optimization. Strong correlations were found between the corresponding density measures of the two CT image sources, as well as between the two sides. Future studies should investigate if BMC of the contralateral articular cap region could provide improved prediction of clinical fixation failure compared to previously proposed measures. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

  6. Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

    KAUST Repository

    Sawlan, Zaid A

    2012-12-01

    Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

  7. Fast neutron reactor noise analysis: beginning failure detection and physical parameter estimation

    International Nuclear Information System (INIS)

    Le Guillou, G.

    1975-01-01

    The analysis of the signals fluctuations coming from a power nuclear reactor (a breeder), by correlation methods and spectral analysis has two principal applications: on line estimation of physical parameters (reactivity coefficients); beginning failures (little boiling, abnormal mechanic vibrations). These two applications give important informations to the reactor core control and permit a good diagnosis [fr

  8. Prediction of late failure after medical abortion from serial beta-hCG measurements and ultrasonography

    DEFF Research Database (Denmark)

    Rørbye, C; Nørgaard, M; Nilas, Lisbeth

    2004-01-01

    on day 15 were greater among late failures than successes. Used as a predictive test, the positive predictive values of these variables were low. CONCLUSION: Neither beta-hCG nor endometrial thickness can be used clinically as diagnostic tests in predicting late failure after medical abortion.......BACKGROUND: Surgical treatment of failed medical abortion may be performed several weeks after initiation of the abortion. There are no recognized methods for early identification of these late failures. We assessed the prognostic values of beta-hCG and ultrasonography in predicting late failure...... thickness by ultrasonography was performed on day 15 after induction of medical abortion. Failures diagnosed after day 15 and within 15 weeks were identified and classified as late failures. All interventions in this group were due to bleeding problems. The predictive values of different absolute...

  9. Predicting water main failures using Bayesian model averaging and survival modelling approach

    International Nuclear Information System (INIS)

    Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

    2015-01-01

    To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties. - Highlights: • Prioritize rehabilitation and replacements (R/R) strategies of water mains. • Consider the uncertainties for the failure prediction. • Improve the prediction capability of the water mains failure models. • Identify the influential and appropriate covariates for different models. • Determine the effects of the covariates on failure

  10. Updated climatological model predictions of ionospheric and HF propagation parameters

    International Nuclear Information System (INIS)

    Reilly, M.H.; Rhoads, F.J.; Goodman, J.M.; Singh, M.

    1991-01-01

    The prediction performances of several climatological models, including the ionospheric conductivity and electron density model, RADAR C, and Ionospheric Communications Analysis and Predictions Program, are evaluated for different regions and sunspot number inputs. Particular attention is given to the near-real-time (NRT) predictions associated with single-station updates. It is shown that a dramatic improvement can be obtained by using single-station ionospheric data to update the driving parameters for an ionospheric model for NRT predictions of f(0)F2 and other ionospheric and HF circuit parameters. For middle latitudes, the improvement extends out thousands of kilometers from the update point to points of comparable corrected geomagnetic latitude. 10 refs

  11. Research on Relationship Between Parameters Correlation of Acoustic Emission and Rock Failure

    Directory of Open Access Journals (Sweden)

    Duan Dong

    2014-12-01

    Full Text Available Analyzes that granite AE signal parameters under uniaxial loading by that way of Pearson linear correlation, research that correlation of characterization parameters within that separate group with various characteristics, and analyzes that relationship between each parameter and destruction. This study shows that: impact, events and ringing are mainly used to describe the damage degree of rock, amplitude characteristics, time characteristics and frequency characteristics are mainly used for acoustic emission source properties, and energy characteristics can not only be used to describe the damage degree of rock, but also be used to analyze the acoustic emission source. That ringing counts are highly interrelated with energy, intensity, duration, RMS and ASL have high correlation, a high correlation is in the three parameters of the energy characteristics, and there is a higher correlation between the two parameters of the timing characteristics. The correlation between the parameters of frequency is very low, and the acoustic emission parameters can't be replaced for each other in analysis, which need separate analysis. Characteristics of ringing and energy can be a very good description of failure, but failure precursors can't be quantized. However, the amplitude, RMS, ASL, can quantify characterization of that precursor of failure, such as the effective voltage value 0.7 V as the precursor of destruction, the emergence of amplitude exceeding 95 dB as that destructive precursor. The relationship between the timing characteristics and damage is not obvious, so you can't use those parameters analysis that fracture of rocks. But those parameters can be used to describe AE source characteristics. The peak frequency, inverse frequency and the center frequency can't reflect AE source characteristics, and that average frequency and initial frequency can reflect AE source characteristics.

  12. Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

    Science.gov (United States)

    Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

    2018-03-01

    This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

  13. Modelling hydrodynamic parameters to predict flow assisted corrosion

    International Nuclear Information System (INIS)

    Poulson, B.; Greenwell, B.; Chexal, B.; Horowitz, J.

    1992-01-01

    During the past 15 years, flow assisted corrosion has been a worldwide problem in the power generating industry. The phenomena is complex and depends on environment, material composition, and hydrodynamic factors. Recently, modeling of flow assisted corrosion has become a subject of great importance. A key part of this effort is modeling the hydrodynamic aspects of this issue. This paper examines which hydrodynamic parameter should be used to correlate the occurrence and rate of flow assisted corrosion with physically meaningful parameters, discusses ways of measuring the relevant hydrodynamic parameter, and describes how the hydrodynamic data is incorporated into the predictive model

  14. A model to predict failure of irradiated U–Mo dispersion fuel

    Energy Technology Data Exchange (ETDEWEB)

    Burkes, Douglas E., E-mail: Douglas.Burkes@pnnl.gov; Senor, David J.; Casella, Andrew M.

    2016-12-15

    Highlights: • Simple model to predict failure of dispersion fuel meat designs. • Evaluated as a function of fabrication parameters and irradiation conditions. • Predictions compare well with experimental measurements of miniature fuel plates. • Interaction layer formation reduces matrix strength and increases temperature. • Si additions to the matrix appear effective only at moderate heat flux and burnup. - Abstract: Numerous global programs are focused on the continued development of existing and new research and test reactor fuels to achieve maximum attainable uranium loadings to support the conversion of a number of the world’s remaining high-enriched uranium fueled reactors to low-enriched uranium fuel. Some of these programs are focused on development and qualification of a fuel design that consists of a uranium–molybdenum (U–Mo) alloy dispersed in an aluminum matrix as one option for reactor conversion. The current paper extends a failure model originally developed for UO{sub 2}-stainless steel dispersion fuels and uses currently available thermal–mechanical property information for the materials of interest in the currently proposed design. A number of fabrication and irradiation parameters were investigated to understand the conditions at which failure of the matrix, classified as onset of pore formation in the matrix, might occur. The results compared well with experimental observations published as part of the Reduced Enrichment for Research and Test Reactors (RERTR)-6 and -7 mini-plate experiments. Fission rate, a function of the {sup 235}U enrichment, appeared to be the most influential parameter in premature failure, mainly as a result of increased interaction layer formation and operational temperature, which coincidentally decreased the strength of the matrix and caused more rapid fission gas production and recoil into the surrounding matrix material. Addition of silicon to the matrix appeared effective at reducing the rate of

  15. How to Predict Oral Rehydration Failure in Children With Gastroenteritis.

    Science.gov (United States)

    Geurts, Dorien; Steyerberg, Ewout W; Moll, Henriëtte; Oostenbrink, Rianne

    2017-11-01

    Oral rehydration is the standard in most current guidelines for young children with acute gastroenteritis (AGE). Failure of oral rehydration can complicate the disease course, leading to morbidity due to severe dehydration. We aimed to identify prognostic factors of oral rehydration failure in children with AGE. A prospective, observational study was performed at the Emergency department, Erasmus Medical Centre, Rotterdam, The Netherlands, 2010-2012, including 802 previously healthy children, ages 1 month to 5 years with AGE. Failure of oral rehydration was defined by secondary rehydration by a nasogastric tube, or hospitalization or revisit for dehydration within 72 hours after initial emergency department visit. We observed 167 (21%) failures of oral rehydration in a population of 802 children with AGE (median 1.03 years old, interquartile range 0.4-2.1; 60% boys). In multivariate logistic regression analysis, independent predictors for failure of oral rehydration were a higher Manchester Triage System urgency level, abnormal capillary refill time, and a higher clinical dehydration scale score. Early recognition of young children with AGE at risk of failure of oral rehydration therapy is important, as emphasized by the 21% therapy failure in our population. Associated with oral rehydration failure are higher Manchester Triage System urgency level, abnormal capillary refill time, and a higher clinical dehydration scale score.

  16. Failure mitigation in software defined networking employing load type prediction

    KAUST Repository

    Bouacida, Nader; Alghadhban, Amer Mohammad JarAlla; Alalmaei, Shiyam Mohammed Abdullah; Mohammed, Haneen; Shihada, Basem

    2017-01-01

    The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since

  17. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    Science.gov (United States)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  18. Predicting Subsequent Task Performance From Goal Motivation and Goal Failure

    Directory of Open Access Journals (Sweden)

    Laura Catherine Healy

    2015-07-01

    Full Text Available Recent research has demonstrated that the cognitive processes associated with goal pursuit can continue to interfere with unrelated tasks when a goal is unfulfilled. Drawing from the self-regulation and goal-striving literatures, the present study explored the impact of goal failure on subsequent cognitive and physical task performance. Furthermore, we examined if the autonomous or controlled motivation underpinning goal striving moderates the responses to goal failure. Athletes (75 male, 59 female, Mage = 19.90 years, SDage = 3.50 completed a cycling trial with the goal of covering a given distance in 8 minutes. Prior to the trial, their motivation was primed using a video. During the trial they were provided with manipulated performance feedback, thus creating conditions of goal success or failure. No differences emerged in the responses to goal failure between the primed motivation or performance feedback conditions. We make recommendations for future research into how individuals can deal with failure in goal striving.

  19. Predictive Simulation of Material Failure Using Peridynamics -- Advanced Constitutive Modeling, Verification and Validation

    Science.gov (United States)

    2016-03-31

    AFRL-AFOSR-VA-TR-2016-0309 Predictive simulation of material failure using peridynamics- advanced constitutive modeling, verification , and validation... Self -explanatory. 8. PERFORMING ORGANIZATION REPORT NUMBER. Enter all unique alphanumeric report numbers assigned by the performing organization, e.g...for public release. Predictive simulation of material failure using peridynamics-advanced constitutive modeling, verification , and validation John T

  20. [Predictive factors for failure of non-invasive positive pressure ventilation in immunosuppressed patients with acute respiratory failure].

    Science.gov (United States)

    Jia, Xiangli; Yan, Ci; Xu, Sicheng; Gu, Xingli; Wan, Qiufeng; Hu, Xinying; Li, Jingwen; Liu, Guangming; Caikai, Shareli; Guo, Zhijin

    2018-02-01

    To evaluate the predictive factors for failure of non-invasive positive pressure ventilation (NIPPV) in immunosuppressed patients with acute respiratory failure (ARF). The clinical data of 118 immuno-deficient patients treated with NIPPV in the respiratory and intensive care unit (RICU) of the First Affiliated Hospital of Xinjiang Medical University from January 2012 to August 2017 were retrospectively analyzed. The patients were divided into a non-endotracheal intubation (ETI) group (n = 62) and ETI group (n = 56) according to whether ETI was performed during the hospitalization period or not. Each observed indicator was analyzed by univariate analysis, and factors leading to failure of NIPPV were further analyzed by Logistic regression. Receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of risk factors for failure of NIPPV in immunosuppressed patients with ARF. The non-intubation rate for NIPPV in immunosuppressed patients was 50.8% (60/118). Compared with the non-ETI group, the body temperature, pH value in the ETI group were significantly increased, the partial pressure of arterial carbon dioxide (PaCO 2 ) was significantly decreased, the ratio of oxygenation index (PaO 2 /FiO 2 ) failure of NIPPV. ROC curve analysis showed that the APACHE II score ≥ 20 and PaO 2 /FiO 2 failure of NIPPV, the area under ROC curve (AUC) of the APACHE II score ≥ 20 was 0.787, the sensitivity was 83.93%, the specificity was 69.35%, the positive predict value (PPV) was 71.21%, the negative predict value (NPV) was 82.69%, the positive likelihood ratio (PLR) was 2.74, the negative likelihood ratio (NLR) was 0.23, and Youden index was 0.53; the AUC of PaO 2 /FiO 2 failure of NIPPV in immunocompromised patients.

  1. Optimal parameters of the SVM for temperature prediction

    Directory of Open Access Journals (Sweden)

    X. Shi

    2015-05-01

    Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

  2. Effect of uncertainty parameters on graphene sheets Young's modulus prediction

    International Nuclear Information System (INIS)

    Sahlaoui, Habib; Sidhom Habib; Guedri, Mohamed

    2013-01-01

    Software based on molecular structural mechanics approach (MSMA) and using finite element method (FEM) has been developed to predict the Young's modulus of graphene sheets. Obtained results have been compared to results available in the literature and good agreement has been shown when the same values of uncertainty parameters are used. A sensibility of the models to their uncertainty parameters has been investigated using a stochastic finite element method (SFEM). The different values of the used uncertainty parameters, such as molecular mechanics force field constants k_r and k_θ, thickness (t) of a graphene sheet and length ( L_B) of a carbon carbon bonds, have been collected from the literature. Strong sensibilities of 91% to the thickness and of 21% to the stretching force (k_r) have been shown. The results justify the great difference between Young's modulus predicted values of the graphene sheets and their large disagreement with experimental results.

  3. Properties of parameter estimation techniques for a beta-binomial failure model. Final technical report

    International Nuclear Information System (INIS)

    Shultis, J.K.; Buranapan, W.; Eckhoff, N.D.

    1981-12-01

    Of considerable importance in the safety analysis of nuclear power plants are methods to estimate the probability of failure-on-demand, p, of a plant component that normally is inactive and that may fail when activated or stressed. Properties of five methods for estimating from failure-on-demand data the parameters of the beta prior distribution in a compound beta-binomial probability model are examined. Simulated failure data generated from a known beta-binomial marginal distribution are used to estimate values of the beta parameters by (1) matching moments of the prior distribution to those of the data, (2) the maximum likelihood method based on the prior distribution, (3) a weighted marginal matching moments method, (4) an unweighted marginal matching moments method, and (5) the maximum likelihood method based on the marginal distribution. For small sample sizes (N = or < 10) with data typical of low failure probability components, it was found that the simple prior matching moments method is often superior (e.g. smallest bias and mean squared error) while for larger sample sizes the marginal maximum likelihood estimators appear to be best

  4. Multi-Marker Strategy in Heart Failure: Combination of ST2 and CRP Predicts Poor Outcome.

    Directory of Open Access Journals (Sweden)

    Anne Marie Dupuy

    Full Text Available Natriuretic peptides (BNP and NT-proBNP are recognized as gold-standard predictive markers in Heart Failure (HF. However, currently ST2 (member of the interleukin 1 receptor family has emerged as marker of inflammation, fibrosis and cardiac stress. We evaluated ST2 and CRP as prognostic markers in 178 patients with chronic heart failure in comparison with other classical markers such as clinical established parameters but also biological markers: NT-proBNP, hs-cTnT alone or in combination. In multivariate analysis, subsequent addition of ST2 led to age, CRP and ST2 as the only remaining predictors of all-cause mortality (HR 1.03, HR 1.61 and HR 2.75, respectively as well as of cardiovascular mortality (HR 1.00, HR 2.27 and HR 3.78, respectively. The combined increase of ST2 and CRP was significant for predicting worsened outcomes leading to identify a high risk subgroup that individual assessment of either marker. The same analysis was performed with ST2 in combination with Barcelona score. Overall, our findings extend previous data demonstrating that ST2 in combination with CRP as a valuable tool for identifying patients at risk of death.

  5. Failure prediction using machine learning and time series in optical network.

    Science.gov (United States)

    Wang, Zhilong; Zhang, Min; Wang, Danshi; Song, Chuang; Liu, Min; Li, Jin; Lou, Liqi; Liu, Zhuo

    2017-08-07

    In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

  6. Prediction of potential failures in hydraulic gear pumps

    Directory of Open Access Journals (Sweden)

    E. Lisowski

    2010-07-01

    Full Text Available Hydraulic gear pumps are used in many machines and devices. In hydraulic systems of machines gear pumps are main component ofsupply unit or perform auxiliary function. Gear pumps opposite to vane pumps are less complicated. They consists of such components as:housing, gear wheels, bearings, shaft, seal for rotation motion which are not very sensitive for damage and that is why they are using veryoften. However, gear pumps are break down from time to time. Usually damage of pump cause shutting down of machines and devices.One of the way for identifying potential failures and foreseeing their effects is a quality method. On the basis of these methods apreventing action might be undertaken before failure appear. In this paper potential failures and damages of a gear pump were presented bythe usage of matrix FMEA analysis.

  7. Patient Characteristics Predicting Readmission Among Individuals Hospitalized for Heart Failure.

    Science.gov (United States)

    O'Connor, Melissa; Murtaugh, Christopher M; Shah, Shivani; Barrón-Vaya, Yolanda; Bowles, Kathryn H; Peng, Timothy R; Zhu, Carolyn W; Feldman, Penny H

    2016-02-01

    Heart failure is difficult to manage and increasingly common with many individuals experiencing frequent hospitalizations. Little is known about patient factors consistently associated with hospital readmission. A literature review was conducted to identify heart failure patient characteristics, measured before discharge, that contribute to variation in hospital readmission rates. Database searches yielded 950 potential articles, of which 34 studies met inclusion criteria. Patient characteristics generally have a very modest effect on all-cause or heart failure-related readmission within 7 to 180 days of index hospital discharge. A range of cardiac diseases and other comorbidities only minimally increase readmission rates. No single patient characteristic stands out as a key contributor across multiple studies underscoring the challenge of developing successful interventions to reduce readmissions. Interventions may need to be general in design with the specific intervention depending on each patient's unique clinical profile. © The Author(s) 2015.

  8. Prediction of interest rate using CKLS model with stochastic parameters

    International Nuclear Information System (INIS)

    Ying, Khor Chia; Hin, Pooi Ah

    2014-01-01

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ (j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ (j) , we assume that φ (j) depends on φ (j−m) , φ (j−m+1) ,…, φ (j−1) and the interest rate r j+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r j+n+1 of the interest rate at the next time point when the value r j+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r j+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters

  9. Prediction of interest rate using CKLS model with stochastic parameters

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)

    2014-06-19

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

  10. Embedded mechatronic systems 1 analysis of failures, predictive reliability

    CERN Document Server

    El Hami, Abdelkhalak

    2015-01-01

    In operation, mechatronics embedded systems are stressed by loads of different causes: climate (temperature, humidity), vibration, electrical and electromagnetic. These stresses in components which induce failure mechanisms should be identified and modeled for better control. AUDACE is a collaborative project of the cluster Mov'eo that address issues specific to mechatronic reliability embedded systems. AUDACE means analyzing the causes of failure of components of mechatronic systems onboard. The goal of the project is to optimize the design of mechatronic devices by reliability. The projec

  11. Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness.

    Science.gov (United States)

    Hodges-Simeon, Carolyn R; Gaulin, Steven J C; Puts, David A

    2010-12-01

    Low mean fundamental frequency (F(0)) in men's voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fertile and non-fertile menstrual cycle phases for desirability in short-term and long-term relationships. Five vocal parameters were analyzed: mean F(0) (an acoustic correlate of vocal fold size), F(0) variation, intensity (loudness), utterance duration, and formant dispersion (D(f), an acoustic correlate of vocal tract length). Parallel but separate ratings of speech transcripts served as controls for content. Multiple regression analyses were used to examine the independent contributions of each of the predictors. Physical dominance was predicted by low F(0) variation and physically dominant word content. Social dominance was predicted only by socially dominant word content. Ratings of attractiveness by women were predicted by low mean F(0), low D(f), high intensity, and attractive word content across cycle phase and mating context. Low D(f) was perceived as attractive by fertile-phase women only. We hypothesize that competitors and potential mates may attend more strongly to different components of men's voices because of the different types of information these vocal parameters provide.

  12. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

    Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

  13. Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

    Science.gov (United States)

    Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

    2017-09-01

    This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

  14. Vertebral body spread in thoracolumbar burst fractures can predict posterior construct failure.

    Science.gov (United States)

    De Iure, Federico; Lofrese, Giorgio; De Bonis, Pasquale; Cultrera, Francesco; Cappuccio, Michele; Battisti, Sofia

    2018-06-01

    The load sharing classification (LSC) laid foundations for a scoring system able to indicate which thoracolumbar fractures, after short-segment posterior-only fixations, would need longer instrumentations or additional anterior supports. We analyzed surgically treated thoracolumbar fractures, quantifying the vertebral body's fragment displacement with the aim of identifying a new parameter that could predict the posterior-only construct failure. This is a retrospective cohort study from a single institution. One hundred twenty-one consecutive patients were surgically treated for thoracolumbar burst fractures. Grade of kyphosis correction (GKC) expressed radiological outcome; Oswestry Disability Index and visual analog scale were considered. One hundred twenty-one consecutive patients who underwent posterior fixation for unstable thoracolumbar burst fractures were retrospectively evaluated clinically and radiologically. Supplementary anterior fixations were performed in 34 cases with posterior instrumentation failure, determined on clinic-radiological evidence or symptomatic loss of kyphosis correction. Segmental kyphosis angle and GKC were calculated according to the Cobb method. The displacement of fracture fragments was obtained from the mean of the adjacent end plate areas subtracted from the area enclosed by the maximum contour of vertebral fragmentation. The "spread" was derived from the ratio between this subtraction and the mean of the adjacent end plate areas. Analysis of variance, Mann-Whitney, and receiver operating characteristic were performed for statistical analysis. The authors report no conflict of interest concerning the materials or methods used in the present study or the findings specified in this paper. No funds or grants have been received for the present study. The spread revealed to be a helpful quantitative measurement of vertebral body fragment displacement, easily reproducible with the current computed tomography (CT) imaging technologies

  15. Prediction of dynamic expected time to system failure

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Lee, Chong Chul [Korea Nuclear Fuel Co., Ltd., Taejon (Korea, Republic of)

    1997-12-31

    The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)

  16. How to Predict Oral Rehydration Failure in Children With Gastroenteritis

    NARCIS (Netherlands)

    D.H.F. Geurts (Dorien); E.W. Steyerberg (Ewout); H.A. Moll (Henriëtte); R. Oostenbrink (Rianne)

    2017-01-01

    textabstractOBJECTIVES:: Oral rehydration is the standard in most current guidelines for young children with acute gastroenteritis (AGE). Failure of oral rehydration can complicate the disease course, leading to morbidity due to severe dehydration. We aimed to identify prognostic factors of oral

  17. Prediction of dynamic expected time to system failure

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Lee, Chong Chul [Korea Nuclear Fuel Co., Ltd., Taejon (Korea, Republic of)

    1998-12-31

    The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)

  18. Predicting plant biomass accumulation from image-derived parameters

    Science.gov (United States)

    Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

    2018-01-01

    Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

  19. Does working memory capacity predict cross-modally induced failures of awareness?

    Science.gov (United States)

    Kreitz, Carina; Furley, Philip; Simons, Daniel J; Memmert, Daniel

    2016-01-01

    People often fail to notice unexpected stimuli when they are focusing attention on another task. Most studies of this phenomenon address visual failures induced by visual attention tasks (inattentional blindness). Yet, such failures also occur within audition (inattentional deafness), and people can even miss unexpected events in one sensory modality when focusing attention on tasks in another modality. Such cross-modal failures are revealing because they suggest the existence of a common, central resource limitation. And, such central limits might be predicted from individual differences in cognitive capacity. We replicated earlier evidence, establishing substantial rates of inattentional deafness during a visual task and inattentional blindness during an auditory task. However, neither individual working memory capacity nor the ability to perform the primary task predicted noticing in either modality. Thus, individual differences in cognitive capacity did not predict failures of awareness even though the failures presumably resulted from central resource limitations. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Reliability of piping system components. Framework for estimating failure parameters from service data

    International Nuclear Information System (INIS)

    Nyman, R.; Hegedus, D.; Tomic, B.; Lydell, B.

    1997-12-01

    This report summarizes results and insights from the final phase of a R and D project on piping reliability sponsored by the Swedish Nuclear Power Inspectorate (SKI). The technical scope includes the development of an analysis framework for estimating piping reliability parameters from service data. The R and D has produced a large database on the operating experience with piping systems in commercial nuclear power plants worldwide. It covers the period 1970 to the present. The scope of the work emphasized pipe failures (i.e., flaws/cracks, leaks and ruptures) in light water reactors (LWRs). Pipe failures are rare events. A data reduction format was developed to ensure that homogenous data sets are prepared from scarce service data. This data reduction format distinguishes between reliability attributes and reliability influence factors. The quantitative results of the analysis of service data are in the form of conditional probabilities of pipe rupture given failures (flaws/cracks, leaks or ruptures) and frequencies of pipe failures. Finally, the R and D by SKI produced an analysis framework in support of practical applications of service data in PSA. This, multi-purpose framework, termed 'PFCA'-Pipe Failure Cause and Attribute- defines minimum requirements on piping reliability analysis. The application of service data should reflect the requirements of an application. Together with raw data summaries, this analysis framework enables the development of a prior and a posterior pipe rupture probability distribution. The framework supports LOCA frequency estimation, steam line break frequency estimation, as well as the development of strategies for optimized in-service inspection strategies

  1. Reliability of piping system components. Framework for estimating failure parameters from service data

    Energy Technology Data Exchange (ETDEWEB)

    Nyman, R [Swedish Nuclear Power Inspectorate, Stockholm (Sweden); Hegedus, D; Tomic, B [ENCONET Consulting GesmbH, Vienna (Austria); Lydell, B [RSA Technologies, Vista, CA (United States)

    1997-12-01

    This report summarizes results and insights from the final phase of a R and D project on piping reliability sponsored by the Swedish Nuclear Power Inspectorate (SKI). The technical scope includes the development of an analysis framework for estimating piping reliability parameters from service data. The R and D has produced a large database on the operating experience with piping systems in commercial nuclear power plants worldwide. It covers the period 1970 to the present. The scope of the work emphasized pipe failures (i.e., flaws/cracks, leaks and ruptures) in light water reactors (LWRs). Pipe failures are rare events. A data reduction format was developed to ensure that homogenous data sets are prepared from scarce service data. This data reduction format distinguishes between reliability attributes and reliability influence factors. The quantitative results of the analysis of service data are in the form of conditional probabilities of pipe rupture given failures (flaws/cracks, leaks or ruptures) and frequencies of pipe failures. Finally, the R and D by SKI produced an analysis framework in support of practical applications of service data in PSA. This, multi-purpose framework, termed `PFCA`-Pipe Failure Cause and Attribute- defines minimum requirements on piping reliability analysis. The application of service data should reflect the requirements of an application. Together with raw data summaries, this analysis framework enables the development of a prior and a posterior pipe rupture probability distribution. The framework supports LOCA frequency estimation, steam line break frequency estimation, as well as the development of strategies for optimized in-service inspection strategies. 63 refs, 30 tabs, 22 figs.

  2. Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure

    Directory of Open Access Journals (Sweden)

    Hendry Purnasidha Bagaswoto

    2016-12-01

    Full Text Available ABSTRACT Heart failure is divided into heart failure with reduced ejection fraction (HFrEF and heart failure with preserved ejection fraction (HFpEF. Additional studies are required to distinguish between these two types of HF. A previous study showed that HFrEF is less likely when ECG findings are normal. This study aims to create a scoring system based on ECG findings that will predict the type of HF. We performed a cross-sectional study analyzing ECG and echocardiographic data from 110 subjects. HFrEF was defined as an ejection fraction ≤40%. Fifty people were diagnosed with HFpEF and 60 people suffered from HFrEF. Multiple logistic regression analysis revealed certain ECG variables that were independent predictors of HFrEF i.e., LAH, QRS duration >100 ms, RBBB, ST-T segment changes and prolongation of the QT interval. Based on ROC curve analysis, we obtained a score for HFpEF of -1 to +3, while HFrEF had a score of +4 to +6 with 76% sensitivity, 96% specificity, 95% positive predictive value, an 80% negative predictive value and an accuracy of 86%. The scoring system derived from this study, including the presence or absence of LAH, QRS duration >100 ms, RBBB, ST-T segment changes and prolongation of the QT interval can be used to predict the type of HF with satisfactory sensitivity and specificity

  3. Failure prediction for Crack-in-Corrosion defects in natural gas transmission pipelines

    International Nuclear Information System (INIS)

    Bedairi, B.; Cronin, D.; Hosseini, A.; Plumtree, A.

    2012-01-01

    Cracks occurring coincidentally with corrosion (Crack-in-Corrosion or CIC), represent a new hybrid defect in pipelines that are not directly addressed in the current codes or assessment methods. To understand the failure response of these defects, the finite element method using an elastic–plastic fracture mechanics approach was applied to predict the failure pressures of comparable crack, corrosion and CIC defects in 508 mm diameter pipe with 5.7 mm wall thickness. Failure pressure predictions were made based on measured tensile, Charpy impact and J testing data, and validated using experimental rupture tests. Plastic collapse was predicted for corrosion and crack defects using the critical strength based on the material tensile strength, whereas fracture was predicted using the measured J 0.2 value. The model predictions were found to be conservative for the CIC defects (17.4% on average), 12.4% conservative for crack-only defects, and 3.2% conservative for corrosion defects compared to the experimental tests, demonstrating the applicability of the material-based failure criteria. For the defects considered in this study, all were predicted to fail by plastic collapse. The finite element method provided less conservative predictions than existing corrosion or crack-based analytical methods. Highlights: ► Cracks occurring coincidentally with corrosion represent a new hybrid defect in pipelines. ► Existing methods for prediction corrosion and crack defect failure pressures are conservative. ► The FE method can provide improved prediction of rupture pressure using actual material properties. ► Failure was predicted using FE with a critical stress for plastic collapse and J value for fracture. ► FE failure pressure predictions for crack in corrosion defects were 17% conservative on average.

  4. Patient Characteristics Predicting Readmission Among Individuals Hospitalized for Heart Failure

    Science.gov (United States)

    O'Connor, Melissa; Murtaugh, Christopher M.; Shah, Shivani; Barrón-Vaya, Yolanda; Bowles, Kathryn H.; Peng, Timothy R.; Zhu, Carolyn W.; Feldman, Penny H.

    2015-01-01

    Heart failure is difficult to manage and increasingly common with many individuals experiencing frequent hospitalizations. Little is known about patient factors consistently associated with hospital readmission. A literature review was conducted to identify heart failure patient characteristics, measured before discharge, that contribute to variation in hospital readmission rates. Database searches yielded 950 potential articles, of which 34 studies met inclusion criteria. Patient characteristics generally have a very modest effect on all-cause or heart failure–related readmission within 7 to 180 days of index hospital discharge. A range of cardiac diseases and other comorbidities only minimally increase readmission rates. No single patient characteristic stands out as a key contributor across multiple studies underscoring the challenge of developing successful interventions to reduce readmissions. Interventions may need to be general in design with the specific intervention depending on each patient's unique clinical profile. PMID:26180045

  5. Prediction of late failure after medical abortion from serial beta-hCG measurements and ultrasonography

    DEFF Research Database (Denmark)

    Rørbye, C; Nørgaard, M; Nilas, Lisbeth

    2004-01-01

    BACKGROUND: Surgical treatment of failed medical abortion may be performed several weeks after initiation of the abortion. There are no recognized methods for early identification of these late failures. We assessed the prognostic values of beta-hCG and ultrasonography in predicting late failure ...

  6. Using neural networks for prediction of nuclear parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

    2013-07-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  7. Using neural networks for prediction of nuclear parameters

    International Nuclear Information System (INIS)

    Pereira Filho, Leonidas; Souto, Kelling Cabral; Machado, Marcelo Dornellas

    2013-01-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  8. Predicting device failure after percutaneous repair of functional mitral regurgitation in advanced heart failure: Implications for patient selection.

    Science.gov (United States)

    Stolfo, Davide; De Luca, Antonio; Morea, Gaetano; Merlo, Marco; Vitrella, Giancarlo; Caiffa, Thomas; Barbati, Giulia; Rakar, Serena; Korcova, Renata; Perkan, Andrea; Pinamonti, Bruno; Pappalardo, Aniello; Berardini, Alessandra; Biagini, Elena; Saia, Francesco; Grigioni, Francesco; Rapezzi, Claudio; Sinagra, Gianfranco

    2018-04-15

    Patients with heart failure (HF) and severe symptomatic functional mitral regurgitation (FMR) may benefit from MitraClip implantation. With increasing numbers of patients being treated the success of procedure becomes a key issue. We sought to investigate the pre-procedural predictors of device failure in patients with advanced HF treated with MitraClip. From April 2012 to November 2016, 76 patients with poor functional class (NYHA class III-IV) and severe left ventricular (LV) remodeling underwent MitraClip implantation at University Hospitals of Trieste and Bologna (Italy). Device failure was assessed according to MVARC criteria. Patients were subsequently followed to additionally assess the patient success after 12months. Mean age was 67±12years, the mean Log-EuroSCORE was 23.4±16.5%, and the mean LV end-diastolic volume index and ejection fraction (EF) were 112±33ml/m 2 and 30.6±8.9%, respectively. At short-term evaluation, device failure was observed in 22 (29%) patients. Univariate predictors of device failure were LVEF, LV and left atrial volumes and anteroposterior mitral annulus diameter. Annulus dimension (OR 1.153, 95% CI 1.002-1.327, p=0.043) and LV end-diastolic volume (OR 1.024, 95% CI 1.000-1.049, p=0.049) were the only variables independently associated with the risk of device failure at the multivariate model. Pre-procedural anteroposterior mitral annulus diameter accurately predicted the risk of device failure after MitraClip in the setting of advanced HF. Its assessment might aid the selection of the best candidates to percutaneous correction of FMR. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Mathematical models to predict rheological parameters of lateritic hydromixtures

    Directory of Open Access Journals (Sweden)

    Gabriel Hernández-Ramírez

    2017-10-01

    Full Text Available The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to the Herschel-Bulkley model for real plastics. In addition, they show that for current operating conditions, even for new situations, UPD mathematical models have a greater ability to predict rheological parameters than least squares mathematical models.

  10. Prediction of failure in tube hydrofonning process using a damage model

    International Nuclear Information System (INIS)

    Majzoobi, G. H.; Saniee, F. Freshteh; Shirazi, A.

    2007-01-01

    In tube hydroforming process (THP), two types of loading, internal pressure and axial feeding and in particular the combination of them, are needed to feed the material into the cavities of the die to form the workpiece into the desired shape. If the variation of pressure versus axial feeding is not determined properly, the workpiece may be buckled, wrinkled or burst during THP. The appropriate variation is normally determined by experiment which is expensive and time-consuming. In this work, numerical simulation using Johnson-Cook models for predicting the elasto-plastic response and the failure of the material are employed to obtain the best combination of internal pressure and axial feeding. The numerical simulations are examined by a number of experiments conducted in the present investigation. The results show very close agreement between the numerical simulations and the experiments, suggesting that the numerical simulations using Johnson-Cook material and failure models provide a valuable tool to examine the different parameters involved in THP

  11. Failure Predictions for VHTR Core Components using a Probabilistic Contiuum Damage Mechanics Model

    Energy Technology Data Exchange (ETDEWEB)

    Fok, Alex

    2013-10-30

    The proposed work addresses the key research need for the development of constitutive models and overall failure models for graphite and high temperature structural materials, with the long-term goal being to maximize the design life of the Next Generation Nuclear Plant (NGNP). To this end, the capability of a Continuum Damage Mechanics (CDM) model, which has been used successfully for modeling fracture of virgin graphite, will be extended as a predictive and design tool for the core components of the very high- temperature reactor (VHTR). Specifically, irradiation and environmental effects pertinent to the VHTR will be incorporated into the model to allow fracture of graphite and ceramic components under in-reactor conditions to be modeled explicitly using the finite element method. The model uses a combined stress-based and fracture mechanics-based failure criterion, so it can simulate both the initiation and propagation of cracks. Modern imaging techniques, such as x-ray computed tomography and digital image correlation, will be used during material testing to help define the baseline material damage parameters. Monte Carlo analysis will be performed to address inherent variations in material properties, the aim being to reduce the arbitrariness and uncertainties associated with the current statistical approach. The results can potentially contribute to the current development of American Society of Mechanical Engineers (ASME) codes for the design and construction of VHTR core components.

  12. Investigation into the influence of build parameters on failure of 3D printed parts

    Science.gov (United States)

    Fornasini, Giacomo

    Additive manufacturing, including fused deposition modeling (FDM), is transforming the built world and engineering education. Deep understanding of parts created through FDM technology has lagged behind its adoption in home, work, and academic environments. Properties of parts created from bulk materials through traditional manufacturing are understood well enough to accurately predict their behavior through analytical models. Unfortunately, Additive Manufacturing (AM) process parameters create anisotropy on a scale that fundamentally affects the part properties. Understanding AM process parameters (implemented by program algorithms called slicers) is necessary to predict part behavior. Investigating algorithms controlling print parameters (slicers) revealed stark differences between the generation of part layers. In this work, tensile testing experiments, including a full factorial design, determined that three key factors, width, thickness, infill density, and their interactions, significantly affect the tensile properties of 3D printed test samples.

  13. Failure Rate Prediction of Active Component Using PM Basis Database

    International Nuclear Information System (INIS)

    Kim, J. S.; Kim, H. W.; Park, J. S.; Jung, S. G.

    2011-01-01

    The safety security and efficient management of NPPs (Nuclear Power Plants) have been increased after the accident of TEPCO's Fukushima nuclear power stations. The needs for the safety and efficiency are becoming more important because about 90 percent of the NPPs all over the world are more than 20 operation years old. The preventive maintenance criteria need to be flexible, considering long-term development of the equipment performance and preventive maintenance. The PMBD (Preventive Maintenance Basis Database) program was widely used for optimization of the preventive maintenance criteria. PMBD program contains all kinds of failure mechanisms for each equipment that may occur in the power plant based on RCM(Reliability-Centered Maintenance) and numerically calculate the variation of reliability and failure rate based on PM interval changes. In this study, propriety evaluation of preventive maintenance task, cycle, technical basis for cost effective preventive maintenance strategy and an appropriate evaluation were suggested by the case application of PMBD for major components in the NPPs

  14. Narrowing the scope of failure prediction using targeted fault load injection

    Science.gov (United States)

    Jordan, Paul L.; Peterson, Gilbert L.; Lin, Alan C.; Mendenhall, Michael J.; Sellers, Andrew J.

    2018-05-01

    As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.

  15. An Integrated Model to Predict Corporate Failure of Listed Companies in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Nisansala Wijekoon

    2015-07-01

    Full Text Available The primary objective of this study is to develop an integrated model to predict corporate failure of listed companies in Sri Lanka. The logistic regression analysis was employed to a data set of 70 matched-pairs of failed and non-failed companies listed in the Colombo Stock Exchange (CSE in Sri Lanka over the period 2002 to 2010. A total of fifteen financial ratios and eight corporate governance variables were used as predictor variables of corporate failure. Analysis of the statistical testing results indicated that model consists with both corporate governance variables and financial ratios improved the prediction accuracy to reach 88.57 per cent one year prior to failure. Furthermore, predictive accuracy of this model in all three years prior to failure is above 80 per cent. Hence model is robust in obtaining accurate results for up to three years prior to failure. It was further found that two financial ratios, working capital to total assets and cash flow from operating activities to total assets, and two corporate governance variables, outside director ratio and company audit committee are having more explanatory power to predict corporate failure. Therefore, model developed in this study can assist investors, managers, shareholders, financial institutions, auditors and regulatory agents in Sri Lanka to forecast corporate failure of listed companies.

  16. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Evanthia E. Tripoliti

    Full Text Available Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years. About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data mining

  17. Sensitivity of probability-of-failure estimates with respect to probability of detection curve parameters

    Energy Technology Data Exchange (ETDEWEB)

    Garza, J. [University of Texas at San Antonio, Mechanical Engineering, 1 UTSA circle, EB 3.04.50, San Antonio, TX 78249 (United States); Millwater, H., E-mail: harry.millwater@utsa.edu [University of Texas at San Antonio, Mechanical Engineering, 1 UTSA circle, EB 3.04.50, San Antonio, TX 78249 (United States)

    2012-04-15

    A methodology has been developed and demonstrated that can be used to compute the sensitivity of the probability-of-failure (POF) with respect to the parameters of inspection processes that are simulated using probability of detection (POD) curves. The formulation is such that the probabilistic sensitivities can be obtained at negligible cost using sampling methods by reusing the samples used to compute the POF. As a result, the methodology can be implemented for negligible cost in a post-processing non-intrusive manner thereby facilitating implementation with existing or commercial codes. The formulation is generic and not limited to any specific random variables, fracture mechanics formulation, or any specific POD curve as long as the POD is modeled parametrically. Sensitivity estimates for the cases of different POD curves at multiple inspections, and the same POD curves at multiple inspections have been derived. Several numerical examples are presented and show excellent agreement with finite difference estimates with significant computational savings. - Highlights: Black-Right-Pointing-Pointer Sensitivity of the probability-of-failure with respect to the probability-of-detection curve. Black-Right-Pointing-Pointer The sensitivities are computed with negligible cost using Monte Carlo sampling. Black-Right-Pointing-Pointer The change in the POF due to a change in the POD curve parameters can be easily estimated.

  18. Periodontal parameters and BANA test in patients with chronic renal failure undergoing hemodialysis

    Science.gov (United States)

    TORRES, Sérgio Aparecido; ROSA, Odila Pereira da Silva; HAYACIBARA, Mitsue Fujimaki; GUIMARÃES, Maria do Carmo Machado; HAYACIBARA, Roberto M.; BRETZ, Walter Antônio

    2010-01-01

    Objectives The aim of this study was to analyze the periodontal parameters of patients with chronic renal failure. Material and Methods The periodontal status of 16 Brazilian patients aged 29 to 53 (41.7±7.2) years with chronic renal failure (CRF) and another matched group of 14 healthy controls with periodontitis was assessed clinically and microbiologically. Probing pocket depth (PPD), gingival recession (GR), dental plaque index (PLI), gingival index (GI), and dental calculus index (CI) were the clinical parameters recorded for the entire dentition (at least 19 teeth), while the anaerobic periodontopathogen colonization in four sites with the highest PPD was evaluated using the BANA test (“PerioScan”; Oral B). Results The results for the CRF group and control group, respectively were: PPD: 1.77±0.32 and 2.65±0.53; GR: 0.58±0.56 and 0.51±0.36; PLI: 1.64±0.56 and 1.24±0.67; GI: 0.64±0.42 and 0.93±0.50; CI: 1.17±0.54 and 0.87±0.52. Comparison between groups using the "t" test revealed a significantly increased PPD (pperiodontal conditions than periodontitis patients, which is an evidence of altered response to local irritants. PMID:20857011

  19. Sensitivity of probability-of-failure estimates with respect to probability of detection curve parameters

    International Nuclear Information System (INIS)

    Garza, J.; Millwater, H.

    2012-01-01

    A methodology has been developed and demonstrated that can be used to compute the sensitivity of the probability-of-failure (POF) with respect to the parameters of inspection processes that are simulated using probability of detection (POD) curves. The formulation is such that the probabilistic sensitivities can be obtained at negligible cost using sampling methods by reusing the samples used to compute the POF. As a result, the methodology can be implemented for negligible cost in a post-processing non-intrusive manner thereby facilitating implementation with existing or commercial codes. The formulation is generic and not limited to any specific random variables, fracture mechanics formulation, or any specific POD curve as long as the POD is modeled parametrically. Sensitivity estimates for the cases of different POD curves at multiple inspections, and the same POD curves at multiple inspections have been derived. Several numerical examples are presented and show excellent agreement with finite difference estimates with significant computational savings. - Highlights: ► Sensitivity of the probability-of-failure with respect to the probability-of-detection curve. ►The sensitivities are computed with negligible cost using Monte Carlo sampling. ► The change in the POF due to a change in the POD curve parameters can be easily estimated.

  20. Estimation Procedure of Common Cause Failure Parameters for CAFE-PSA

    International Nuclear Information System (INIS)

    Kang, Dae Il; Hwang, M. J.; Han, S. H.

    2009-03-01

    Detailed common cause failure (CCF) analysis generally needs the data for CCF events from other nuclear power plants because the CCF events rarely occur. Since 2002, KAERI has participated in the international common cause failure data exchange (ICDE) project to get data for CCF events. The operation office of the ICDE project sent about 400 CCF event data for emergency diesel generators, motor operated valves, check valves, pumps, and breakers to KAERI in 2009. However, there was no program available to analyze the ICDE CCF event data. Therefore, we developed the CAFE-PSA (common CAuse Failure Event analysis program for PSA) to estimate CCF parameters by using the ICDE CCF event data. With CAFE-PSA, the CCF events in the ICDE database can be qualitatively and quantitatively analyzed. The qualitative analysis results of the ICDE CCF data, by using the CAFE-PSA, showed that the major root cause of CCF events, for motor operated valves, check valves, and pumps, was the fault of their internal parts, and that for emergency diesel generators and breakers was the inadequacy of design/manufacture or construction. The quantitative analysis results of the ICDE CCF data, by using the CAFE-PSA, showed that the estimated Alpha Factors of components, mentioned above, were lower than those previously used in the PSA for domestic nuclear power plants, but were higher than those in USNRC 2007 CCF data. Through performing qualitative and quantitative analysis of the ICDE CCF data, by using the CAFE-PSA, a plan for coping with CCF events for design and operation of nuclear power plants can be produced and reasonable values for CCF parameters can be estimated. In addition, it is expected that the technical adequacy of PSA can be improved

  1. Representative parameter of immunostimulatory ginseng polysaccharide to predict radioprotection

    Energy Technology Data Exchange (ETDEWEB)

    Son, Hyeog Jin; Shim, Ji Young; Ahn, Ji Yeon; Yun, Yeon Sook; Song, Jie Young [Korea Institute of Radiological and Medical Sciences, Seoul (Korea, Republic of)

    2008-09-15

    According to the increase in the use of radiotherapy to cancer patients, many approaches have been tried to develop new agents for the protection of surrounding normal tissues. However, it is still few applied in the clinic as a radioprotector. We aim to find a representative parameter for radioprotection to easily predict the activity of in vivo experiment from the results of in vitro screening. The polysaccharide extracted from Panax ginseng was used in this study because the immunostimulator has been regarded as one of the radioprotective agent category and was already reported having a promising radioprotective activity through the increase of hematopoietic cells and the production of several cytokines. Mitogenic activity, AK cells activity and nitric oxide production were monitored for the in vitro immunological assay, and endogenous Colony-Forming Unit (e-CFU) was measured as in vivo radioprotective parameter. The immunological activity was increased by the galactose contents of ginseng polysaccharide dependently. The result of this study suggests that mitogenic activity of splenocytes demonstrated a good correlation with in vivo radioprotective effect, and may be used as a representative parameter to screen the candidates for radioprotector.

  2. Do Urinary Cystine Parameters Predict Clinical Stone Activity?

    Science.gov (United States)

    Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

    2018-02-01

    An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p stone activity (r = -0.29, p r = -0.88, p r = -0.87, p stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  3. Influence of some fabrication parameters and operating conditions on the PCI failure occurrence in LWR fuel rods

    International Nuclear Information System (INIS)

    Bouffioux, P.

    1980-01-01

    In recent LWR designs, the fuel rod failures are induced by a chemically assisted mechanical process, i.e. stress corrosion cracking. The analytical approach towards the analysis of PCI-SCC failures is mainly based on the predictions of the COMETHE code. The failure criteria rely on the concept of a stress threshold together with fission product availability. In the present paper, the use of the COMETHE code to minimize PCI induced clad failure occurrences is illustrated by parametric studies to define acceptable fuel specifications and reactor operating conditions (steady and transient). (author)

  4. Automatic stimulation of experiments and learning based on prediction failure recognition

    NARCIS (Netherlands)

    Juarez Cordova, A.G.; Kahl, B.; Henne, T.; Prassler, E.

    2009-01-01

    In this paper we focus on the task of automatically and autonomously initiating experimentation and learning based on the recognition of prediction failure. We present a mechanism that utilizes conceptual knowledge to predict the outcome of robot actions, observes their execution and indicates when

  5. Assessment of the de Hirsch Predictive Index Tests of Reading Failure.

    Science.gov (United States)

    Askov, Warren; And Others

    The predictive validity and the general usability of a battery of 10 tests reported by de Hirsch, Jansky, and Langford, the de Hirsch Predictive Index Tests of reading failure, were examined. The de Hirsch battery was administered to 433 kindergarten children in six public schools. When the pupils entered first grade, the Metropolitan Readiness…

  6. Corporate Failure Prediction of Public Listed Companies in Malaysia

    Directory of Open Access Journals (Sweden)

    Qaiser Rafique Yasser

    2015-02-01

    Full Text Available This paper aims to extent the prediction model of financial distress among Malaysian public listed companies from period 2006 to 2010. Altman Z-Score Models was used to identify classification on three main zones which are safe, grey or distress zone. The results specify that 56 % of listed companies were classified as ‘distress zone’, 24 % are known as ‘grey zone’ while 20 % are classified in ‘safe zone’. Two likely to fail companies was correctly predict at distress zone which Z-Score was lower than 1.81. Moreover, the findings show most of the companies were facing financial distress during global financial crisis on 2008. Industrial transportation and industrial engineering sectors are generally classified as ‘safe zone’ while food and staplers retailing, real estate investment and services and industrial metals and mining sectors are classified as ‘distress zone’.

  7. Serum Procalcitonin and Peripheral Venous Lactate for Predicting Dengue Shock and/or Organ Failure: A Prospective Observational Study.

    Directory of Open Access Journals (Sweden)

    Vipa Thanachartwet

    2016-08-01

    Full Text Available Currently, there are no biomarkers that can predict the incidence of dengue shock and/or organ failure, although the early identification of risk factors is important in determining appropriate management to reduce mortality. Therefore, we sought to determine the factors associated with dengue shock and/or organ failure and to evaluate the prognostic value of serum procalcitonin (PCT and peripheral venous lactate (PVL levels as biomarkers of dengue shock and/or organ failure.A prospective observational study was conducted among adults hospitalized for confirmed viral dengue infection at the Hospital for Tropical Diseases in Bangkok, Thailand between October 2013 and July 2015. Data, including baseline characteristics, clinical parameters, laboratory findings, serum PCT and PVL levels, management, and outcomes, were recorded on pre-defined case report forms. Of 160 patients with dengue, 128 (80.0% patients had dengue without shock or organ failure, whereas 32 (20.0% patients developed dengue with shock and/or organ failure. Using a stepwise multivariate logistic regression analysis, PCT ≥0.7 ng/mL (odds ratio [OR]: 4.80; 95% confidence interval [CI]: 1.60-14.45; p = 0.005 and PVL ≥2.5 mmol/L (OR: 27.99, 95% CI: 8.47-92.53; p <0.001 were independently associated with dengue shock and/or organ failure. A combination of PCT ≥0.7 ng/mL and PVL ≥2.5 mmol/L provided good prognostic value for predicting dengue shock and/or organ failure, with an area under the receiver operating characteristics curve of 0.83 (95% CI: 0.74-0.92, a sensitivity of 81.2% (95% CI: 63.6-92.8%, and a specificity of 84.4% (95% CI: 76.9-90.2%. Dengue shock patients with non-clearance of PCT and PVL expired during hospitalization.PCT ≥0.7 ng/mL and PVL ≥2.5 mmol/L were independently associated with dengue shock and/or organ failure. The combination of PCT and PVL levels could be used as prognostic biomarkers for the prediction of dengue shock and/or organ failure.

  8. Toward a predictive model for the failure of elastomer seals.

    Science.gov (United States)

    Molinari, Nicola; Khawaja, Musab; Sutton, Adrian; Mostofi, Arash; Baker Hughes Collaboration

    Nitrile butadiene rubber (NBR) and hydrogenated-NBR (HNBR) are widely used elastomers, especially as seals in oil and gas industry. During exposure to the extreme temperatures and pressures typical of well-hole conditions, ingress of gases causes degradation of performance, including mechanical failure. Using computer simulations, we investigate this problem at two different length- and time-scales. First, starting with our model of NBR based on the OPLS all-atom force-field, we develop a chemically-inspired description of HNBR, where C=C double bonds are saturated with either hydrogen or intramolecular cross-links, mimicking the hydrogenation of NBR to form HNBR. We validate against trends for the mass density and glass transition temperature for HNBR as a function of cross-link density, and for NBR as a function of the fraction of acrylonitrile in the copolymer. Second, a coarse-grained approach is taken in order to study mechanical behaviour and to overcome the length- and time-scale limitations inherent to the all-atom model. The effect of nanoparticle fillers added to the elastomer matrix is investigated. Our initial focus is on understanding the mechanical properties at the elevated temperatures and pressures experienced in well-hole conditions. Baker Hughes.

  9. A new model for anisotropic damage in concrete and its application to the prediction of failure of some containment vessel

    International Nuclear Information System (INIS)

    Badel, P.-B.; Godard, V.; Leblond, J.-B.

    2005-01-01

    The aim of this paper is to propose a new model for damage in concrete structures which incorporates such complex features as damage anisotropy and asymmetry between tension and compression, while being expressed in a format well suited for numerical applications and involving a limited number of material parameters which can be determined from standard experiments. A crude version of the model involving a single tonsorial internal variable representing damage in tension, and a single material parameter, is presented first. The predictions of this simple model are satisfactory in simple tension, but not so in simple compression. As a remedy, various refinements are then introduced in a second version of the model involving an additional tonsorial or scalar internal variable representing damage in compression, and five additional material parameters. An example of determination of the model parameters using experimental stress-strain curves in simple tension and compression, plus failure envelopes in biaxial tension/compression, is presented next. The model is finally applied to the numerical prediction of the failure of some containment vessel subjected to some large internal pressure, with a comparison with calculations based on a simpler isotropic variant of the model using a single scalar damage variable. The results illustrate the relevance of models incorporating both asymmetry between tension and compression and anisotropy of damage for simulations of industrial concrete structures. (authors)

  10. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    Science.gov (United States)

    Anggraeni, Novia Antika

    2015-04-01

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.

  11. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    International Nuclear Information System (INIS)

    Anggraeni, Novia Antika

    2015-01-01

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days

  12. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    Energy Technology Data Exchange (ETDEWEB)

    Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com [Geophysics Sub-department, Physics Department, Faculty of Mathematic and Natural Science, Universitas Gadjah Mada. BLS 21 Yogyakarta 55281 (Indonesia)

    2015-04-24

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.

  13. Medical and Periodontal Clinical Parameters in Patients at Different Levels of Chronic Renal Failure

    Directory of Open Access Journals (Sweden)

    Caroline Perozini

    2017-01-01

    Full Text Available Aim. To assess the clinical periodontal and medical parameters in patients with chronic renal failure (CRF at different levels of renal disease. Background. CRF is a progressive and irreversible loss of renal function associated with a decline in the glomerular filtration rate. Periodontal disease is a destructive inflammatory disease affecting periodontal tissues that shows high prevalence in patients with CRF. Materials and Methods. 102 CRF patients were included and divided into an early stage group (EG, predialysis group (PDG, and hemodialysis group (HDG. The medical parameters were taken from the patients’ records. Results. Periodontal clinical condition differed among the CRF groups. Clinical attachment loss was greater in the HDG and PDG group compared to the EG (p=0.0364; the same was observed in the Plaque Index (p=0.0296; the others periodontal parameters did not show any differences. Ferritin levels were significantly higher in the HDG when compared to the EG and PGD (p<0.0001, and fibrinogen was higher in PDG compared with the others (p<0.0001; the triglycerides also showed higher values in the HDG compared with the other groups (p<0.0001. Conclusion. The patients with renal involvement should have a multidisciplinary approach to an improvement in their oral and systemic health.

  14. Possibilities And Influencing Parameters For The Early Detection Of Sheet Metal Failure In Press Shop Operations

    International Nuclear Information System (INIS)

    Gerlach, Joerg; Kessler, Lutz; Paul, Udo; Roesen, Hartwig

    2007-01-01

    The concept of forming limit curves (FLC) is widely used in industrial practice. The required data should be delivered for typical material properties (measured on coils with properties in a range of +/- of the standard deviation from the mean production values) by the material suppliers. In particular it should be noted that its use for the validation of forming robustness providing forming limit curves for the variety of scattering in the mechanical properties is impossible. Therefore a forecast of the expected limit strains without expensive cost and time-consuming experiments is necessary. In the paper the quality of a regression analysis for determining forming limit curves based on tensile test results is presented and discussed.Owing to the specific definition of limit strains with FLCs following linear strain paths, the significance of this failure definition is limited. To consider nonlinear strain path effects, different methods are given in literature. One simple method is the concept of limit stresses. It should be noted that the determined value of the critical stress is dependent on the extrapolation of the tensile test curve. When the yield curve extrapolation is very similar to an exponential function, the definition of the critical stress value is very complicated due to the low slope of the hardening function at large strains.A new method to determine general failure behavior in sheet metal forming is the common use and interpretation of three criteria: onset on material instability (comparable with FLC concept), value of critical shear fracture and the value of ductile fracture. This method seems to be particularly successful for newly developed high strength steel grades in connection with more complex strain paths for some specific material elements. Nevertheless the identification of the different failure material parameters or functions will increase and the user has to learn with the interpretation of the numerical results

  15. TNF Receptor 1/2 Predict Heart Failure Risk in Type 2 Diabetes Mellitus Patients.

    Science.gov (United States)

    Ping, Zhang; Aiqun, Ma; Jiwu, Li; Liang, Shao

    2017-04-06

    Inflammation plays an important role in heart failure and diabetes mellitus. Traditional serum markers have limited predictive value in heart failure and diabetes. TNFR1 and TNFR2 (TNFR1/2) have been proven to be strongly associated with heart failure and diabetes complications. This study aimed to assess the association of sTNFR1 and sTNFR2 levels and incidental HF risk in diabetes patients.We detected the mRNA, protein, and serum expression of TNFR1/2, their downstream signaling pathway protein NF-kB, and JNK expression and some traditional serum inflammatory markers in a heart failure group without diabetes mellitus or abnormal glucose tolerance (n = 84), a diabetes mellitus group without heart failure (n = 86), and a heart failure with diabetes mellitus group (n = 86).TNFR1/2 were significantly higher in patients with heart failure and diabetes mellitus based on mRNA expression to protein expression and serum expression. However, there were no differences in mRNA, protein, and serum levels of TNFR1/2 between the HF group and DM group. Furthermore, there were no differences between the groups in some traditional serum inflammatory markers.This study demonstrated higher expressions of TNFR, NF-kB, and JNK in patients with heart failure and diabetes mellitus. Compared with traditional serum markers, TNFR1 and TNFR2 are associated with heart failure risk in type 2 diabetes mellitus patients.

  16. Compressive failure modes and parameter optimization of the trabecular structure of biomimetic fully integrated honeycomb plates.

    Science.gov (United States)

    Chen, Jinxiang; Tuo, Wanyong; Zhang, Xiaoming; He, Chenglin; Xie, Juan; Liu, Chang

    2016-12-01

    To develop lightweight biomimetic composite structures, the compressive failure and mechanical properties of fully integrated honeycomb plates were investigated experimentally and through the finite element method. The results indicated that: fracturing of the fully integrated honeycomb plates primarily occurred in the core layer, including the sealing edge structure. The morphological failures can be classified into two types, namely dislocations and compactions, and were caused primarily by the stress concentrations at the interfaces between the core layer and the upper and lower laminations and secondarily by the disordered short-fiber distribution in the material; although the fully integrated honeycomb plates manufactured in this experiment were imperfect, their mass-specific compressive strength was superior to that of similar biomimetic samples. Therefore, the proposed bio-inspired structure possesses good overall mechanical properties, and a range of parameters, such as the diameter of the transition arc, was defined for enhancing the design of fully integrated honeycomb plates and improving their compressive mechanical properties. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Failure prediction of low-carbon steel pressure vessel and cylindrical models

    International Nuclear Information System (INIS)

    Zhang, K.D.; Wang, W.

    1987-01-01

    The failure loads predicted by failure assessment methods (namely the net-section stress criterion; the EPRI engineering approach for elastic-plastic analysis; the CEGB failure assessment route; the modified R6 curve by Milne for strain hardening; and the failure assessment curve based on J estimation by Ainsworth) have been compared with burst test results on externally, axially sharp notched pressure vessel and open-ended cylinder models made from typical low-carbon steel St45 seamless tube which has a transverse true stress-strain curve of straight-line and parabola type and a high value of ultimate strength to yield. It was concluded from the comparison that whilst the net-section stress criterion and the CEGB route did not give conservative predictions, Milne's modified curve did give a conservative and good prediction; Ainsworth's curve gave a fairly conservative prediction; and EPRI solutions also could conditionally give a good prediction but the conditions are still somewhat uncertain. It is suggested that Milne's modified R6 curve is used in failure assessment of low-carbon steel pressure vessels. (author)

  18. Profound Endothelial Damage Predicts Impending Organ Failure and Death in Sepsis

    DEFF Research Database (Denmark)

    Johansen, Maria E; Johansson, Pär I.; Ostrowski, Sisse R

    2015-01-01

    levels at enrollment predicted risk of multiple organ failure during follow-up (HR [> 14 ng/mL vs. organ failure and death in septic......Endothelial damage contributes to organ failure and mortality in sepsis, but the extent of the contribution remains poorly quantified. Here, we examine the association between biomarkers of superficial and profound endothelial damage (syndecan-1 and soluble thrombomodulin [sTM], respectively......), organ failure, and death in sepsis. The data from a clinical trial, including critically ill patients predominantly suffering sepsis (Clinicaltrials.gov: NCT00271752) were studied. Syndecan-1 and sTM levels at the time of study enrollment were determined. The predictive ability of biomarker levels...

  19. Identifying the necessary and sufficient number of risk factors for predicting academic failure.

    Science.gov (United States)

    Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

    2012-03-01

    Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  20. Probability of failure prediction for step-stress fatigue under sine or random stress

    Science.gov (United States)

    Lambert, R. G.

    1979-01-01

    A previously proposed cumulative fatigue damage law is extended to predict the probability of failure or fatigue life for structural materials with S-N fatigue curves represented as a scatterband of failure points. The proposed law applies to structures subjected to sinusoidal or random stresses and includes the effect of initial crack (i.e., flaw) sizes. The corrected cycle ratio damage function is shown to have physical significance.

  1. Induction of labour: clinical predictive factors for success and failure.

    Science.gov (United States)

    Batinelli, Laura; Serafini, Andrea; Nante, Nicola; Petraglia, Felice; Severi, Filiberto Maria; Messina, Gabriele

    2018-04-01

    Induction of labour (IOL) is a widely-used practice in obstetrics. Our aim was to evaluate predictors of vaginal delivery in postdate pregnancies induced with prostaglandins. We conducted a retrospective cross-sectional study with analytic component. A total of 145 women, admitted for IOL after the 41st week of gestation, were induced with a vaginal pessary releasing prostaglandins. Type of delivery, whether vaginal or caesarean, was the outcome. Several maternal and foetal variables were investigated. The Kaplan-Maier curves, monovariate and a multivariate logistic regression were carried out. In our population, 80.7% of women had vaginal delivery after the induction. Multiparity and a high Bishop score at the beginning of the IOL were protective factors for a vaginal delivery (respectively OR 0.16, p = .028 and OR 0.62, p = .034) while age >35 years, and the foetal birth weight >3500 g at the birth, resulted in being risk factors for caesarean section (respectively OR 4.20, p = .006 and OR 3.63, p = .013). IMPACT STATEMENT What is already known on this subject: Induction of labour (IOL) is a widely used practice in obstetrics. Scientific literature shows several predictors of successful induction, although there is no unanimity except for 'multiparity' and 'favourable Bishop score' which are associated with positive outcome of the induction. The main difficulty in finding other predictive factors is the heterogeneity of this field (different local protocols in each hospital, type of induction, populations and outcomes chosen in each study). In addition to that, populations are not always comparable due to the different gestation. For this reason, we decided to select a specific population of women, such as low risk postterm pregnancies induced with prostaglandins, in order to detect possible predictive factors for the success of the IOL for women with uncomplicated pregnancies. What the results of this study add: Our study agrees with existing

  2. A micromechanical four-phase model to predict the compressive failure surface of cement concrete

    Directory of Open Access Journals (Sweden)

    A. Caporale,

    2014-07-01

    Full Text Available In this work, a micromechanical model is used in order to predict the failure surface of cement concrete subject to multi-axial compression. In the adopted model, the concrete material is schematised as a composite with the following constituents: coarse aggregate (gravel, fine aggregate (sand and cement paste. The cement paste contains some voids which grow during the loading process. In fact, the non-linear behavior of the concrete is attributed to the creation of cracks in the cement paste; the effect of the cracks is taken into account by introducing equivalent voids (inclusions with zero stiffness in the cement paste. The three types of inclusions (namely gravel, sand and voids have different scales, so that the overall behavior of the concrete is obtained by the composition of three different homogenizations; in the sense that the concrete is regarded as the homogenized material of the two-phase composite constituted of the gravel and the mortar; in turn, the mortar is the homogenized material of the two-phase composite constituted of the sand inclusions and a (porous cement paste matrix; finally, the (porous cement paste is the homogenized material of the two-phase composite constituted of voids and the pure paste. The pure paste represents the cement paste before the loading process, so that it does not contain voids or other defects due to the loading process. The abovementioned three homogenizations are realized with the predictive scheme of Mori-Tanaka in conjunction with the Eshelby method. The adopted model can be considered an attempt to find micromechanical tools able to capture peculiar aspects of the cement concrete in load cases of uni-axial and multi-axial compression. Attributing the non-linear behavior of concrete to the creation of equivalent voids in the cement paste provides correspondence with many phenomenological aspects of concrete behavior. Trying to improve this correspondence, the influence of the parameters of the

  3. Sonographical predictive markers of failure of induction of labour in term pregnancy.

    Science.gov (United States)

    Brik, Maia; Mateos, Silvia; Fernandez-Buhigas, Irene; Garbayo, Paloma; Costa, Gloria; Santacruz, Belen

    2017-02-01

    Predictive markers of failure of induction of labour in term pregnancy were evaluated. A prospective study including 245 women attending induction of labour was performed. The inclusion criteria were singleton pregnancies, gestational age 37-42 weeks and the main outcomes were failure of induction, induction to delivery interval and mode of delivery. Women with a longer cervical length prior to induction (CLpi) had a higher rate of failure of induction (30.9 ± 6.8 vs. 23.9 ± 9.3, p labour.

  4. Factors Predicting Treatment Failure in Patients Treated with Iodine-131 for Graves’ Disease

    International Nuclear Information System (INIS)

    Manohar, Kuruva; Mittal, Bhagwant Rai; Bhoil, Amit; Bhattacharya, Anish; Dutta, Pinaki; Bhansali, Anil

    2013-01-01

    Treatment of Graves' disease with iodine-131 ( 131 I) is well-known; however, all patients do not respond to a single dose of 131 I and may require higher and repeated doses. This study was carried out to identify the factors, which can predict treatment failure to a single dose of 131 I treatment in these patients. Data of 150 patients with Graves' disease treated with 259-370 MBq of 131 I followed-up for at least 1-year were retrospectively analyzed. Logistic regression analysis was used to predict factors which can predict treatment failure, such as age, sex, duration of disease, grade of goiter, duration of treatment with anti-thyroid drugs, mean dosage of anti-thyroid drugs used, 99m Tc-pertechnetate ( 99m TcO 4 - ) uptake at 20 min, dose of 131 I administered, total triiodothyronine and thyroxine levels. Of the 150 patients, 25 patients required retreatment within 1 year of initial treatment with 131 I. Logistic regression analysis revealed that male sex and 99m TcO 4 - uptake were associated with treatment failure. On receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) was significant for 99m TcO 4 - uptake predicting treatment failure (AUC = 0.623; P = 0.039). Optimum cutoff for 99m TcO 4 - uptake was 17.75 with a sensitivity of 68% and specificity of 66% to predict treatment failure. Patients with >17.75% 99m TcO 4 - uptake had odds ratio of 3.14 (P = 0.014) for treatment failure and male patients had odds ratio of 1.783 for treatment failure. Our results suggest that male patients and patients with high pre-treatment 99m TcO 4 - uptake are more likely to require repeated doses of 131 I to achieve complete remission

  5. Spontaneous breathing test in the prediction of extubation failure in the pediatric population.

    Science.gov (United States)

    Nascimento, Milena Siciliano; Rebello, Celso Moura; Vale, Luciana Assis Pires Andrade; Santos, Érica; Prado, Cristiane do

    2017-01-01

    To assess whether the spontaneous breathing test can predict the extubation failure in pediatric population. A prospective and observational study that evaluated data of inpatients at the Pediatric Intensive Care Unit between May 2011 and August 2013, receiving mechanical ventilation for at least 24 hours followed by extubation. The patients were classified in two groups: Test Group, with patients extubated after spontaneous breathing test, and Control Group, with patients extubated without spontaneous breathing test. A total of 95 children were enrolled in the study, 71 in the Test Group and 24 in the Control Group. A direct comparison was made between the two groups regarding sex, age, mechanical ventilation time, indication to start mechanical ventilation and respiratory parameters before extubation in the Control Group, and before the spontaneous breathing test in the Test Group. There was no difference between the parameters evaluated. According to the analysis of probability of extubation failure between the two groups, the likelihood of extubation failure in the Control Group was 1,412 higher than in the Test Group, nevertheless, this range did not reach significance (p=0.706). This model was considered well-adjusted according to the Hosmer-Lemeshow test (p=0.758). The spontaneous breathing test was not able to predict the extubation failure in pediatric population. Avaliar se o teste de respiração espontânea pode ser utilizado para predizer falha da extubação na população pediátrica. Estudo prospectivo, observacional, no qual foram avaliados todos os pacientes internados no Centro de Terapia Intensiva Pediátrica, no período de maio de 2011 a agosto de 2013, que utilizaram ventilação mecânica por mais de 24 horas e que foram extubados. Os pacientes foram classificados em dois grupos: Grupo Teste, que incluiu os pacientes extubados depois do teste de respiração espontânea; e Grupo Controle, pacientes foram sem teste de respiração espont

  6. Chairside CAD/CAM materials. Part 3: Cyclic fatigue parameters and lifetime predictions.

    Science.gov (United States)

    Wendler, Michael; Belli, Renan; Valladares, Diana; Petschelt, Anselm; Lohbauer, Ulrich

    2018-06-01

    Chemical and mechanical degradation play a key role on the lifetime of dental restorative materials. Therefore, prediction of their long-term performance in the oral environment should base on fatigue, rather than inert strength data, as commonly observed in the dental material's field. The objective of the present study was to provide mechanistic fatigue parameters of current dental CAD/CAM materials under cyclic biaxial flexure and assess their suitability in predicting clinical fracture behaviors. Eight CAD/CAM materials, including polycrystalline zirconia (IPS e.max ZirCAD), reinforced glasses (Vitablocs Mark II, IPS Empress CAD), glass-ceramics (IPS e.max CAD, Suprinity PC, Celtra Duo), as well as hybrid materials (Enamic, Lava Ultimate) were evaluated. Rectangular plates (12×12×1.2mm 3 ) with highly polished surfaces were prepared and tested in biaxial cyclic fatigue in water until fracture using the Ball-on-Three-Balls (B3B) test. Cyclic fatigue parameters n and A* were obtained from the lifetime data for each material and further used to build SPT diagrams. The latter were used to compare in-vitro with in-vivo fracture distributions for IPS e.max CAD and IPS Empress CAD. Susceptibility to subcritical crack growth under cyclic loading was observed for all materials, being more severe (n≤20) in lithium-based glass-ceramics and Vitablocs Mark II. Strength degradations of 40% up to 60% were predicted after only 1 year of service. Threshold stress intensity factors (K th ) representing the onset of subcritical crack growth (SCG), were estimated to lie in the range of 0.37-0.44 of K Ic for the lithium-based glass-ceramics and Vitablocs Mark II and between 0.51-0.59 of K Ic for the other materials. Failure distributions associated with mechanistic estimations of strength degradation in-vitro showed to be useful in interpreting failure behavior in-vivo. The parameter K th stood out as a better predictor of clinical performance in detriment to the SCG n

  7. Value of Serial Heart Rate Variability Measurement for Prediction of Appropriate ICD Discharge in Patients with Heart Failure

    NARCIS (Netherlands)

    ten Sande, Judith N.; Damman, Peter; Tijssen, Jan G. P.; de Groot, Joris R.; Knops, Reinoud E.; Wilde, Arthur A. M.; van Dessel, Pascal F. H. M.

    2014-01-01

    HRV and Appropriate ICD Shock in Heart Failure Introduction Decreased heart rate variability (HRV) is associated with adverse outcomes in patients with heart failure. Our objective was to examine whether decreased HRV predicts appropriate implantable cardioverter defibrillator (ICD) shocks. Methods

  8. Predicting failure response of spot welded joints using recent extensions to the Gurson model

    DEFF Research Database (Denmark)

    Nielsen, Kim Lau

    2010-01-01

    The plug failure modes of resistance spot welded shear-lab and cross-tension test specimens are studied, using recent extensions to the Gurson model. A comparison of the predicted mechanical response is presented when using either: (i) the Gurson-Tvergaard-Needleman model (GTN-model), (ii...... is presented. The models are applied to predict failure of specimens containing a fully intact weld nugget as well as a partly removed weld nugget to address the problems of shrinkage voids or larger weld defects. All analysis are carried out by full 3D finite element modelling....

  9. SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Iyengar, P; Zhang, Y; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining

  10. Predictive Factors of Respiratory Failure in Children with Guillain-Barre Syndrome

    Directory of Open Access Journals (Sweden)

    Nemat Bilan

    2015-03-01

    Full Text Available Introduction:Guillain-Barre Syndrome(GBS is the most common cause of acute flaccid paralysis. Respiratory failure is the most serious short-term complication of GBS and invasive mechanical ventilation is required in 30% of patients.moreover,60% of those who are intubated develop major complications including pnemonia,sepsis,GI bleeding and pulmonary embolism. Thus respiratory failure prediction is crucial. the aim of this study was to determine clinical predictors of respiratory failure to avoid respiratory distress and aspiration.Methods and materials: in a cross sectional and analytical study 140 patients with clinically diagnosis of Guillain-Barre Syndrome were enrolled in study,from october 2008 to october 2014. .demographic data,nerologic examination,cranial nerve and autonomic nervous system involvement, and respiratory failure were recorded prospectively.Results:15 out of 140 patients(10,7% developed respiratory failure and underwent mechanical ventilation.the male/female ratio in patients with respiratory failure and patients without respiratory involvement were (53%/(47% and (54%/(46% respectively(p-value:0.4.the mean age in these two groups were 2,7±1,9 and 5,5±3,2(p-value:0,003.cranial nerve involvement (7,9,10 was recorded in patients with respiratory failure and without respiratory failure54% and25% respectively (p-value:0,03.absent upper limb deep tendon reflexes in these two groups were 70% and 44% respectively.(p-value:0,03 and autonomic nervous system involvement 24% vs. 14%(p-value:0,3.conclusion : our study suggests that younger age , cranial nerve involvement and absent upper limb deep tendon reflexes are predictive factors of respiratory failure in patients with Guillain-Barre Syndrome(GBS.

  11. Correlation between endogenous polyamines in human cardiac tissues and clinical parameters in patients with heart failure.

    Science.gov (United States)

    Meana, Clara; Rubín, José Manuel; Bordallo, Carmen; Suárez, Lorena; Bordallo, Javier; Sánchez, Manuel

    2016-02-01

    Polyamines contribute to several physiological and pathological processes, including cardiac hypertrophy in experimental animals. This involves an increase in ornithine decarboxylase (ODC) activity and intracellular polyamines associated with cyclic adenosine monophosphate (cAMP) increases. The aim of the study was to establish the role of these in the human heart in living patients. For this, polyamines (by high performance liquid chromatography) and the activity of ODC and N(1)-acetylpolyamine oxidases (APAO) were determined in the right atrial appendage of 17 patients undergoing extracorporeal circulation to correlate with clinical parameters. There existed enzymatic activity associated with the homeostasis of polyamines. Left atria size was positively associated with ODC (r = 0.661, P = 0.027) and negatively with APAO-N(1) -acetylspermine (r = -0.769, P = 0.026), suggesting that increased levels of polyamines are associated with left atrial hemodynamic overload. Left ventricular ejection fraction (LVEF) and heart rate were positively associated with spermidine (r = 0.690, P = 0.003; r = 0.590, P = 0.021) and negatively with N(1)-acetylspermidine (r = -0.554, P = 0.032; r = -0.644, P = 0.018). LVEF was negatively correlated with cAMP levels (r = -0.835, P = 0.001) and with cAMP/ODC (r = -0.794, P = 0.011), cAMP/spermidine (r = -0.813, P = 0.001) and cAMP/spermine (r = -0.747, P = 0.003) ratios. Abnormal LVEF patients showed decreased ODC activity and spermidine, and increased N(1) -acetylspermidine, and cAMP. Spermine decreased in congestive heart failure patients. The trace amine isoamylamine negatively correlated with septal wall thickness (r = -0.634, P = 0.008) and was increased in cardiac heart failure. The results indicated that modifications in polyamine homeostasis might be associated with cardiac function and remodelling. Increased cAMP might have a deleterious effect on function. Further studies should confirm these findings and the involvement of

  12. Differential subsidence and its effect on subsurface infrastructure: predicting probability of pipeline failure (STOOP project)

    Science.gov (United States)

    de Bruijn, Renée; Dabekaussen, Willem; Hijma, Marc; Wiersma, Ane; Abspoel-Bukman, Linda; Boeije, Remco; Courage, Wim; van der Geest, Johan; Hamburg, Marc; Harmsma, Edwin; Helmholt, Kristian; van den Heuvel, Frank; Kruse, Henk; Langius, Erik; Lazovik, Elena

    2017-04-01

    Due to heterogeneity of the subsurface in the delta environment of the Netherlands, differential subsidence over short distances results in tension and subsequent wear of subsurface infrastructure, such as water and gas pipelines. Due to uncertainties in the build-up of the subsurface, however, it is unknown where this problem is the most prominent. This is a problem for asset managers deciding when a pipeline needs replacement: damaged pipelines endanger security of supply and pose a significant threat to safety, yet premature replacement raises needless expenses. In both cases, costs - financial or other - are high. Therefore, an interdisciplinary research team of geotechnicians, geologists and Big Data engineers from research institutes TNO, Deltares and SkyGeo developed a stochastic model to predict differential subsidence and the probability of consequent pipeline failure on a (sub-)street level. In this project pipeline data from company databases is combined with a stochastic geological model and information on (historical) groundwater levels and overburden material. Probability of pipeline failure is modelled by a coupling with a subsidence model and two separate models on pipeline behaviour under stress, using a probabilistic approach. The total length of pipelines (approx. 200.000 km operational in the Netherlands) and the complexity of the model chain that is needed to calculate a chance of failure, results in large computational challenges, as it requires massive evaluation of possible scenarios to reach the required level of confidence. To cope with this, a scalable computational infrastructure has been developed, composing a model workflow in which components have a heterogeneous technological basis. Three pilot areas covering an urban, a rural and a mixed environment, characterised by different groundwater-management strategies and different overburden histories, are used to evaluate the differences in subsidence and uncertainties that come with

  13. TBCs for Gas Turbines under Thermomechanical Loadings: Failure Behaviour and Life Prediction

    Directory of Open Access Journals (Sweden)

    Herzog R.

    2012-10-01

    Full Text Available The present contribution gives an overview about recent research on a thermal barrier coating (TBC system consisted of (i an intermetallic MCrAlY-alloy Bondcoat (BC applied by vacuum plasma spraying (VPS and (ii an Yttria Stabilised Zirconia (YSZ top coat air plasma sprayed (APS at Forschungszentrum Juelich, Institute of Energy and Climate Research (IEK-1. The influence of high temperature dwell time, maximum and minimum temperature on crack growth kinetics during thermal cycling of such plasma sprayed TBCs is investigated using infrared pulse thermography (IT, acoustic emission (AE analysis and scanning electron microscopy. Thermocyclic life in terms of accumulated time at maximum temperature decreases with increasing high temperature dwell time and increases with increasing minimum temperature. AE analysis proves that crack growth mainly occurs during cooling at temperatures below the ductile-to-brittle transition temperature of the BC. Superimposed mechanical load cycles accelerate delamination crack growth and, in case of sufficiently high mechanical loadings, result in premature fatigue failure of the substrate. A life prediction model based on TGO growth kinetics and a fracture mechanics approach has been developed which accounts for the influence of maximum and minimum temperature as well as of high temperature dwell time with good accuracy in an extremely wide parameter range.

  14. Influence of Different Yield Loci on Failure Prediction with Damage Models

    Science.gov (United States)

    Heibel, S.; Nester, W.; Clausmeyer, T.; Tekkaya, A. E.

    2017-09-01

    Advanced high strength steels are widely used in the automotive industry to simultaneously improve crash performance and reduce the car body weight. A drawback of these multiphase steels is their sensitivity to damage effects and thus the reduction of ductility. For that reason the Forming Limit Curve is only partially suitable for this class of steels. An improvement in failure prediction can be obtained by using damage mechanics. The objective of this paper is to comparatively review the phenomenological damage model GISSMO and the Enhanced Lemaitre Damage Model. GISSMO is combined with three different yield loci, namely von Mises, Hill48 and Barlat2000 to investigate the influence of the choice of the plasticity description on damage modelling. The Enhanced Lemaitre Model is used with Hill48. An inverse parameter identification strategy for a DP1000 based on stress-strain curves and optical strain measurements of shear, uniaxial, notch and (equi-)biaxial tension tests is applied to calibrate the models. A strong dependency of fracture strains on the choice of yield locus can be observed. The identified models are validated on a cross-die cup showing ductile fracture with slight necking.

  15. The Renal Arterial Resistance Index Predicts Worsening Renal Function in Chronic Heart Failure Patients

    Science.gov (United States)

    Iacoviello, Massimo; Monitillo, Francesco; Leone, Marta; Citarelli, Gaetano; Doronzo, Annalisa; Antoncecchi, Valeria; Puzzovivo, Agata; Rizzo, Caterina; Lattarulo, Maria Silvia; Massari, Francesco; Caldarola, Pasquale; Ciccone, Marco Matteo

    2016-01-01

    Background/Aim The renal arterial resistance index (RRI) is a Doppler measure, which reflects abnormalities in the renal blood flow. The aim of this study was to verify the value of RRI as a predictor of worsening renal function (WRF) in a group of chronic heart failure (CHF) outpatients. Methods We enrolled 266 patients in stable clinical conditions and on conventional therapy. Peak systolic velocity and end diastolic velocity of a segmental renal artery were obtained by pulsed Doppler flow, and RRI was calculated. Creatinine serum levels were evaluated at baseline and at 1 year, and the changes were used to assess WRF occurrence. Results During follow-up, 34 (13%) patients showed WRF. RRI was associated with WRF at univariate (OR: 1.13; 95% CI: 1.07–1.20) as well as at a forward stepwise multivariate logistic regression analysis (OR: 1.09; 95% CI: 1.03–1.16; p = 0.005) including the other univariate predictors. Conclusions Quantification of arterial renal perfusion provides a new parameter that independently predicts the WRF in CHF outpatients. Its possible role in current clinical practice to better define the risk of cardiorenal syndrome progression is strengthened. PMID:27994601

  16. Bulbar impairment score predicts noninvasive volume-cycled ventilation failure during an acute lower respiratory tract infection in ALS.

    Science.gov (United States)

    Servera, Emilio; Sancho, Jesús; Bañuls, Pilar; Marín, Julio

    2015-11-15

    Amyotrophic lateral sclerosis (ALS) patients can suffer episodes of lower respiratory tract infections (LRTI) leading to an acute respiratory failure (ARF) requiring noninvasive ventilation (NIV). To determine whether clinical or functional parameters can predict noninvasive management failure during LRTI causing ARF in ALS. A prospective study involving all ALS patients with ARF requiring NIV in a Respiratory Care Unit. NIV was provided with volume-cycled ventilators. 63 ALS patients were included (APACHE II: 14.93±3.56, Norris bulbar subscore (NBS): 18.78±9.68, ALSFRS-R: 19.90±6.98, %FVC: 40.01±18.07%, MIC: 1.62±0.74L, PCF 2.51±1.15L/s, PImax -34.90±19.44cmH2O, PEmax 51.20±28.84cmH2O). In 73.0% of patients NIV was successful in averting death or endotracheal intubation. Differences were found between the success and failure in the NBS (22.08±6.15 vs 8.66±3.39, pNIV failure was the NBS (OR 0.53, 95% CI 0.31-0.92, p 0.002) with a cut-off point of 12 (S 0.93; E 0.97; PPV 0.76; NPV 0.97). NBS can predict noninvasive management failure during LRTI in ALS. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Prediction of Ductile Failure in the Stretch-Forming of AA2024 Sheets

    International Nuclear Information System (INIS)

    Vallellano, C.; Guzman, C.; Garcia-Lomas, F. J.

    2007-01-01

    A number of ductile failure criteria are nowadays being used to predict the formability of aluminium alloy sheets. Generally speaking, integral criteria (e.g. those proposed by Cockcroft and Latham, Brozzo et al., Oyane et al Chaouadi et al., etc.) have been probed to work well when the principal strains are of opposite sign, i.e. in the left side of the Forming Limit Diagram (FLD). However, when tensile biaxial strains are present, as occurs in stretch-forming practice, their predictions are usually very poor and even non-conservatives. As an alternative, local criteria, such as the classical Tresca's and Bressan and Williams' criteria, have demonstrated a good capability to predict the failure in some automotive aluminum alloys under stretching. The present work analyses experimentally and numerically the failure in AA2024-T3 sheets subjected to biaxial stretching. A series of out-of-plane stretching tests have been simulated using ABAQUS. The experimental and the numerical FLD for different failure criteria are compared. The influence on the failure of the hydrostatic pressure and the normal stress to the fracture plane is also discussed

  18. A model for predicting embankment slope failures in clay-rich soils; A Louisiana example

    Science.gov (United States)

    Burns, S. F.

    2015-12-01

    A model for predicting embankment slope failures in clay-rich soils; A Louisiana example It is well known that smectite-rich soils significantly reduce the stability of slopes. The question is how much smectite in the soil causes slope failures. A study of over 100 sites in north and south Louisiana, USA, compared slopes that failed during a major El Nino winter (heavy rainfall) in 1982-1983 to similar slopes that did not fail. Soils in the slopes were tested for per cent clay, liquid limits, plasticity indices and semi-quantitative clay mineralogy. Slopes with the High Risk for failure (85-90% chance of failure in 8-15 years after construction) contained soils with a liquid limit > 54%, a plasticity index over 29%, and clay contents > 47%. Slopes with an Intermediate Risk (55-50% chance of failure in 8-15 years) contained soils with a liquid limit between 36-54%, plasticity index between 16-19%, and clay content between 32-47%. Slopes with a Low Risk chance of failure (soils with a liquid limit plasticity index soil characteristics before construction. If the soils fall into the Low Risk classification, construct the embankment normally. If the soils fall into the High Risk classification, one will need to use lime stabilization or heat treatments to prevent failures. Soils in the Intermediate Risk class will have to be evaluated on a case by case basis.

  19. Probabilistic Modeling of Updating Epistemic Uncertainty In Pile Capacity Prediction With a Single Failure Test Result

    Directory of Open Access Journals (Sweden)

    Indra Djati Sidi

    2017-12-01

    Full Text Available The model error N has been introduced to denote the discrepancy between measured and predicted capacity of pile foundation. This model error is recognized as epistemic uncertainty in pile capacity prediction. The statistics of N have been evaluated based on data gathered from various sites and may be considered only as a eneral-error trend in capacity prediction, providing crude estimates of the model error in the absence of more specific data from the site. The results of even a single load test to failure, should provide direct evidence of the pile capacity at a given site. Bayes theorem has been used as a rational basis for combining new data with previous data to revise assessment of uncertainty and reliability. This study is devoted to the development of procedures for updating model error (N, and subsequently the predicted pile capacity with a results of single failure test.

  20. A model-based prognostic approach to predict interconnect failure using impedance analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Dae Il; Yoon, Jeong Ah [Dept. of System Design and Control Engineering. Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

    2016-10-15

    The reliability of electronic assemblies is largely affected by the health of interconnects, such as solder joints, which provide mechanical, electrical and thermal connections between circuit components. During field lifecycle conditions, interconnects are often subjected to a DC open circuit, one of the most common interconnect failure modes, due to cracking. An interconnect damaged by cracking is sometimes extremely hard to detect when it is a part of a daisy-chain structure, neighboring with other healthy interconnects that have not yet cracked. This cracked interconnect may seem to provide a good electrical contact due to the compressive load applied by the neighboring healthy interconnects, but it can cause the occasional loss of electrical continuity under operational and environmental loading conditions in field applications. Thus, cracked interconnects can lead to the intermittent failure of electronic assemblies and eventually to permanent failure of the product or the system. This paper introduces a model-based prognostic approach to quantitatively detect and predict interconnect failure using impedance analysis and particle filtering. Impedance analysis was previously reported as a sensitive means of detecting incipient changes at the surface of interconnects, such as cracking, based on the continuous monitoring of RF impedance. To predict the time to failure, particle filtering was used as a prognostic approach using the Paris model to address the fatigue crack growth. To validate this approach, mechanical fatigue tests were conducted with continuous monitoring of RF impedance while degrading the solder joints under test due to fatigue cracking. The test results showed the RF impedance consistently increased as the solder joints were degraded due to the growth of cracks, and particle filtering predicted the time to failure of the interconnects similarly to their actual timesto- failure based on the early sensitivity of RF impedance.

  1. Depression increasingly predicts mortality in the course of congestive heart failure.

    Science.gov (United States)

    Jünger, Jana; Schellberg, Dieter; Müller-Tasch, Thomas; Raupp, Georg; Zugck, Christian; Haunstetter, Armin; Zipfel, Stephan; Herzog, Wolfgang; Haass, Markus

    2005-03-02

    Congestive heart failure (CHF) is frequently associated with depression. However, the impact of depression on prognosis has not yet been sufficiently established. To prospectively investigate the influence of depression on mortality in patients with CHF. In 209 CHF patients depression was assessed by the Hospital Anxiety and Depression Scale (HADS-D). Compared to survivors (n=164), non-survivors (n=45) were characterized by a higher New York Heart Association (NYHA) functional class (2.8+/-0.7 vs. 2.5+/-0.6), and a lower left ventricular ejection fraction (LVEF) (18+/-8 vs. 23+/-10%) and peakVO(2) (13.1+/-4.5 vs. 15.4+/-5.2 ml/kg/min) at baseline. Furthermore, non-survivors had a higher depression score (7.5+/-4.0 vs. 6.1+/-4.3) (all P<0.05). After a mean follow-up of 24.8 months the depression score was identified as a significant indicator of mortality (P<0.01). In multivariate analysis the depression score predicted mortality independent from NYHA functional class, LVEF and peakVO(2). Combination of depression score, LVEF and peakVO(2) allowed for a better risk stratification than combination of LVEF and peakVO(2) alone. The risk ratio for mortality in patients with an elevated depression score (i.e. above the median) rose over time to 8.2 after 30 months (CI 2.62-25.84). The depression score predicts mortality independent of somatic parameters in CHF patients not treated for depression. Its prognostic power increases over time and should, thus, be accounted for in risk stratification and therapy.

  2. Use of the total motile sperm count to predict total fertilization failure in in vitro fertilization

    NARCIS (Netherlands)

    Repping, Sjoerd; van Weert, Janne-Meije; Mol, Ben W. J.; de Vries, Jan W. A.; van der Veen, Fulco

    2002-01-01

    Objective: To evaluate the capacity of baseline characteristics and total motile sperm count (TMC) to predict total fertilization failure (TIFF) in patients undergoing IVF. Design: Retrospective cohort study. Setting: University hospital. Patient(s): Eight hundred ninety-two couples with a total of

  3. Prediction of Composite Pressure Vessel Failure Location using Fiber Bragg Grating Sensors

    Science.gov (United States)

    Kreger, Steven T.; Taylor, F. Tad; Ortyl, Nicholas E.; Grant, Joseph

    2006-01-01

    Ten composite pressure vessels were instrumented with fiber Bragg grating sensors in order to assess the strain levels of the vessel under various loading conditions. This paper and presentation will discuss the testing methodology, the test results, compare the testing results to the analytical model, and present a possible methodology for predicting the failure location and strain level of composite pressure vessels.

  4. Health Visitor's Role in Prediction of Early Childhood Injuries and Failure to Thrive.

    Science.gov (United States)

    Dean, Janet G.; And Others

    1978-01-01

    Discusses the role of the health visitor in the prediction of early childhood injuries, abuse, and failure to thrive--based on a three-year study of the relationship between early maternal attitudes and subsequent child health. Journal availability: Pergamon Press Ltd., Headington Hill Hall, Oxford, OX3 OBW England. (DLS)

  5. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  6. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    2017-01-01

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  7. Parameter estimation and prediction of nonlinear biological systems: some examples

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2006-01-01

    Rearranging and reparameterizing a discrete-time nonlinear model with polynomial quotient structure in input, output and parameters (xk = f(Z, p)) leads to a model linear in its (new) parameters. As a result, the parameter estimation problem becomes a so-called errors-in-variables problem for which

  8. Predictive parameters of infectiologic complications in patients after TIPSS

    International Nuclear Information System (INIS)

    Cohnen, M.; Saleh, A.; Moedder, U.; Luethen, R.; Bode, J.; Haeussinger, D.; Daeubener, W.

    2003-01-01

    Aim To define predictive parameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/μl) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8±1.0; d1: 3.2±1.5; d7: 4.3±3.2), and white blood cell count (d0: 7700±2600; d1: 10800±2800; d7: 7500±1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6±0.6; d1: 1.8±1.0; d7: 1.9±0.6. WBC: d0: 6900±1500; d1: 8000±1600; d7: 7600±1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [de

  9. Can baseline ultrasound results help to predict failure to achieve DAS28 remission after 1 year of tight control treatment in early RA patients?

    Science.gov (United States)

    Ten Cate, D F; Jacobs, J W G; Swen, W A A; Hazes, J M W; de Jager, M H; Basoski, N M; Haagsma, C J; Luime, J J; Gerards, A H

    2018-01-30

    At present, there are no prognostic parameters unequivocally predicting treatment failure in early rheumatoid arthritis (RA) patients. We investigated whether baseline ultrasonography (US) findings of joints, when added to baseline clinical, laboratory, and radiographical data, could improve prediction of failure to achieve Disease Activity Score assessing 28 joints (DAS28) remission (baseline. Clinical, laboratory, and radiographical parameters were recorded. Primary analysis was the prediction by logistic regression of the absence of DAS28 remission 12 months after diagnosis and start of therapy. Of 194 patients included, 174 were used for the analysis, with complete data available for 159. In a multivariate model with baseline DAS28 (odds ratio (OR) 1.6, 95% confidence interval (CI) 1.2-2.2), the presence of rheumatoid factor (OR 2.3, 95% CI 1.1-5.1), and type of monitoring strategy (OR 0.2, 95% CI 0.05-0.85), the addition of baseline US results for joints (OR 0.96, 95% CI 0.89-1.04) did not significantly improve the prediction of failure to achieve DAS28 remission (likelihood ratio test, 1.04; p = 0.31). In an early RA population, adding baseline ultrasonography of the hands, wrists, and feet to commonly available baseline characteristics did not improve prediction of failure to achieve DAS28 remission at 12 months. Clinicaltrials.gov, NCT01752309 . Registered on 19 December 2012.

  10. Effect of Nebivolol on MIBG Parameters and Exercise in Heart Failure with Normal Ejection Fraction

    Energy Technology Data Exchange (ETDEWEB)

    Messias, Leandro Rocha, E-mail: lmessias@cardiol.br; Ferreira, Aryanne Guimarães; Miranda, Sandra Marina Ribeiro de; Teixeira, José Antônio Caldas [Universidade Federal Fluminense, Rio de Janeiro, RJ (Brazil); Azevedo, Jader Cunha de [Universidade Federal Fluminense, Rio de Janeiro, RJ (Brazil); Hospital Procardíaco, Rio de Janeiro, RJ (Brazil); Messias, Ana Carolina Nader Vasconcelos [Hospital Federal dos Servidores do Estado do Rio de Janeiro, Rio de Janeiro, RJ (Brazil); Maróstica, Elisabeth [Universidade Federal Fluminense, Rio de Janeiro, RJ (Brazil); Mesquita, Claudio Tinoco [Universidade Federal Fluminense, Rio de Janeiro, RJ (Brazil); Hospital Procardíaco, Rio de Janeiro, RJ (Brazil)

    2016-05-15

    More than 50% of the patients with heart failure have normal ejection fraction (HFNEF). Iodine-123 metaiodobenzylguanidine (123I-MIBG) scintigraphy and cardiopulmonary exercise test (CPET) are prognostic markers in HFNEF. Nebivolol is a beta-blocker with vasodilating properties. To evaluate the impact of nebivolol therapy on CPET and123I-MIBG scintigraphic parameters in patients with HFNEF. Twenty-five patients underwent 123I-MIBG scintigraphy to determine the washout rate and early and late heart-to-mediastinum ratios. During the CPET, we analyzed the systolic blood pressure (SBP) response, heart rate (HR) during effort and recovery (HRR), and oxygen uptake (VO{sub 2}). After the initial evaluation, we divided our cohort into control and intervention groups. We then started nebivolol and repeated the tests after 3 months. After treatment, the intervention group showed improvement in rest SBP (149 mmHg [143.5-171 mmHg] versus 135 mmHg [125-151 mmHg, p = 0.016]), rest HR (78 bpm [65.5-84 bpm] versus 64.5 bpm [57.5-75.5 bpm, p = 0.028]), peak SBP (235 mmHg [216.5-249 mmHg] versus 198 mmHg [191-220.5 mmHg], p = 0.001), peak HR (124.5 bpm [115-142 bpm] versus 115 bpm [103.7-124 bpm], p= 0.043), HRR on the 1st minute (6.5 bpm [4.75-12.75 bpm] versus 14.5 bpm [6.7-22 bpm], p = 0.025) and HRR on the 2nd minute (15.5 bpm [13-21.75 bpm] versus 23.5 bpm [16-31.7 bpm], p = 0.005), but no change in peak VO{sub 2} and 123I-MIBG scintigraphic parameters. Despite a better control in SBP, HR during rest and exercise, and improvement in HRR, nebivolol failed to show a positive effect on peak VO2 and 123I-MIBG scintigraphic parameters. The lack of effect on adrenergic activity may be the cause of the lack of effect on functional capacity.

  11. Effect of Nebivolol on MIBG Parameters and Exercise in Heart Failure with Normal Ejection Fraction

    International Nuclear Information System (INIS)

    Messias, Leandro Rocha; Ferreira, Aryanne Guimarães; Miranda, Sandra Marina Ribeiro de; Teixeira, José Antônio Caldas; Azevedo, Jader Cunha de; Messias, Ana Carolina Nader Vasconcelos; Maróstica, Elisabeth; Mesquita, Claudio Tinoco

    2016-01-01

    More than 50% of the patients with heart failure have normal ejection fraction (HFNEF). Iodine-123 metaiodobenzylguanidine (123I-MIBG) scintigraphy and cardiopulmonary exercise test (CPET) are prognostic markers in HFNEF. Nebivolol is a beta-blocker with vasodilating properties. To evaluate the impact of nebivolol therapy on CPET and123I-MIBG scintigraphic parameters in patients with HFNEF. Twenty-five patients underwent 123I-MIBG scintigraphy to determine the washout rate and early and late heart-to-mediastinum ratios. During the CPET, we analyzed the systolic blood pressure (SBP) response, heart rate (HR) during effort and recovery (HRR), and oxygen uptake (VO 2 ). After the initial evaluation, we divided our cohort into control and intervention groups. We then started nebivolol and repeated the tests after 3 months. After treatment, the intervention group showed improvement in rest SBP (149 mmHg [143.5-171 mmHg] versus 135 mmHg [125-151 mmHg, p = 0.016]), rest HR (78 bpm [65.5-84 bpm] versus 64.5 bpm [57.5-75.5 bpm, p = 0.028]), peak SBP (235 mmHg [216.5-249 mmHg] versus 198 mmHg [191-220.5 mmHg], p = 0.001), peak HR (124.5 bpm [115-142 bpm] versus 115 bpm [103.7-124 bpm], p= 0.043), HRR on the 1st minute (6.5 bpm [4.75-12.75 bpm] versus 14.5 bpm [6.7-22 bpm], p = 0.025) and HRR on the 2nd minute (15.5 bpm [13-21.75 bpm] versus 23.5 bpm [16-31.7 bpm], p = 0.005), but no change in peak VO 2 and 123I-MIBG scintigraphic parameters. Despite a better control in SBP, HR during rest and exercise, and improvement in HRR, nebivolol failed to show a positive effect on peak VO2 and 123I-MIBG scintigraphic parameters. The lack of effect on adrenergic activity may be the cause of the lack of effect on functional capacity

  12. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    International Nuclear Information System (INIS)

    Xu Ruirui; Chen Tianlun; Gao Chengfeng

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  13. Metallic ureteral stents in malignant ureteral obstruction: clinical factors predicting stent failure.

    Science.gov (United States)

    Chow, Po-Ming; Hsu, Jui-Shan; Huang, Chao-Yuan; Wang, Shuo-Meng; Lee, Yuan-Ju; Huang, Kuo-How; Yu, Hong-Jheng; Pu, Yeong-Shiau; Liang, Po-Chin

    2014-06-01

    To provide clinical outcomes of the Resonance metallic ureteral stent in patients with malignant ureteral obstruction, as well as clinical factors predicting stent failure. Cancer patients who have received Resonance stents from July 2009 to March 2012 for ureteral obstruction were included for chart review. Stent failure was detected by clinical symptoms, image studies, and renal function tests. Survival analysis for stent duration was used to estimate patency rate and factors predicting stent failure. A total of 117 stents were inserted successfully into 94 ureteral units in 79 patients. There were no major complications. These stents underwent survival analysis and proportional hazard regression. The median duration for the stents was 5.77 months. In multivariate analysis, age (P=0.043), preoperative serum creatinine level (P=0.0174), and cancer type (P=0.0494) were significant factors associated with stent failure. Cancer treatment before and after stent insertion had no effect on stent duration. Resonance stents are effective and safe in relieving malignant ureteral obstructions. Old age and high serum creatinine level are predictors for stent failure. Stents in patients with lower gastrointestinal cancers have longer functional duration.

  14. Reliability prediction system based on the failure rate model for electronic components

    International Nuclear Information System (INIS)

    Lee, Seung Woo; Lee, Hwa Ki

    2008-01-01

    Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. Among the reliability prediction methods are the statistical analysis based method, the similarity analysis method based on an external failure rate database, and the method based on the physics-of-failure model. In this study, we developed a system by which the reliability of electronic components can be predicted by creating a system for the statistical analysis method of predicting reliability most easily. The failure rate models that were applied are MILHDBK- 217F N2, PRISM, and Telcordia (Bellcore), and these were compared with the general purpose system in order to validate the effectiveness of the developed system. Being able to predict the reliability of electronic components from the stage of design, the system that we have developed is expected to contribute to enhancing the reliability of electronic components

  15. Reliability prediction of engineering systems with competing failure modes due to component degradation

    International Nuclear Information System (INIS)

    Son, Young Kap

    2011-01-01

    Reliability of an engineering system depends on two reliability metrics: the mechanical reliability, considering component failures, that a functional system topology is maintained and the performance reliability of adequate system performance in each functional configuration. Component degradation explains not only the component aging processes leading to failure in function, but also system performance change over time. Multiple competing failure modes for systems with degrading components in terms of system functionality and system performance are considered in this paper with the assumption that system functionality is not independent of system performance. To reduce errors in system reliability prediction, this paper tries to extend system performance reliability prediction methods in open literature through combining system mechanical reliability from component reliabilities and system performance reliability. The extended reliability prediction method provides a useful way to compare designs as well as to determine effective maintenance policy for efficient reliability growth. Application of the method to an electro-mechanical system, as an illustrative example, is explained in detail, and the prediction results are discussed. Both mechanical reliability and performance reliability are compared to total system reliability in terms of reliability prediction errors

  16. Tumor Necrosis Factor Inhibitor Primary Failure Predicts Decreased Ustekinumab Efficacy in Psoriasis Patients.

    Science.gov (United States)

    Sorensen, Eric P; Fanucci, Kristina A; Saraiya, Ami; Volf, Eva; Au, Shiu-chung; Argobi, Yahya; Mansfield, Ryan; Gottlieb, Alice B

    2015-08-01

    Additional studies are needed to examine the efficacy of ustekinumab in psoriasis patients who have previously been exposed to tumor necrosis factor inhibitors (TNFi). To examine the predictive effect of TNFi primary failure and the number of TNFi exposures on the efficacy of ustekinumab in psoriasis treatment. This retrospective study examined 44 psoriasis patients treated at the Tufts Medical Center Department of Dermatology between January 2008 and July 2014. Patients were selected if they were treated with ustekinumab and had ≥ 1 previous TNFi exposure. The following subgroups were compared: patients with vs without a previous TNFi primary failure, and patients with one vs multiple previous TNFi exposures. The efficacy measure used was the previously validated Simple Measure for Assessing Psoriasis Activity (S-MAPA), which is calculated by the product of the body surface area and physician global assessment. The primary outcome was the percentage improvement S-MAPA from course baseline at week 12 of ustekinumab treatment. Secondary outcomes were the psoriasis clearance, primary failure, and secondary failure rates with ustekinumab treatment. Patients with a previous TNFi primary failure had a significantly lower percentage improvement in S-MAPA score at week 12 of ustekinumab treatment compared with patients without TNFi primary failure (36.2% vs 61.1%, P=.027). Multivariate analysis demonstrated that this relationship was independent of patient demographics and medical comorbidities. Patients with multiple TNFi exposures had a non-statistically significant lower percentage S-MAPA improvement at week 12 (40.5% vs 52.9%, P=.294) of ustekinumab treatment compared with patients with a single TNFi exposure. Among psoriasis patients previously exposed to TNFi, a history of a previous TNFi primary failure predicts a decreased response to ustekinumab independent of patient demographics and medical comorbidities.

  17. Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components

    Energy Technology Data Exchange (ETDEWEB)

    Duffy, Stephen [Cleveland State Univ., Cleveland, OH (United States)

    2013-09-09

    This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

  18. Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components

    International Nuclear Information System (INIS)

    Duffy, Stephen

    2013-01-01

    This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

  19. Prediction of failure of highly irradiated Zircaloy clad tubes under reactivity initiated accidents

    International Nuclear Information System (INIS)

    Jernkvist, L.O.

    2003-01-01

    This paper deals with failure of irradiated Zircaloy tubes under the heat-up stage of a reactivity initiated accident (RIA). More precisely, by use of a model for plastic strain localization and necking failure, we theoretically analyse the effects of local surface defects on clad ductility and survivability under RIA. The results show that even very shallow surface defects, e.g. arising from a non-uniform or partially spilled oxide layer, have a strong limiting effect on clad ductility. Moreover, in presence of surface defects, the ability of the clad tube to expand radially without necking failure is found to be extremely sensitive to the stress biaxiality ratio σ zz /σ θθ , which is here assumed to be in the range from 0 to 1. The results of our analysis are compared with clad ductility data available in literature, and their consequences for clad failure prediction under RIA are discussed. In particular, the results raise serious concerns regarding the applicability of failure criteria, which are based on clad strain energy density. These criteria do not capture the observed sensitivity to stress biaxiality on clad failure propensity. (author)

  20. Cardiac rehabilitation in patients with ST-segment elevation myocardial infarction: can its failure be predicted?

    Science.gov (United States)

    Irzmański, Robert; Kapusta, Joanna; Obrębska-Stefaniak, Agnieszka; Urzędowicz, Beata; Kowalski, Jan

    2017-07-01

    The prognosis in patients after acute coronary syndromes (ACS) is significantly burdened by coexisting anaemia, leukocytosis and low glomerular filtration rate (GFR). Hyperglycaemia in the early stages of ACS is a strong predictor of death and heart failure in non-diabetic subjects. This study aimed to evaluate the effect of hyperglycaemia, anaemia, leukocytosis, thrombocytopaenia and decreased GFR on the risk of the failure of cardiac rehabilitation (phase II at the hospital) in post-ST-segment elevation myocardial infarction (STEMI) patients. The study included 136 post-STEMI patients, 96 men and 40 women, aged 60.1 ± 11.8 years, admitted for cardiac rehabilitation (phase II) to the Department of Internal Medicine and Cardiac Rehabilitation, WAM University Hospital in Lodz, Poland. On admission fasting blood cell count was performed and serum glucose and creatinine level was determined (GFR assessment). The following results were considered abnormal: glucose ⩾ 100 mg/dl, GFR 10 × 103/μl; platelets (PLTs) failure of cardiac rehabilitation. This risk has been defined on the basis of the patient's inability to tolerate workload increment >5 Watt in spite of the applied program of cardiac rehabilitation. As a result of building a logistic regression model, the most statistically significant risk factors were selected, on the basis of which cardiac rehabilitation failure index was determined. leukocytosis and reduced GFR determined most significantly the risk of failure of cardiac rehabilitation (respectively OR = 6.42 and OR = 3.29, p = 0.007). These parameters were subsequently utilized to construct a rehabilitation failure index. Peripheral blood cell count and GFR are important in assessing the prognosis of cardiac rehabilitation effects. leukocytosis and decreased GFR determine to the highest degree the risk of cardiac rehabilitation failure. Cardiac rehabilitation failure index may be useful in classifying patients into an appropriate model of

  1. Accuracy of circulating histones in predicting persistent organ failure and mortality in patients with acute pancreatitis.

    Science.gov (United States)

    Liu, T; Huang, W; Szatmary, P; Abrams, S T; Alhamdi, Y; Lin, Z; Greenhalf, W; Wang, G; Sutton, R; Toh, C H

    2017-08-01

    Early prediction of acute pancreatitis severity remains a challenge. Circulating levels of histones are raised early in mouse models and correlate with disease severity. It was hypothesized that circulating histones predict persistent organ failure in patients with acute pancreatitis. Consecutive patients with acute pancreatitis fulfilling inclusion criteria admitted to Royal Liverpool University Hospital were enrolled prospectively between June 2010 and March 2014. Blood samples were obtained within 48 h of abdominal pain onset and relevant clinical data during the hospital stay were collected. Healthy volunteers were enrolled as controls. The primary endpoint was occurrence of persistent organ failure. The predictive values of circulating histones, clinical scores and other biomarkers were determined. Among 236 patients with acute pancreatitis, there were 156 (66·1 per cent), 57 (24·2 per cent) and 23 (9·7 per cent) with mild, moderate and severe disease respectively, according to the revised Atlanta classification. Forty-seven healthy volunteers were included. The area under the receiver operating characteristic (ROC) curve (AUC) for circulating histones in predicting persistent organ failure and mortality was 0·92 (95 per cent c.i. 0·85 to 0·99) and 0·96 (0·92 to 1·00) respectively; histones were at least as accurate as clinical scores or biochemical markers. For infected pancreatic necrosis and/or sepsis, the AUC was 0·78 (0·62 to 0·94). Histones did not predict or correlate with local pancreatic complications, but correlated negatively with leucocyte cell viability (r = -0·511, P = 0·001). Quantitative assessment of circulating histones in plasma within 48 h of abdominal pain onset can predict persistent organ failure and mortality in patients with acute pancreatitis. Early death of immune cells may contribute to raised circulating histone levels in acute pancreatitis. © 2017 The Authors. BJS published by John Wiley & Sons Ltd on behalf of BJS

  2. Integrated Logistics Support Analysis of the International Space Station Alpha, Background and Summary of Mathematical Modeling and Failure Density Distributions Pertaining to Maintenance Time Dependent Parameters

    Science.gov (United States)

    Sepehry-Fard, F.; Coulthard, Maurice H.

    1995-01-01

    The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.

  3. Predictive value of daily living score in acute respiratory failure of COPD patients requiring invasive mechanical ventilation pilot study

    Directory of Open Access Journals (Sweden)

    Langlet Ketty

    2012-10-01

    Full Text Available Abstract Background Mechanical ventilation (MV is imperative in many forms of acute respiratory failure (ARF in COPD patients. Previous studies have shown the difficulty to identify parameters predicting the outcome of COPD patients treated by invasive MV. Our hypothesis was that a non specialized score as the activities daily living (ADL score may help to predict the outcome of these patients. Methods We studied the outcome of 25 COPD patients admitted to the intensive care unit for ARF requiring invasive MV. The patients were divided into those weaning success (group A n = 17, 68% or failure (group B n = 8, 32%. We investigated the correlation between the ADL score and the outcome and mortality. Results The ADL score was higher in group A (5.1 ±1.1 vs 3.7 ± 0.7 in group B, p  Conclusion Our pilot study demonstrates that the ADL score is predictive of weaning success and mortality at 6 months, suggesting that the assessment of daily activities should be an important component of ARF management in COPD patients.

  4. Secondary Circulating Prostate Cells Predict Biochemical Failure in Prostate Cancer Patients after Radical Prostatectomy and without Evidence of Disease

    Directory of Open Access Journals (Sweden)

    Nigel P. Murray

    2013-01-01

    Full Text Available Introduction. Although 90% of prostate cancer is considered to be localized, 20%–30% of patients will experience biochemical failure (BF, defined as serum PSA >0.2 ng/mL, after radical prostatectomy (RP. The presence of circulating prostate cells (CPCs in men without evidence of BF may be useful to predict patients at risk for BF. We describe the frequency of CPCs detected after RP, relation with clinicopathological parameters, and association with biochemical failure. Methods and Patients. Serial blood samples were taken during followup after RP, mononuclear cells were obtained by differential gel centrifugation, and CPCs identified using standard immunocytochemistry using anti-PSA monoclonal antibodies. Age, pathological stage (organ confined, nonorgan confined, pathological grade, margin status (positive, negative, extracapsular extension, perineural, vascular, and lymphatic infiltration (positive, negative were compared with the presence/absence of CPCs and with and without biochemical failure. Kaplan Meier methods were used to compare the unadjusted biochemical failure free survival of patients with and without CPCs. Results. 114 men participated, and secondary CPCs were detected more frequently in patients with positive margins, extracapsular extension, and vascular and lymphatic infiltration and were associated with biochemical failure independent of these clinicopathological variables, and with a shorter time to BF. Conclusions. Secondary CPCs are an independent risk factor associated with increased BF in men with a PSA <0.2 ng/mL after radical prostatectomy, but do not determine if the recurrence is due to local or systemic disease. These results warrant larger studies to confirm the findings.

  5. Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States

    Science.gov (United States)

    Chouaib, Wafa; Alila, Younes; Caldwell, Peter V.

    2018-05-01

    The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%-60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced

  6. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS.Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version.Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method.Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  7. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS. Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version. Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method. Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  8. Predicting the creep life and failure mode of low-alloy steel weldments

    Energy Technology Data Exchange (ETDEWEB)

    Brear, J.M.; Middleton, C.J.; Aplin, P.F. [ERA Technology Ltd., Leatherhead (United Kingdom)

    1998-12-31

    This presentation reviews and consolidates experience gained through a number of research projects and practical plant assessments in predicting both the life and the likely failure mode and location in low alloy steel weldments. The approach adopted begins with the recognition that the relative strength difference between the microstructural regions is a key factor controlling both life and failure location. Practical methods based on hardness measurement and adaptable to differing weld geometries are presented and evidence for correlations between hardness ratio, damage accumulation and strain development is discussed. Predictor diagrams relating weld life and failure location to the service conditions and the hardness of the individual microstructural constituents are suggested and comments are given on the implications for identifying the circumstances in which Type IV cracking is to be expected. (orig.) 6 refs.

  9. Predicting the creep life and failure mode of low-alloy steel weldments

    Energy Technology Data Exchange (ETDEWEB)

    Brear, J M; Middleton, C J; Aplin, P F [ERA Technology Ltd., Leatherhead (United Kingdom)

    1999-12-31

    This presentation reviews and consolidates experience gained through a number of research projects and practical plant assessments in predicting both the life and the likely failure mode and location in low alloy steel weldments. The approach adopted begins with the recognition that the relative strength difference between the microstructural regions is a key factor controlling both life and failure location. Practical methods based on hardness measurement and adaptable to differing weld geometries are presented and evidence for correlations between hardness ratio, damage accumulation and strain development is discussed. Predictor diagrams relating weld life and failure location to the service conditions and the hardness of the individual microstructural constituents are suggested and comments are given on the implications for identifying the circumstances in which Type IV cracking is to be expected. (orig.) 6 refs.

  10. Predictive Performance of Echocardiographic Parameters for Cardiovascular Events Among Elderly Treated Hypertensive Patients.

    Science.gov (United States)

    Chowdhury, Enayet K; Jennings, Garry L R; Dewar, Elizabeth; Wing, Lindon M H; Reid, Christopher M

    2016-07-01

    Hypertension leads to cardiac structural and functional changes, commonly assessed by echocardiography. In this study, we assessed the predictive performance of different echocardiographic parameters including left ventricular hypertrophy (LVH) on future cardiovascular outcomes in elderly hypertensive patients without heart failure. Data from LVH substudy of the Second Australian National Blood Pressure trial were used. Echocardiograms were performed at entry into the study. Cardiovascular outcomes were identified over short term (median 4.2 years) and long term (median 10.9 years). LVH was defined using threshold values of LV mass (LVM) indexed to either body surface area (BSA) or height(2.7): >115/95g/m(2) (LVH-BSA(115/95)) or ≥49/45g/m(2.7) (LVH-ht(49/45)) in males/females, respectively, and ≥125g/m(2) (LVH-BSA(125)) or ≥51g/m(2.7) (LVH-ht(51)) for both sexes. In the 666 participants aged ≥65 years in this analysis, LVH prevalence at baseline was 33%-70% depending on definition; and after adjusting for potential risk factors, only LVH-BSA(115/95) predicted both short- and long-term cardiovascular outcomes. Participants having LVH-BSA(115/95) (69%) at baseline had twice the risk of having any first cardiovascular event over the short term (hazard ratio, 95% confidence interval: 2.00, 1.12-3.57, P = 0.02) and any fatal cardiovascular events (2.11, 1.21-3.68, P = 0.01) over the longer term. Among other echocardiographic parameters, LVM and LVM indexed to either BSA or height(2.7) predicted cardiovascular events over both short and longer term. In elderly treated hypertensive patients without heart failure, determining LVH by echocardiography is highly dependent on the methodology adopted. LVH-BSA(115/95) is a reliable predictor of future cardiovascular outcomes in the elderly. © American Journal of Hypertension, Ltd 2016. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  11. Quality of life predicts outcome in a heart failure disease management program.

    LENUS (Irish Health Repository)

    O'Loughlin, Christina

    2012-02-01

    BACKGROUND: Chronic heart failure (HF) is associated with a poor Health Related Quality of Life (HRQoL). HRQoL has been shown to be a predictor of HF outcomes however, variability in the study designs make it difficult to apply these findings to a clinical setting. The aim of this study was to establish if HRQoL is a predictor of long-term mortality and morbidity in HF patients followed-up in a disease management program (DMP) and if a HRQoL instrument could be applied to aid in identifying high-risk patients within a clinical context. METHODS: This is a retrospective analysis of HF patients attending a DMP with 18+\\/-9 months follow-up. Clinical and biochemical parameters were recorded on discharge from index HF admission and HRQoL measures were recorded at 2 weeks post index admission. RESULTS: 225 patients were enrolled into the study (mean age=69+\\/-12 years, male=61%, and 78%=systolic HF). In multivariable analysis, all dimensions of HRQoL (measured by the Minnesota Living with HF Questionnaire) were independent predictors of both mortality and readmissions particularly in patients <80 years. A significant interaction between HRQoL and age (Total((HRQoL))age: p<0.001) indicated that the association of HRQoL with outcomes diminished as age increased. CONCLUSIONS: These data demonstrate that HRQoL is a predictor of outcome in HF patients managed in a DMP. Younger patients (<65 years) with a Total HRQoL score of > or =50 are at high risk of an adverse outcome. In older patients > or =80 years HRQoL is not useful in predicting outcome.

  12. Prediction of meteorological parameters - 3: Rainfall and droughts

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-11-01

    We describe two new methods by which rainfall and hence meteorological droughts at any location on the earth may be predicted. The first method is based upon well supported observations that rainfall distribution at a given location during any local sunspot-related temperature/heat cycle is approximately similar to the distribution during another cycle associated with approximately similar sunspot cycle provided that the two temperature/heat cycles involved are immediately preceded by approximately similar sunspot cycles. The second method is based upon the fact that rainfall belts or patterns seem to be closely related to certain spatial and time-dependent temperature/heat patterns in the earth-atmosphere system. Reasonable predictions of these temperature/heat patterns may be made, and hence the associated rainfall patterns or belts may correspondingly be predicted. Specific examples are given to illustrate the two prediction methods. (author). 12 refs, 11 figs, 1 tab

  13. A relation to predict the failure of materials and potential application to volcanic eruptions and landslides.

    Science.gov (United States)

    Hao, Shengwang; Liu, Chao; Lu, Chunsheng; Elsworth, Derek

    2016-06-16

    A theoretical explanation of a time-to-failure relation is presented, with this relationship then used to describe the failure of materials. This provides the potential to predict timing (tf - t) immediately before failure by extrapolating the trajectory as it asymptotes to zero with no need to fit unknown exponents as previously proposed in critical power law behaviors. This generalized relation is verified by comparison with approaches to criticality for volcanic eruptions and creep failure. A new relation based on changes with stress is proposed as an alternative expression of Voight's relation, which is widely used to describe the accelerating precursory signals before material failure and broadly applied to volcanic eruptions, landslides and other phenomena. The new generalized relation reduces to Voight's relation if stress is limited to increase at a constant rate with time. This implies that the time-derivatives in Voight's analysis may be a subset of a more general expression connecting stress derivatives, and thus provides a potential method for forecasting these events.

  14. Acceleration Test Method for Failure Prediction of the End Cap Contact Region of Sodium Cooled Fast Reactor Fuel Rod

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hyung-Kyu; Lee, Young-Ho; Lee, Hyun-Seung; Lee, Kang-Hee [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2017-05-15

    This paper reports the results of an acceleration test to predict the contact-induced failure that could occur at the cylinder-to-hole joint for the fuel rod of a sodium-cooled fast reactor (SFR). To incorporate the fuel life of the SFR currently under development at KAERI (around 35,000 h), the acceleration test method of reliability engineering was adopted in this work. A finite element method was used to evaluate the flow-induced vibration frequency and amplitude for the test parameter values. Five specimens were tested. The failure criterion during the life of the SFR fuel was applied. The S-N curve of the HT-9, the material of concern, was used to obtain the acceleration factor. As a result, a test time of 16.5 h was obtained for each specimen. It was concluded that the B{sub 0.004} life would be guaranteed for the SFR fuel rods with 99% confidence if no failure was observed at any of the contact surfaces of the five specimens.

  15. In vitro/in silico investigation of failure criteria to predict flexural strength of composite resins.

    Science.gov (United States)

    Yamaguchi, Satoshi; Mehdawi, Idris Mohamed; Sakai, Takahiko; Abe, Tomohiro; Inoue, Sayuri; Imazato, Satoshi

    2018-01-30

    The aim of this study was to investigate a failure criterion to predict flexural strengths of composite resins (CR) by three-dimensional finite element analysis (3D-FEA). Models of flexural strength for test specimens of CR and rods comprising a three-point loading were designed. Calculation of Young's moduli and Poisson's ratios of CR were conducted using a modified McGee-McCullough model. Using the experimental CR, flexural strengths were measured by three-point bending tests with crosshead speed 1.0 mm/min and compared with the values determined by in silico analysis. The flexural strengths of experimental CR calculated using the maximum principal strain significantly correlated with those obtained in silico amongst the four types of failure criteria applied. The in silico analytical model established in this study was found to be effective to predict the flexural strengths of CR incorporating various silica filler contents by maximum principal strain.

  16. PROBABILISTIC PREDICTION OF BANK FAILURES WITH FINANCIAL RATIOS: AN EMPIRICAL STUDY ON TURKISH BANKS

    Directory of Open Access Journals (Sweden)

    Gamze Özel

    2014-02-01

    Full Text Available Banking risk management has become more important during the last 20 years in response to a worldwide increase in the number of bank failures. Turkey has experienced a series of economic and financial crisis since the declaration of Republic and banking system has the most affected sector from the results of these crises. This paper examines some bank failure prediction models using financial ratios. Survival, ordinary and conditional logistic regression models are employed in order to develop these prediction models. The empirical results indicate that the bank is more likely to go bankrupt if it is unprofitable, small, highly leveraged, and has liquidity problems and less financial flexibility to invest itself. 

  17. Development and validation of a dynamic outcome prediction model for paracetamol-induced acute liver failure

    DEFF Research Database (Denmark)

    Bernal, William; Wang, Yanzhong; Maggs, James

    2016-01-01

    : The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation......BACKGROUND: Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver...... transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death. METHODS: Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK...

  18. Five and four-parameter lifetime distributions for bathtub-shaped failure rate using Perks mortality equation

    International Nuclear Information System (INIS)

    Zeng, Hongtao; Lan, Tian; Chen, Qiming

    2016-01-01

    Two lifetime distributions derived from Perks' mortality rate function, one with 4 parameters and the other with 5 parameters, for the modeling of bathtub-shaped failure rates are proposed in this paper. The Perks' mortality/failure rate functions have historically been used for human life modeling in life insurance industry. Although this distribution is no longer used in insurance industry, considering many nice and some unique features of this function, it is necessary to revisit it and introduce it to the reliability community. The parameters of the distributions can control the scale, shape, and location of the PDF. The 4-parameter distribution can be used to model the bathtub failure rate. This model is applied to three previously published groups of lifetime data. This study shows they fit very well. The 5-parameter version can potentially model constant hazard rates of the later life of some devices in addition to the good features of 4-parameter version. Both the 4 and 5-parameter versions have closed form PDF and CDF. The truncated distributions of both versions stay within the original distribution family with simple parameter transformation. This nice feature is normally considered to be only possessed by the simple exponential distribution - Highlights: • Two new distributions are proposed to model bathtub shaped hazard rate. • Derive the close-form PDF, CDF and feature of scalability and truncatability. • Perks4 is verified to be good to model common bathtub shapes through comparison. • Perks5 has the potential to model the stabilization of hazard rate at later life.

  19. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    OpenAIRE

    Bita Najafian; Aminsaburi Aminsaburi; Seyyed Hassan Fakhraei; Abolfazl afjeh; Fatemeh Eghbal; Reza Noroozian

    2015-01-01

    Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS. Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure). The patients were divided into failu...

  20. Economic sustainability in franchising: a model to predict franchisor success or failure

    OpenAIRE

    Calderón Monge, Esther; Pastor Sanz, Ivan .; Huerta Zavala, Pilar Angélica

    2017-01-01

    As a business model, franchising makes a major contribution to gross domestic product (GDP). A model that predicts franchisor success or failure is therefore necessary to ensure economic sustainability. In this study, such a model was developed by applying Lasso regression to a sample of franchises operating between 2002 and 2013. For franchises with the highest likelihood of survival, the franchise fees and the ratio of company-owned to franchised outlets were suited to the age ...

  1. Predicting the Failure of Aluminum Exposed to Simulated Fire and Mechanical Loading Using Finite Element Modeling

    OpenAIRE

    Arthur, Katherine Marie

    2011-01-01

    The interest in the use of aluminum as a structural material in marine applications has increased greatly in recent years. This increase is primarily due to the low weight of aluminum compared to other structural materials as well as its ability to resist corrosion. However, a critical issue in the use of any structural material for naval applications is its response to fire. Past experience has shown that finite element programs can produce accurate predictions of failure of structural c...

  2. Prostate cancer volume adds significantly to prostate-specific antigen in the prediction of early biochemical failure after external beam radiation therapy

    International Nuclear Information System (INIS)

    D'Amico, Anthony V.; Propert, Kathleen J.

    1996-01-01

    Purpose: A new clinical pretreatment quantity that closely approximates the true prostate cancer volume is defined. Methods and Materials: The cancer-specific prostate-specific antigen (PSA), PSA density, prostate cancer volume (V Ca ), and the volume fraction of the gland involved with carcinoma (V Ca fx) were calculated for 227 prostate cancer patients managed definitively with external beam radiation therapy. 1. PSA density PSA/ultrasound prostate gland volume 2. Cancer-specific PSA = PSA - [PSA from benign epithelial tissue] 3. V Ca = Cancer-specific PSA/[PSA in serum per cm 3 of cancer] 4. V Ca fx = V Ca /ultrasound prostate gland volume A Cox multiple regression analysis was used to test whether any of these-clinical pretreatment parameters added significantly to PSA in predicting early postradiation PSA failure. Results: The prostate cancer volume (p = 0.039) and the volume fraction of the gland involved by carcinoma (p = 0.035) significantly added to the PSA in predicting postradiation PSA failure. Conversely, the PSA density and the cancer-specific PSA did not add significantly (p > 0.05) to PSA in predicting postradiation PSA failure. The 20-month actuarial PSA failure-free rates for patients with calculated tumor volumes of ≤0.5 cm 3 , 0.5-4.0 cm 3 , and >4.0 cm 3 were 92, 80, and 47%, respectively (p = 0.00004). Conclusion: The volume of prostate cancer (V Ca ) and the resulting volume fraction of cancer both added significantly to PSA in their ability to predict for early postradiation PSA failure. These new parameters may be used to select patients in prospective randomized trials that examine the efficacy of combining radiation and androgen ablative therapy in patients with clinically localized disease, who are at high risk for early postradiation PSA failure

  3. Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.

    Science.gov (United States)

    Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M

    2011-08-01

    During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

  4. Wear prediction on total ankle replacement effect of design parameters

    CERN Document Server

    Saad, Amir Putra Bin Md; Harun, Muhamad Noor; Kadir, Mohammed Rafiq Abdul

    2016-01-01

    This book develops and analyses computational wear simulations of the total ankle replacement for the stance phase of gait cycle. The emphasis is put on the relevant design parameters. The book presents a model consisting of three components; tibial, bearing and talar representing their physiological functions.

  5. Variable input parameter influence on river corridor prediction

    NARCIS (Netherlands)

    Zerfu, T.; Beevers, L.; Crosato, A.; Wright, N.

    2015-01-01

    This paper considers the erodible river corridor, which is the area in which the main river channel is free to migrate over a period of time. Due to growing anthropogenic pressure, predicting the corridor width has become increasingly important for the planning of development along rivers. Several

  6. Predictive value of daily living score in acute respiratory failure of COPD patients requiring invasive mechanical ventilation pilot study.

    Science.gov (United States)

    Langlet, Ketty; Van Der Linden, Thierry; Launois, Claire; Fourdin, Caroline; Cabaret, Philippe; Kerkeni, Nadia; Barbe, Coralie; Lebargy, François; Deslée, Gaetan

    2012-10-18

    Mechanical ventilation (MV) is imperative in many forms of acute respiratory failure (ARF) in COPD patients. Previous studies have shown the difficulty to identify parameters predicting the outcome of COPD patients treated by invasive MV. Our hypothesis was that a non specialized score as the activities daily living (ADL) score may help to predict the outcome of these patients. We studied the outcome of 25 COPD patients admitted to the intensive care unit for ARF requiring invasive MV. The patients were divided into those weaning success (group A n = 17, 68%) or failure (group B n = 8, 32%). We investigated the correlation between the ADL score and the outcome and mortality. The ADL score was higher in group A (5.1 ±1.1 vs 3.7 ± 0.7 in group B, p success and mortality at 6 months, suggesting that the assessment of daily activities should be an important component of ARF management in COPD patients.

  7. Presenting hydrothorax predicts failure of needle aspiration in primary spontaneous pneumothorax.

    Science.gov (United States)

    Wu, Kwok Kei; Lui, Chun Tat; Ho, Chik Leung; Tsui, Kwok Leung; Fung, Hin Tat

    2016-06-01

    The objective was to evaluate if existence of hydrothorax in initial chest radiograph predicts treatment outcome in patients with primary spontaneous pneumothorax who received needle thoracostomy. This is a retrospective cohort study carried out from January 2011 to August 2014 in 1 public hospital in Hong Kong. All consecutive adult patients aged 18years or above who attended the emergency department with the diagnosis of primary spontaneous pneumothorax with needle aspiration performed as primary treatment were included. Age, smoking status, size of pneumothorax, previous history of pneumothorax, aspirated gas volume and presence of hydropneumothorax in initial radiograph were included in the analysis. The outcome was success or failure of the needle aspiration. Logistic regression was used to identify the predicting factors of failure of needle aspiration. There were a total of 127 patients included. Seventy-three patients (57.5%) were successfully treated with no recurrence upon discharge. Among 54 failure cases, 13 patients (10.2%) failed immediately after procedure as evident by chest radiograph and required second treatment. Forty-one patients (32.3%) failed upon subsequent chest radiographs. Multivariate logistic regression showed factors independently associated with the failure of needle aspiration, which included hydropneumothorax in the initial radiograph (odds ratio [OR]=4.47 [1.56i12.83], P=.005), previous history of pneumothorax (OR=3.92 [1.57-9.79], P=.003), and large size of pneumothorax defined as apex-to-cupola distance ≥5cm (OR=2.75 [1.21-6.26], P=.016). Hydropneumothorax, previous history of pneumothorax, and large size were independent predictors of failure of needle aspiration in treatment of primary spontaneous pneumothorax. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Changes of the thermodynamic parameters in failure conditions of the micro-CHP cycle

    Science.gov (United States)

    Matysko, Robert; Mikielewicz, Jarosław; Ihnatowicz, Eugeniusz

    2014-03-01

    The paper presents the calculations for the failure conditions of the ORC (organic Rankine cycle) cycle in the electrical power system. It analyses the possible reasons of breakdown, such as the electrical power loss or the automatic safety valve failure. The micro-CHP (combined heat and power) system should have maintenance-free configuration, which means that the user does not have to be acquainted with all the details of the ORC system operation. However, the system should always be equipped with the safety control systems allowing for the immediate turn off of the ORC cycle in case of any failure. In case of emergency, the control system should take over the safety tasks and protect the micro-CHP system from damaging. Although, the control systems are able to respond quickly to the CHP system equipped with the inertial systems, the negative effects of failure are unavoidable and always remain for some time. Moreover, the paper presents the results of calculations determining the inertia for the micro-CHP system of the circulating ORC pump, heat removal pump (cooling condenser) and the heat supply pump in failure conditions.

  9. Changes of the thermodynamic parameters in failure conditions of the micro-CHP cycle

    Directory of Open Access Journals (Sweden)

    Matysko Robert

    2014-03-01

    Full Text Available The paper presents the calculations for the failure conditions of the ORC (organic Rankine cycle cycle in the electrical power system. It analyses the possible reasons of breakdown, such as the electrical power loss or the automatic safety valve failure. The micro-CHP (combined heat and power system should have maintenance-free configuration, which means that the user does not have to be acquainted with all the details of the ORC system operation. However, the system should always be equipped with the safety control systems allowing for the immediate turn off of the ORC cycle in case of any failure. In case of emergency, the control system should take over the safety tasks and protect the micro-CHP system from damaging. Although, the control systems are able to respond quickly to the CHP system equipped with the inertial systems, the negative effects of failure are unavoidable and always remain for some time. Moreover, the paper presents the results of calculations determining the inertia for the micro-CHP system of the circulating ORC pump, heat removal pump (cooling condenser and the heat supply pump in failure conditions.

  10. Defining and predicting 'intrauterine fetal renal failure' in congenital lower urinary tract obstruction.

    Science.gov (United States)

    Ruano, Rodrigo; Safdar, Adnan; Au, Jason; Koh, Chester J; Gargollo, Patricio; Shamshirsaz, Alireza A; Espinoza, Jimmy; Cass, Darrell L; Olutoye, Oluyinka O; Olutoye, Olutoyin A; Welty, Stephen; Roth, David R; Belfort, Michael A; Braun, Michael C

    2016-04-01

    The aim of this study was to identify predictors of 'intrauterine fetal renal failure' in fetuses with severe congenital lower urinary tract obstruction (LUTO). We undertook a retrospective study of 31 consecutive fetuses with a diagnosis of LUTO in a tertiary Fetal Center between April 2013 and April 2015. Predictors of 'intrauterine fetal renal failure' were evaluated in those infants with severe LUTO who had either a primary composite outcome measure of neonatal death in the first 24 h of life due to severe pulmonary hypoplasia or a need for renal replacement therapy within 7 days of life. The following variables were analyzed: fetal bladder re-expansion 48 h after vesicocentesis, fetal renal ultrasound characteristics, fetal urinary indices, and amniotic fluid volume. Of the 31 fetuses included in the study, eight met the criteria for 'intrauterine fetal renal failure'. All of the latter had composite poor postnatal outcomes based on death within 24 h of life (n = 6) or need for dialysis within 1 week of life (n = 2). The percentage of fetal bladder refilling after vesicocentesis at time of initial evaluation was the only predictor of 'intrauterine fetal renal failure' (cut-off <27 %, area under the time-concentration curve 0.86, 95 % confidence interval 0.68-0.99; p = 0.009). We propose the concept of 'intrauterine fetal renal failure' in fetuses with the most severe forms of LUTO. Fetal bladder refilling can be used to reliably predict 'intrauterine fetal renal failure', which is associated with severe pulmonary hypoplasia or the need for dialysis within a few days of life.

  11. Mathematical models to predict rheological parameters of lateritic hydromixtures

    OpenAIRE

    Gabriel Hernández-Ramírez; Arístides A. Legrá-Lobaina; Beatriz Ramírez-Serrano; Liudmila Pérez-García

    2017-01-01

    The present work had as objective to establish mathematical models that allow the prognosis of the rheological parameters of the lateritic pulp at concentrations of solids from 35% to 48%, temperature of the preheated hydromixture superior to 82 ° C and number of mineral between 3 and 16. Four samples of lateritic pulp were used in the study at different process locations. The results allowed defining that the plastic properties of the lateritic pulp in the conditions of this study conform to...

  12. Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness

    OpenAIRE

    Hodges-Simeon, Carolyn R.; Gaulin, Steven J. C.; Puts, David A.

    2010-01-01

    Low mean fundamental frequency (F 0) in men’s voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fert...

  13. Clinical parameters predictive of malignancy of thyroid follicular neoplasms

    International Nuclear Information System (INIS)

    Davis, N.L.; Gordon, M.; Germann, E.; Robins, R.E.; McGregor, G.I.

    1991-01-01

    Needle aspiration biopsy is commonly employed in the evaluation of thyroid nodules. Unfortunately, the cytologic finding of a 'follicular neoplasm' does not distinguish between a thyroid adenoma and a follicular cancer. The purpose of this study was to identify clinical parameters that characterize patients with an increased risk of having a thyroid follicular cancer who preoperatively have a 'follicular neoplasm' identified by needle aspiration biopsy. A total of 395 patients initially treated at Vancouver General Hospital and the British Columbia Cancer Agency between the years of 1965 and 1985 were identified and their data were entered into a computer database. Patients with thyroid adenomas were compared to patients with follicular cancer using the chi-square test and Student's t-test. Statistically significant parameters that distinguished patients at risk of having a thyroid cancer (p less than 0.05) included age greater than 50 years, nodule size greater than 3 cm, and a history of neck irradiation. Sex, family history of goiter or neoplasm, alcohol and tobacco use, and use of exogenous estrogen were not significant parameters. Patients can be identified preoperatively to be at an increased risk of having a follicular cancer and accordingly appropriate surgical resection can be planned

  14. Prediction of Milk Quality Parameters Using Vibrational Spectroscopy and Chemometrics

    DEFF Research Database (Denmark)

    Eskildsen, Carl Emil Aae

    fatty acids, protein fractions and coagulation properties from Fourier transform infrared measurements. This thesis shows how such predictions are trapped in a cage of covariance with major milk constituents like total fat and protein content. The prediction models for detailed milk composition...... are not based on causal relationships and this may seriously compromise calibration robustness. It is not recommended to implement indirect models for detailed milk composition in milk recording or breeding programs as such model are providing information on, for example, total protein rather than the specific...... protein fractions. If Fourier transform infrared based models on detailed milk composition are to be implemented in, for example, breeding programs it is recommended to decompose, for example, the individual fatty acids into functional groups, such as methyl, methylene, olefinic and carboxylic groups...

  15. Estimation of liver parameters and oxidative stress in chronic renal failure patients on hemodialysis in Erbil governorate

    Science.gov (United States)

    Kakey, Musher Ismail Salih; Abdoulrahman, Kamaran Kaiani

    2017-09-01

    The present study aims to evaluate iron related parameters in chronic renal failure (CRF) patients on hemodialysis (HD). The study was carried out in Kidney Dialysis Center of Hawler Teaching Hospital in Erbil governorate. This study comprised (76) patients with chronic renal failure on hemodialysis and 41 healthy subjects as a control group of same ages. All hemodialysis patients were taking erythropoietin. The blood samples were taken from the patients before and after the process of hemodialysis for liver parameters and oxidative stress estimations. The results of this study showed lower levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, total bilirubin, total protein and total antioxidant capacity (TAC), while higher levels of alkaline phosphatase (ALP), direct bilirubin and malondialdeyhde (MDA) before analysis was seen. Hemodialysis causes increasing in AST, ALT, albumin, total bilirubin, total protein and decreasing in ALP, direct bilirubin MDA and TAC.

  16. Estimation of failure probability of the end induced current depending on uncertain parameters of a transmission line

    International Nuclear Information System (INIS)

    Larbi, M.; Besnier, P.; Pecqueux, B.

    2014-01-01

    This paper treats about the risk analysis of an EMC default using a statistical approach based on reliability methods. A probability of failure (i.e. probability of exceeding a threshold) of an induced current by crosstalk is computed by taking into account uncertainties on input parameters influencing extreme levels of interference in the context of transmission lines. Results are compared to Monte Carlo simulation (MCS). (authors)

  17. ON THE PERFORMANCE OF ARTIFICIAL INTELLIGENCEMETHODS FOR FAILURE PREDICTION: EVIDENCE FROMISTANBUL STOCK EXCHANGE

    Directory of Open Access Journals (Sweden)

    Dr Hakan Er

    2014-01-01

    Full Text Available The prediction of business failure is a widely studied subject in financialliterature. Many earlier studies on this topic employed statistical methods such asmultiple discriminant analysis, logit and probit topredict corporate failure usingpast financial data (especially the ratio data. However, there has been a recentsurge in academic interest in the use of artificialintelligence (AI methods topredict financial distress. Numerous studies documented that AI methodsoutperform traditional methods. Majority of these studies used data fromestablished markets, the number of studies on emerging market data is ratherlimited and only a handful of studies employed Turkish data for analysis. Thisstudy aims to contribute to the literature by applying the artificial neural networksto predict deletions from Istanbul Stock Exchange (ISE National 100 Index. Thesample is constructed using the quarterly fundamental data of the companies listedin this index the period between January 2008 and December 2012. We employedNeural Networks (NN, logit and probit to predict deletions from index onequarter before they have occurred. Results show that although the logit providesslightly better in-sample predictions, all of the methods fail to identify deletions inthe out-of-sample periods.

  18. Can We Rely on Predicted Basal Metabolic Rate in Patients With Intestinal Failure on Home Parenteral Nutrition?

    Science.gov (United States)

    Skallerup, Anders; Nygaard, Louis; Olesen, Søren Schou; Vinter-Jensen, Lars; Køhler, Marianne; Rasmussen, Henrik Højgaard

    2017-09-01

    Intestinal failure (IF) is a serious and common complication of short bowel syndrome with patients depending on parenteral nutrition (PN) support. Effective nutrition management requires an accurate estimation of the patient's basal metabolic rate (BMR) to avoid underfeeding or overfeeding. However, indirect calorimetry, considered the gold standard for BMR assessment, is a time- and resource-consuming procedure. Consequently, several equations for prediction of BMR have been developed in different settings, but their accuracy in patients with IF are yet to be investigated. We evaluated the accuracy of predicted BMR in clinically stable patients with IF dependent on home parenteral nutrition (HPN). In total, 103 patients with IF were included. We used indirect calorimetry for assessment of BMR and calculated predicted BMR using different equations based on anthropometric and/or bioelectrical impedance parameters. The accuracy of predicted BMR was evaluated using Bland-Altman analysis with measured BMR as the gold standard. The average measured BMR was 1272 ± 245 kcal/d. The most accurate estimations of BMR were obtained using the Harris-Benedict equation (mean bias, 14 kcal/d [ P = .28]; limits of agreement [LoA], -238 to 266 kcal/d) and the Johnstone equation (mean bias, -16 kcal/d [ P = .24]; LoA, -285 to 253 kcal/d). For both equations, 67% of patients had a predicted BMR from 90%-110% All other equations demonstrated a statistically and clinically significant difference between measured and predicted BMR. The Harris-Benedict and Johnstone equations reliably predict BMR in two-thirds of clinically stable patients with IF on HPN.

  19. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty (discussion paper)

    NARCIS (Netherlands)

    Pande, S.; Arkesteijn, L.; Savenije, H.H.G.; Bastidas, L.A.

    2014-01-01

    This paper presents evidence that model prediction uncertainty does not necessarily rise with parameter dimensionality (the number of parameters). Here by prediction we mean future simulation of a variable of interest conditioned on certain future values of input variables. We utilize a relationship

  20. Hope of Success and Fear of Failure Predicting Academic Procrastination Students Who Working on a Thesis

    Directory of Open Access Journals (Sweden)

    Sari Zakiah Akmal

    2017-08-01

    Full Text Available Students, who are working on the thesis, have some difficulties caused by internal and external factors. Those problems can disrupt the completion of their thesis, such as the tendency to do academic procrastination. Increasing achievement motivation can reduce academic procrastination. This study aims to look at the role of achievement motivation (hope of success and fear of failure in predicting academic procrastination. The study used a quantitative approach by distributing academic procrastination and achievement motivation questionnaires. The study involved 182 students who were working on a thesis as samples, which were obtained by using accidental sampling technique. Data were analyzed using multiple regressions. It showed that the hope of success and fear of failure have a significant role in predicting academic procrastination (R2 = 13.8%, F = 14,356, p <0.05. The hope of success can decrease academic procrastination, while fear of failure can improve it. Thus, interventions to reduce academic procrastination can be delivered by increasing students hope of success.

  1. Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

    Directory of Open Access Journals (Sweden)

    Zhiqiang Cai

    2014-01-01

    Full Text Available For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN model with data mining technology. First, the conception of FPBN is introduced to describe the state of components and system and the cause-effect relationships among them. The types of network nodes, the directions of network edges, and the conditional probability distributions (CPDs of nodes in FPBN are discussed in detail. According to the characteristics of nodes and edges in FPBN, a divide-and-conquer principle based algorithm (FPBN-DC is introduced to build the best FPBN network structures of different types of nodes separately. Then, the CPDs of nodes in FPBN are calculated by the maximum likelihood estimation method based on the built network. Finally, a simulation study of a helicopter convertor model is carried out to demonstrate the application of FPBN-DC. According to the simulations results, the FPBN-DC algorithm can get better fitness value with the lower number of iterations, which verified its effectiveness and efficiency compared with traditional algorithm.

  2. Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

    Science.gov (United States)

    Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

    2018-04-01

    With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

  3. Bayesian Estimation of Source Parameters and Associated Coulomb Failure Stress Changes for the 2005 Fukuoka (Japan) Earthquake

    KAUST Repository

    Dutta, Rishabh

    2017-12-20

    Several researchers have studied the source parameters of the 2005 Fukuoka (northwestern Kyushu Island, Japan) earthquake (MW 6.6) using teleseismic, strong motion and geodetic data. However, in all previous studies, errors of the estimated fault solutions have been neglected, making it impossible to assess the reliability of the reported solutions. We use Bayesian inference to estimate the location, geometry and slip parameters of the fault and their uncertainties using Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) data. The offshore location of the earthquake makes the fault parameter estimation challenging, with geodetic data coverage mostly to the southeast of the earthquake. To constrain the fault parameters, we use a priori constraints on the magnitude of the earthquake and the location of the fault with respect to the aftershock distribution and find that the estimated fault slip ranges from 1.5 m to 2.5 m with decreasing probability. The marginal distributions of the source parameters show that the location of the western end of the fault is poorly constrained by the data whereas that of the eastern end, located closer to the shore, is better resolved. We propagate the uncertainties of the fault model and calculate the variability of Coulomb failure stress changes for the nearby Kego fault, located directly below Fukuoka city, showing that the mainshock increased stress on the fault and brought it closer to failure.

  4. Bayesian estimation of source parameters and associated Coulomb failure stress changes for the 2005 Fukuoka (Japan) Earthquake

    Science.gov (United States)

    Dutta, Rishabh; Jónsson, Sigurjón; Wang, Teng; Vasyura-Bathke, Hannes

    2018-04-01

    Several researchers have studied the source parameters of the 2005 Fukuoka (northwestern Kyushu Island, Japan) earthquake (Mw 6.6) using teleseismic, strong motion and geodetic data. However, in all previous studies, errors of the estimated fault solutions have been neglected, making it impossible to assess the reliability of the reported solutions. We use Bayesian inference to estimate the location, geometry and slip parameters of the fault and their uncertainties using Interferometric Synthetic Aperture Radar and Global Positioning System data. The offshore location of the earthquake makes the fault parameter estimation challenging, with geodetic data coverage mostly to the southeast of the earthquake. To constrain the fault parameters, we use a priori constraints on the magnitude of the earthquake and the location of the fault with respect to the aftershock distribution and find that the estimated fault slip ranges from 1.5 to 2.5 m with decreasing probability. The marginal distributions of the source parameters show that the location of the western end of the fault is poorly constrained by the data whereas that of the eastern end, located closer to the shore, is better resolved. We propagate the uncertainties of the fault model and calculate the variability of Coulomb failure stress changes for the nearby Kego fault, located directly below Fukuoka city, showing that the main shock increased stress on the fault and brought it closer to failure.

  5. Improved failure prediction in forming simulations through pre-strain mapping

    Science.gov (United States)

    Upadhya, Siddharth; Staupendahl, Daniel; Heuse, Martin; Tekkaya, A. Erman

    2018-05-01

    The sensitivity of sheared edges of advanced high strength steel (AHSS) sheets to cracking during subsequent forming operations and the difficulty to predict this failure with any degree of accuracy using conventionally used FLC based failure criteria is a major problem plaguing the manufacturing industry. A possible method that allows for an accurate prediction of edge cracks is the simulation of the shearing operation and carryover of this model into a subsequent forming simulation. But even with an efficient combination of a solid element shearing operation and a shell element forming simulation, the need for a fine mesh, and the resulting high computation time makes this approach not viable from an industry point of view. The crack sensitivity of sheared edges is due to work hardening in the shear-affected zone (SAZ). A method to predict plastic strains induced by the shearing process is to measure the hardness after shearing and calculate the ultimate tensile strength as well as the flow stress. In combination with the flow curve, the relevant strain data can be obtained. To eliminate the time-intensive shearing simulation necessary to obtain the strain data in the SAZ, a new pre-strain mapping approach is proposed. The pre-strains to be mapped are, hereby, determined from hardness values obtained in the proximity of the sheared edge. To investigate the performance of this approach the ISO/TS 16630 hole expansion test was simulated with shell elements for different materials, whereby the pre-strains were mapped onto the edge of the hole. The hole expansion ratios obtained from such pre-strain mapped simulations are in close agreement with the experimental results. Furthermore, the simulations can be carried out with no increase in computation time, making this an interesting and viable solution for predicting edge failure due to shearing.

  6. Prediction of Spring Rate and Initial Failure Load due to Material Properties of Composite Leaf Spring

    International Nuclear Information System (INIS)

    Oh, Sung Ha; Choi, Bok Lok

    2014-01-01

    This paper presented analysis methods for adapting E-glass fiber/epoxy composite (GFRP) materials to an automotive leaf spring. It focused on the static behaviors of the leaf spring due to the material composition and its fiber orientation. The material properties of the GFRP composite were directly measured based on the ASTM standard test. A reverse implementation was performed to obtain the complete set of in-situ fiber and matrix properties from the ply test results. Next, the spring rates of the composite leaf spring were examined according to the variation of material parameters such as the fiber angles and resin contents of the composite material. Finally, progressive failure analysis was conducted to identify the initial failure load by means of an elastic stress analysis and specific damage criteria. As a result, it was found that damage first occurred along the edge of the leaf spring owing to the shear stresses

  7. Prediction of compressibility parameters of the soils using artificial neural network.

    Science.gov (United States)

    Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

    2016-01-01

    The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

  8. Serum albumin level predicts initial intravenous immunoglobulin treatment failure in Kawasaki disease.

    Science.gov (United States)

    Kuo, Ho-Chang; Liang, Chi-Di; Wang, Chih-Lu; Yu, Hong-Ren; Hwang, Kao-Pin; Yang, Kuender D

    2010-10-01

    Kawasaki disease (KD) is a systemic vasculitis primarily affecting children who are initial IVIG treatment. This study was conducted to investigate the risk factors for initial IVIG treatment failure in KD. Children who met KD diagnosis criteria and were admitted for IVIG treatment were retrospectively enrolled for analysis. Patients were divided into IVIG-responsive and IVIG-resistant groups. Initial laboratory data before IVIG treatment were collected for analysis. A total of 131 patients were enrolled during the study period. At 48 h after completion of initial IVIG treatment, 20 patients (15.3%) had an elevated body temperature. Univariate analysis showed that patients who had initial findings of high neutrophil count, abnormal liver function, low serum albumin level (≤2.9 g/dL) and pericardial effusion were at risk for IVIG treatment failure. Multivariate analysis with a logistic regression procedure showed that serum albumin level was considered the independent predicting factor of IVIG resistance in patients with KD (p = 0.006, OR = 40, 95% CI: 52.8-562). There was no significant correlation between age, gender, fever duration before IVIG treatment, haemoglobin level, total leucocyte and platelet counts, C-reactive protein level, or sterile pyuria and initial IVIG treatment failure. The specificity and sensitivity for prediction of IVIG treatment failure in this study were 96% and 34%, respectively. Pre-IVIG treatment serum albumin levels are a useful predictor of IVIG resistance in patients with KD. © 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.

  9. Evaluation of accelerated test parameters for CMOS IC total dose hardness prediction

    International Nuclear Information System (INIS)

    Sogoyan, A.V.; Nikiforov, A.Y.; Chumakov, A.I.

    1999-01-01

    The approach to accelerated test parameters evaluation is presented in order to predict CMOS IC total dose behavior in variable dose-rate environment. The technique is based on the analytical model of MOSFET parameters total dose degradation. The simple way to estimate model parameter is proposed using IC's input-output MOSFET radiation test results. (authors)

  10. Cognitive Models of Risky Choice: Parameter Stability and Predictive Accuracy of Prospect Theory

    Science.gov (United States)

    Glockner, Andreas; Pachur, Thorsten

    2012-01-01

    In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are…

  11. A Systematic Review on Prognostic Indicators of Acute Liver Failure and Their Predictive Value for Poor Outcome

    NARCIS (Netherlands)

    Wlodzimirow, Kama A.; Eslami, Saeid; Abu-Hanna, Ameen; Nieuwoudt, Martin; Chamuleau, Robert A. F. M.

    2014-01-01

    This review provides a large amount of information, including the extensive list of worldwide used indicators to predict outcome in patients with acute liver failure. There is large heterogeneity in prognostic indicators of acute liver failure, methods of measurement, complexity of calculation and

  12. Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.

    Science.gov (United States)

    Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M

    2017-09-01

    Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Micromechanics-based damage model for failure prediction in cold forming

    Energy Technology Data Exchange (ETDEWEB)

    Lu, X.Z.; Chan, L.C., E-mail: lc.chan@polyu.edu.hk

    2017-04-06

    The purpose of this study was to develop a micromechanics-based damage (micro-damage) model that was concerned with the evolution of micro-voids for failure prediction in cold forming. Typical stainless steel SS316L was selected as the specimen material, and the nonlinear isotropic hardening rule was extended to describe the large deformation of the specimen undergoing cold forming. A micro-focus high-resolution X-ray computed tomography (CT) system was employed to trace and measure the micro-voids inside the specimen directly. Three-dimensional (3D) representative volume element (RVE) models with different sizes and spatial locations were reconstructed from the processed CT images of the specimen, and the average size and volume fraction of micro-voids (VFMV) for the specimen were determined via statistical analysis. Subsequently, the micro-damage model was compiled as a user-defined material subroutine into the finite element (FE) package ABAQUS. The stress-strain responses and damage evolutions of SS316L specimens under tensile and compressive deformations at different strain rates were predicted and further verified experimentally. It was concluded that the proposed micro-damage model is convincing for failure prediction in cold forming of the SS316L material.

  14. Long-range hydrometeorological ensemble predictions of drought parameters

    Science.gov (United States)

    Fundel, F.; Jörg-Hess, S.; Zappa, M.

    2012-06-01

    Low streamflow as consequence of a drought event affects numerous aspects of life. Economic sectors that may be impacted by drought are, e.g. power production, agriculture, tourism and water quality management. Numerical models have increasingly been used to forecast low-flow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the low-flow indices duration, severity and magnitude, with a forecast lead-time of one month, to assess their potential usefulness for predictions. The ECMWF VarEPS 5 member reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification shows that, compared to peak flow, probabilistic low-flow forecasts are skillful for longer lead-times, low-flow index forecasts could also be beneficially included in a decision-making process. The results suggest monthly runoff forecasts are useful for accessing the risk of hydrological droughts.

  15. Prediction of the association state of insulin using spectral parameters.

    Science.gov (United States)

    Uversky, Vladimir N; Garriques, Liza Nielsen; Millett, Ian S; Frokjaer, Sven; Brange, Jens; Doniach, Sebastian; Fink, Anthony L

    2003-04-01

    Human insulin exists in different association states, from monomer to hexamer, depending on the conditions. In the presence of zinc the "normal" state is a hexamer. The structural properties of 20 variants of human insulin were studied by near-UV circular dichroism, fluorescence spectroscopy, and small-angle X-ray scattering (SAXS). The mutants showed different degrees of association (monomer, dimers, tetramers, and hexamers) at neutral pH. A correlation was shown between the accessibility of tyrosines to acrylamide quenching and the degree of association of the insulin mutants. The near-UV CD spectra of the insulins were affected by protein association and by mutation-induced structural perturbations. However, the shape and intensity of difference CD spectra, obtained by subtraction of the spectra measured in 20% acetic acid (where all insulin species were monomeric) from the corresponding spectra measured at neutral pH, correlate well with the degree of insulin association. In fact, the near-UV CD difference spectra for monomeric, dimeric, tetrameric, and hexameric insulin are very distinctive, both in terms of intensity and shape. The results show that the spectral properties of the insulins reflect their state of association, and can be used to predict their oligomeric state. Copyright 2003 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 92:847-858, 2003

  16. Extreme events and predictability of catastrophic failure in composite materials and in the Earth

    Science.gov (United States)

    Main, I.; Naylor, M.

    2012-05-01

    Despite all attempts to isolate and predict extreme earthquakes, these nearly always occur without obvious warning in real time: fully deterministic earthquake prediction is very much a `black swan'. On the other hand engineering-scale samples of rocks and other composite materials often show clear precursors to dynamic failure under controlled conditions in the laboratory, and successful evacuations have occurred before several volcanic eruptions. This may be because extreme earthquakes are not statistically special, being an emergent property of the process of dynamic rupture. Nevertheless, probabilistic forecasting of event rate above a given size, based on the tendency of earthquakes to cluster in space and time, can have significant skill compared to say random failure, even in real-time mode. We address several questions in this debate, using examples from the Earth (earthquakes, volcanoes) and the laboratory, including the following. How can we identify `characteristic' events, i.e. beyond the power law, in model selection (do dragon-kings exist)? How do we discriminate quantitatively between stationary and non-stationary hazard models (is a dragon likely to come soon)? Does the system size (the size of the dragon's domain) matter? Are there localising signals of imminent catastrophic failure we may not be able to access (is the dragon effectively invisible on approach)? We focus on the effect of sampling effects and statistical uncertainty in the identification of extreme events and their predictability, and highlight the strong influence of scaling in space and time as an outstanding issue to be addressed by quantitative studies, experimentation and models.

  17. A standardized model for predicting flap failure using indocyanine green dye

    Science.gov (United States)

    Zimmermann, Terence M.; Moore, Lindsay S.; Warram, Jason M.; Greene, Benjamin J.; Nakhmani, Arie; Korb, Melissa L.; Rosenthal, Eben L.

    2016-03-01

    Techniques that provide a non-invasive method for evaluation of intraoperative skin flap perfusion are currently available but underutilized. We hypothesize that intraoperative vascular imaging can be used to reliably assess skin flap perfusion and elucidate areas of future necrosis by means of a standardized critical perfusion threshold. Five animal groups (negative controls, n=4; positive controls, n=5; chemotherapy group, n=5; radiation group, n=5; chemoradiation group, n=5) underwent pre-flap treatments two weeks prior to undergoing random pattern dorsal fasciocutaneous flaps with a length to width ratio of 2:1 (3 x 1.5 cm). Flap perfusion was assessed via laser-assisted indocyanine green dye angiography and compared to standard clinical assessment for predictive accuracy of flap necrosis. For estimating flap-failure, clinical prediction achieved a sensitivity of 79.3% and a specificity of 90.5%. When average flap perfusion was more than three standard deviations below the average flap perfusion for the negative control group at the time of the flap procedure (144.3+/-17.05 absolute perfusion units), laser-assisted indocyanine green dye angiography achieved a sensitivity of 81.1% and a specificity of 97.3%. When absolute perfusion units were seven standard deviations below the average flap perfusion for the negative control group, specificity of necrosis prediction was 100%. Quantitative absolute perfusion units can improve specificity for intraoperative prediction of viable tissue. Using this strategy, a positive predictive threshold of flap failure can be standardized for clinical use.

  18. Numerical models for the prediction of failure for multilayer fusion Al-alloy sheets

    International Nuclear Information System (INIS)

    Gorji, Maysam; Berisha, Bekim; Hora, Pavel; Timm, Jürgen

    2013-01-01

    Initiation and propagation of cracks in monolithic and multi-layer aluminum alloys, called “Fusion”, is investigated. 2D plane strain finite element simulations are performed to model deformation due to bending and to predict failure. For this purpose, fracture strains are measured based on microscopic pictures of Nakajima specimens. In addition to, micro-structure of materials is taken into account by introducing a random grain distribution over the sheet thickness as well as a random distribution of the measured yield curve. It is shown that the performed experiments and the introduced FE-Model are appropriate methods to highlight the advantages of the Fusion material, especially for bending processes

  19. A proposal of parameter to predict biaxial fatigue life for CF8M cast stainless steels

    International Nuclear Information System (INIS)

    Park, Joong Cheul; Kwon, Jae Do

    2005-01-01

    Biaxial low cycle fatigue test was carried out to predict fatigue life under combined axial-torsional loading condition which is that of in-phase and out-of-phase for CF8M cast stainless steels. Fatemi Socie(FS) parameter which is based on critical plane approach is not only one of methods but also the best method that can predict fatigue life under biaxial loading condition. But the result showed that, biaxial fatigue life prediction by using FS parameter with several different parameters for the CF8M cast stainless steels is not conservative but best results. So in this present research, we proposed new fatigue life prediction parameter considering effective shear stress instead of FS parameter which considers the maximum normal stress acting on maximum shear strain and its effectiveness was verified

  20. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

    Directory of Open Access Journals (Sweden)

    Andrew D. Revell

    2013-01-01

    Full Text Available Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%. Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.

  1. Effect of bubble interface parameters on predicted of bubble departure diameter in a narrow channel

    International Nuclear Information System (INIS)

    Xu Jianjun; Xie Tianzhou; Zhou Wenbin; Chen Bingde; Huang Yanping

    2014-01-01

    The predicted model on the bubble departure diameter in a narrow channel is built by analysis of forces acting on the bubble, and effects of bubble interface parameters such as the bubble inclination angle, upstream contact angle, downstream contact angle and bubble contact diameter on predicted bubble departure diameters in a narrow channel are analysed by comparing with the visual experimental data. Based on the above results, the bubble interface parameters as the input parameters used to obtain the bubble departure diameter in a narrow channel are assured, and the bubble departure diameters in a narrow channel are predicted by solving the force equation. The predicted bubble departure diameters are verified by the 58 bubble departure diameters obtained from the vertical and inclined visual experiment, and the predicted results agree with the experimental results. The different forces acting on the bubble are obtained and the effect of thermal parameters in this experiment on bubble departure diameters is analysed. (authors)

  2. An analysis of predictive factors for concurrent acute-on-chronic liver failure and hepatorenal syndrome

    Directory of Open Access Journals (Sweden)

    CHEN Yanfang

    2015-09-01

    Full Text Available ObjectiveTo learn the clinical characteristics of concurrent acute-on-chronic liver failure (ACLF and hepatorenal syndrome (HRS, and to investigate the predictive factors for HRS in patients with ACLF. MethodsA total of 806 patients with ACLF who were admitted to our hospital from January 2012 to May 2014 were selected and divided into two groups according to the incidence of concurrent HRS. Clinical indices and laboratory test results were analyzed in the two groups, and the multivariate logistic regression analysis was used to figure out independent indices for the prediction of HRS in patients with ACLF. A prediction model was established and the receiver operating characteristic curve was drawn to evaluate the accuracy of the prediction model. Comparison of continuous data between the two groups was made by t test, and comparison of categorical data between the two groups was made by χ2 test. ResultsIn all patients with ACLF, 229 had HRS and 577 had no HRS. The univariate logistic regression analysis showed that hepatic encephalopathy, peritonitis, infection, age, cystatin C (Cys-C, serum creatinine (SCr, blood urea nitrogen, albumin, prealbumin, total bilirubin, direct bilirubin, total cholesterol, K+, Na+, phosphorus, Ca2+, prothrombin time, prothrombin activity, international normalized ratio, and hematocrit were significant predictive factors for HRS. The multivariate logistic regression analysis showed that concurrent peritonitis, Cys-C, SCr, and HCO3- were independent predictive factors for HRS in patients with ACLF (OR=3.155, P<0.01; OR=30.773, P<0.01; OR=1062, P<0.01; OR=0.915, P<0.05. The model was proved of great value in prediction. ConclusionConcurrent peritonitis, Cys-C, SCr, and HCO3- are effective predictive factors for HRS in patients with ACLF.

  3. Failure criterion effect on solid production prediction and selection of completion solution

    Directory of Open Access Journals (Sweden)

    Dariush Javani

    2017-12-01

    Full Text Available Production of fines together with reservoir fluid is called solid production. It varies from a few grams or less per ton of reservoir fluid posing only minor problems, to catastrophic amount possibly leading to erosion and complete filling of the borehole. This paper assesses solid production potential in a carbonate gas reservoir located in the south of Iran. Petrophysical logs obtained from the vertical well were employed to construct mechanical earth model. Then, two failure criteria, i.e. Mohr–Coulomb and Mogi–Coulomb, were used to investigate the potential of solid production of the well in the initial and depleted conditions of the reservoir. Using these two criteria, we estimated critical collapse pressure and compared them to the reservoir pressure. Solid production occurs if collapse pressure is greater than pore pressure. Results indicate that the two failure criteria show different estimations of solid production potential of the studied reservoir. Mohr–Coulomb failure criterion estimated solid production in both initial and depleted conditions, where Mogi–Coulomb criterion predicted no solid production in the initial condition of reservoir. Based on Mogi–Coulomb criterion, the well may not require completion solutions like perforated liner, until at least 60% of reservoir pressure was depleted which leads to decrease in operation cost and time.

  4. Human Factors Predicting Failure and Success in Hospital Information System Implementations in Sub-Saharan Africa.

    Science.gov (United States)

    Verbeke, Frank; Karara, Gustave; Nyssen, Marc

    2015-01-01

    From 2007 through 2014, the authors participated in the implementation of open source hospital information systems (HIS) in 19 hospitals in Rwanda, Burundi, DR Congo, Congo-Brazzaville, Gabon, and Mali. Most of these implementations were successful, but some failed. At the end of a seven-year implementation effort, a number of risk factors, facilitators, and pragmatic approaches related to the deployment of HIS in Sub-Saharan health facilities have been identified. Many of the problems encountered during the HIS implementation process were not related to technical issues but human, cultural, and environmental factors. This study retrospectively evaluates the predictive value of 14 project failure factors and 15 success factors in HIS implementation in the Sub-Saharan region. Nine of the failure factors were strongly correlated with project failure, three were moderately correlated, and one weakly correlated. Regression analysis also confirms that eight factors were strongly correlated with project success, four moderately correlated, and two weakly correlated. The study results may help estimate the expedience of future HIS projects.

  5. Cannabis use predicts risks of heart failure and cerebrovascular accidents: results from the National Inpatient Sample.

    Science.gov (United States)

    Kalla, Aditi; Krishnamoorthy, Parasuram M; Gopalakrishnan, Akshaya; Figueredo, Vincent M

    2018-06-06

    Cannabis for medicinal and/or recreational purposes has been decriminalized in 28 states as of the 2016 election. In the remaining states, cannabis remains the most commonly used illicit drug. Cardiovascular effects of cannabis use are not well established due to a limited number of studies. We therefore utilized a large national database to examine the prevalence of cardiovascular risk factors and events amongst patients with cannabis use. Patients aged 18-55 years with cannabis use were identified in the National Inpatient Sample 2009-2010 database using the Ninth Revision of International Classification of Disease code 304.3. Demographics, risk factors, and cardiovascular event rates were collected on these patients and compared with general population data. Prevalence of heart failure, cerebrovascular accident (CVA), coronary artery disease, sudden cardiac death, and hypertension were significantly higher in patients with cannabis use. After multivariate regression adjusting for age, sex, hypertension, diabetes mellitus, hyperlipidemia, coronary artery disease, tobacco use, and alcohol use, cannabis use remained an independent predictor of both heart failure (odds ratio = 1.1, 1.03-1.18, P < 0.01) and CVA (odds ratio = 1.24, 1.14-1.34, P < 0.001). Cannabis use independently predicted the risks of heart failure and CVA in individuals 18-55 years old. With continued legalization of cannabis, potential cardiovascular effects and their underlying mechanisms need to be further investigated.

  6. A practical approach to parameter estimation applied to model predicting heart rate regulation

    DEFF Research Database (Denmark)

    Olufsen, Mette; Ottesen, Johnny T.

    2013-01-01

    Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities....... Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate...... a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting...

  7. Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

    Science.gov (United States)

    Paja, W.; Wrzesień, M.; Niemiec, R.; Rudnicki, W. R.

    2015-07-01

    The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

  8. Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

    Science.gov (United States)

    Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

    2016-03-01

    Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

  9. A procedure to identify and to assess risk parameters in a SCR (Steel Catenary Riser) due to the fatigue failure

    Energy Technology Data Exchange (ETDEWEB)

    Stefane, Wania [Universidade Estadual de Campinas (UNICAMP), Campinas, SP (Brazil). Faculdade de Engenharia Mecanica; Morooka, Celso K. [Universidade Estadual de Campinas (UNICAMP), Campinas, SP (Brazil). Dept. de Engenharia de Petroleo. Centro de Estudos de Petroleo; Pezzi Filho, Mario [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). E and P. ENGP/IPMI/ES; Matt, Cyntia G.C.; Franciss, Ricardo [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas (CENPES)

    2009-12-19

    The discovery of offshore fields in ultra deep water and the presence of reservoirs located in great depths below the seabed requires innovative solutions for offshore oil production systems. Many riser configurations have emerged as economically viable technological solutions for these scenarios. Therefore the study and the development of methodologies applied to riser design and procedures to calculate and to dimension production risers, taken into account the effects of mete ocean conditions, such as waves, current and platform motion in the fatigue failure is fundamental. The random nature of these conditions as well as the mechanical characteristics of the riser components are critical to a probabilistic treatment to ensure the greatest reliability for risers and minimum risks associated to different aspects of the operation like the safety of the installation, economical concerns and the environment. The current work presents a procedure of the identification and the assessment of main parameters of risk when considering fatigue failure. Static and dynamic behavior of Steel Catenary Riser (SCR) under the effects of mete ocean conditions and uncertainties related to total cumulative damage (Miner-Palmgren's rule) are taken into account. The methodology adopted is probabilistic and the approach is analytical. The procedure is based on the First Order Reliability Method (FORM) which usually presents low computational effort and acceptable accuracy. The procedure suggested is applied for two practical cases, one using data available from the literature and the second with data collected from an actual Brazilian offshore field operation. For both cases, results of the probability of failure due to fatigue were obtained for different locations along the SCR length connected to a semi-submersible platform. From these results, the sensitivity of the probability of failure due to fatigue for a SCR could be verified, and the most effective parameter could also be

  10. Determination of crack morphology parameters from service failures for leak-rate analyses

    Energy Technology Data Exchange (ETDEWEB)

    Wilkowski, G.; Ghadiali, N.; Paul, D. [Battelle Memorial Institute, Columbus, OH (United States)] [and others

    1997-04-01

    In leak-rate analyses described in the literature, the crack morphology parameters are typically not well agreed upon by different investigators. This paper presents results on a review of crack morphology parameters determined from examination of service induced cracks. Service induced cracks were found to have a much more tortuous flow path than laboratory induced cracks due to crack branching associated with the service induced cracks. Several new parameters such as local and global surface roughnesses, as well as local and global number of turns were identified. The effect of each of these parameters are dependent on the crack-opening displacement. Additionally, the crack path is typically assumed to be straight through the pipe thickness, but the service data show that the flow path can be longer due to the crack following a fusion line, and/or the number of turns, where the number of turns in the past were included as a pressure drop term due to the turns, but not the longer flow path length. These parameters were statistically evaluated for fatigue cracks in air, corrosion-fatigue, IGSCC, and thermal fatigue cracks. A refined version of the SQUIRT leak-rate code was developed to account for these variables. Sample calculations are provided in this paper that show how the crack size can vary for a given leak rate and the statistical variation of the crack morphology parameters.

  11. Do premorbid predictors of alcohol dependence also predict the failure to recover from alcoholism?

    DEFF Research Database (Denmark)

    Penick, Elizabeth C; Knop, Joachim; Nickel, Elizabeth J

    2010-01-01

    OBJECTIVE: In a search for viable endophenotypes of alcoholism, this longitudinal study attempted to identify premorbid predictors of alcohol dependence that also predicted the course of alcoholism. METHOD: The 202 male subjects who completed a 40-year follow-up were originally selected from...... diagnoses of alcohol abuse or alcohol dependence that were characterized as currently active or currently in remission according to Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised, course specifiers. RESULTS: The majority of subjects with a diagnosis of alcohol abuse were......: cognitive efficiency and early behavioral dyscontrol in childhood. Both factors predicted the failure to remit (low cognitive efficiency and high behavioral dyscontrol) even when lifetime alcoholism severity was controlled. CONCLUSIONS: This 4-decade study found a striking disconnect between measures...

  12. A model for predicting pellet-cladding interaction induced fuel rod failure, based on nonlinear fracture mechanics

    International Nuclear Information System (INIS)

    Jernkvist, L.O.

    1993-01-01

    A model for predicting pellet-cladding mechanical interaction induced fuel rod failure, suitable for implementation in finite element fuel-performance codes, is presented. Cladding failure is predicted by explicitly modelling the propagation of radial cracks under varying load conditions. Propagation is assumed to be due to either iodine induced stress corrosion cracking or ductile fracture. Nonlinear fracture mechanics concepts are utilized in modelling these two mechanisms of crack growth. The novelty of this approach is that the development of cracks, which may ultimately lead to fuel rod failure, can be treated as a dynamic and time-dependent process. The influence of cyclic loading, ramp rates and material creep on the failure mechanism can thereby be investigated. Results of numerical calculations, in which the failure model has been used to study the dependence of cladding creep rate on crack propagation velocity, are presented. (author)

  13. Value of routine investigations to predict loop diuretic down-titration success in stable heart failure.

    Science.gov (United States)

    Martens, Pieter; Verbrugge, Frederik H; Boonen, Levinia; Nijst, Petra; Dupont, Matthias; Mullens, Wilfried

    2018-01-01

    Guidelines advocate down-titration of loop diuretics in chronic heart failure (CHF) when patients have no signs of volume overload. Limited data are available on the expected success rate of this practice or how routine diagnostic tests might help steering this process. Fifty ambulatory CHF-patients on stable neurohumoral blocker/diuretic therapy for at least 3months without any clinical sign of volume overload were prospectively included to undergo loop diuretic down-titration. All patients underwent a similar pre-down-titration evaluation consisting of a dyspnea scoring, physical examination, transthoracic echocardiography (diastolic function, right ventricular function, cardiac filling pressures and valvular disease), blood sample (serum creatinine, plasma NT-pro-BNP and neurohormones). Loop diuretic maintenance dose was subsequently reduced by 50% or stopped if dose was ≤40mg furosemide equivalents. Successful down-titration was defined as a persistent dose reduction after 30days without weight increase >1.5kg or new-onset symptoms of worsening heart failure. At 30-day follow-up, down-titration was successful in 62% (n=31). In 12/19 patients exhibiting down-titration failure, this occurred within the first week. Physical examination, transthoracic echocardiography and laboratory analysis had limited predictive capability to detect patients with down-titration success/failure (positive likelihood-ratios below 1.5, or area under the curve [AUC] non-statically different from AUC=0.5). Loop diuretic down-titration is feasible in a majority of stable CHF patients in which the treating clinician felt continuation of loops was unnecessary to sustain euvolemia. Importantly, routine diagnostics which suggest euvolemia, have limited diagnostic impact on the post-test probability. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Slope Failure Prediction and Early Warning Awareness Education for Reducing Landslides Casualty in Malaysia

    Science.gov (United States)

    Koay, S. P.; Tay, L. T.; Fukuoka, H.; Koyama, T.; Sakai, N.; Jamaludin, S. B.; Lateh, H.

    2015-12-01

    Northeast monsoon causes heavy rain in east coast of Peninsular Malaysia from November to March, every year. During this monsoon period, besides the happening of flood along east coast, landslides also causes millions of Malaysian Ringgit economical losses. Hence, it is essential to study the prediction of slope failure to prevent the casualty of landslides happening. In our study, we introduce prediction method of the accumulated rainfall affecting the stability of the slope. If the curve, in the graph, which is presented by rainfall intensity versus accumulated rainfall, crosses over the critical line, the condition of the slope is considered in high risk where the data are calculated and sent from rain gauge in the site via internet. If the possibility of slope failure is going high, the alert message will be sent out to the authorities for decision making on road block or setting the warning light at the road side. Besides road block and warning light, we propose to disseminate short message, to pre-registered mobile phone user, to notify the public for easing the traffic jam and avoiding unnecessary public panic. Prediction is not enough to prevent the casualty. Early warning awareness of the public is very important to reduce the casualty of landslides happening. IT technology does not only play a main role in disseminating information, early warning awareness education, by using IT technology, should be conducted, in schools, to give early warning awareness on natural hazard since childhood. Knowing the pass history on landslides occurrence will gain experience on the landslides happening. Landslides historical events with coordinate information are stored in database. The public can browse these historical events via internet. By referring to such historical landslides events, the public may know where did landslides happen before and the possibility of slope failure occurrence again is considered high. Simulation of rainfall induced slope failure mechanism

  15. Prediction of posthepatectomy liver failure using transient elastography in patients with hepatitis B related hepatocellular carcinoma.

    Science.gov (United States)

    Lei, Jie-Wen; Ji, Xiao-Yu; Hong, Jun-Feng; Li, Wan-Bin; Chen, Yan; Pan, Yan; Guo, Jia

    2017-12-29

    It is essential to accurately predict Postoperative liver failure (PHLF) which is a life-threatening complication. Liver hardness measurement (LSM) is widely used in non-invasive assessment of liver fibrosis. The aims of this study were to explore the application of preoperative liver stiffness measurements (LSM) by transient elastography in predicting postoperative liver failure (PHLF) in patients with hepatitis B related hepatocellular carcinoma. The study included 247 consecutive patients with hepatitis B related hepatocellular carcinoma who underwent hepatectomy between May 2015 and September 2015. Detailed preoperative examinations including LSM were performed before hepatectomy. The endpoint was the development of PHLF. All of the patients had chronic hepatitis B defined as the presence of hepatitis B surface antigen (HBsAg) for more than 6 months and 76 (30.8%) had cirrhosis. PHLF occurred in 37 (14.98%) patients. Preoperative LSM (odds ratio, OR, 1.21; 95% confidence interval, 95% CI: 1.13-1.29; P hepatocellular carcinoma.

  16. Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics

    Science.gov (United States)

    Khanmohammadi, Mohammdreza; Ghasemi, Keyvan; Garmarudi, Amir Bagheri; Ramin, Mehdi

    2015-02-01

    A new diagnostic approach based on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectrometry and classification algorithm has been introduced which provides a rapid, reliable, and easy way to perform blood test for the diagnosis of renal failure. Blood serum samples from 35 renal failure patients and 40 healthy persons were analyzed by ATR-FTIR spectrometry. The resulting data was processed by Quadratic Discriminant Analysis (QDA) and QDA combined with simple filtered method. Spectroscopic studies were performed in 900-2000 cm-1 spectral region with 3.85 cm-1 data space. Results showed 93.33% and 100% of accuracy for QDA and filter-QDA models, respectively. In the first step, 30 samples were applied to construct the model. In order to modify the capability of QDA in prediction of test samples, filter-based feature selection methods were applied. It was found that the filtered spectra coupled with QDA could correctly predict the test samples in most of the cases.

  17. Failure Predictions of Out-of-Autoclave Sandwich Joints with Delaminations Under Flexure Loads

    Science.gov (United States)

    Nordendale, Nikolas A.; Goyal, Vinay K.; Lundgren, Eric C.; Patel, Dhruv N.; Farrokh, Babak; Jones, Justin; Fischetti, Grace; Segal, Kenneth N.

    2015-01-01

    An analysis and a test program was conducted to investigate the damage tolerance of composite sandwich joints. The joints contained a single circular delamination between the face-sheet and the doubler. The coupons were fabricated through out-of-autoclave (OOA) processes, a technology NASA is investigating for joining large composite sections. The four-point bend flexure test was used to induce compression loading into the side of the joint where the delamination was placed. The compression side was chosen since it tends to be one of the most critical loads in launch vehicles. Autoclave cure was used to manufacture the composite sandwich sections, while the doubler was co-bonded onto the sandwich face-sheet using an OOA process after sandwich panels were cured. A building block approach was adopted to characterize the mechanical properties of the joint material, including the fracture toughness between the doubler and face-sheet. Twelve four-point-bend samples were tested, six in the sandwich core ribbon orientation and six in sandwich core cross-ribbon direction. Analysis predicted failure initiation and propagation at the pre-delaminated location, consistent with experimental observations. A building block approach using fracture analyses methods predicted failure loads in close agreement with tests. This investigation demonstrated a small strength reduction due to a flaw of significant size compared to the width of the sample. Therefore, concerns of bonding an OOA material to an in-autoclave material was mitigated for the geometries, materials, and load configurations considered.

  18. miRNA signatures can predict acute liver failure in hepatitis E infected pregnant females

    Directory of Open Access Journals (Sweden)

    Nirupma Trehanpati

    2017-04-01

    Full Text Available Background: Acute viral hepatitis E (AVH-E can often result in acute liver failure (ALF during pregnancy. microRNAs serve as mediators in drug induced liver failure. We investigated their role as a biomarker in predicting ALF due to HEV (ALF-E. Methods: We performed next generation sequencing and subsequent validation studies in PBMCs of pregnant (P self limiting AVH-E, ALF due to HEV (ALF-E and compared with AVH-E in non-pregnant (NP females and healthy controls. Findings: Eleven microRNAs were significantly expressed in response to HEV infection; importantly, miR- 431, 654, 1468 and 4435, were distinctly expressed in pregnant self-limiting AVH-E and healthy females (p = 0.0005, but not in ALF-E. Sixteen exclusive microRNAs differentiated ALF-E from self limiting AVH-E in pregnant females. miR-450b which affects cellular proliferation and metabolic processes through RNF20 and SECB was predominanlty upregulated and correlated with poor outcome (ROC 0.958, p = 0.001. Interpretation: Our results reveal that a specific microRNA profile can predict fatality in ALF-E in pregnancy. These microRNAs could be exploited as prognostic biomarkers and help in the development of new therapeutic interventions. Keywords: Health sciences, Virology

  19. Can we predict uranium bioavailability based on soil parameters? Part 1: Effect of soil parameters on soil solution uranium concentration

    International Nuclear Information System (INIS)

    Vandenhove, H.; Hees, M. van; Wouters, K.; Wannijn, J.

    2007-01-01

    Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for 238 U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K d , L kg -1 ) and the organic matter content (R 2 = 0.70) and amorphous Fe content (R 2 = 0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH = 6, log(K d ) was linearly related with pH [log(K d ) = - 1.18 pH + 10.8, R 2 = 0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex. - Uranium solubility in soil can be predicted from organic matter or amorphous iron content and pH or with complex multilinear models considering several soil parameters

  20. Determination of water chemistry parameters which influence on failure intensity of RBMK equipment

    Energy Technology Data Exchange (ETDEWEB)

    Kovalev, S.M.; Kritski, V.G.; Berezina, I.G.; Stjazhkin, P.S.; Olejnik, P.V. [All-Russian Design and Scientific Research Inst. of Complex Power Technology (VNIPIET), St. Petersburg (Russian Federation)

    2002-07-01

    The coolant quality has an effect on intensity of austenitic stainless steel intergranular corrosion. The correlation between rate of intergranular stress corrosion cracking and water electro-conductivity (on generalized data) for non-stabilized stainless steel 304SS type and stabilized stainless steel X18H10T type is shown in Fig. 1. The cracking rate is increased with electro-conductivity rise and this is equivalently to reduction of pipelines lifetime, increase of amount and depth of cracks in welds. The increased values of water electro-conductivity correspond to high concentrations of iron corrosion product, corrosion-active anions (chlorides, sulphates et. al.), arrived to circulation circuit due to failure of equipment elements, for example, of condenser tubes or during start-up. (author)

  1. Prediction Model of Machining Failure Trend Based on Large Data Analysis

    Science.gov (United States)

    Li, Jirong

    2017-12-01

    The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

  2. A prediction model for 5-year cardiac mortality in patients with chronic heart failure using {sup 123}I-metaiodobenzylguanidine imaging

    Energy Technology Data Exchange (ETDEWEB)

    Nakajima, Kenichi; Matsuo, Shinro [Kanazawa University Hospital, Department of Nuclear Medicine, Kanazawa (Japan); Nakata, Tomoaki [Sapporo Medical University School of Medicine, Second Department of Internal Medicine (Cardiology), Sapporo (Japan); Hakodate-Goryoukaku Hospital, Department of Cardiology, Hakodate (Japan); Yamada, Takahisa [Osaka Prefectural General Medical Center, Department of Cardiology, Osaka (Japan); Yamashina, Shohei [Toho University Omori Medical Center, Department of Cardiovascular Medicine, Tokyo (Japan); Momose, Mitsuru [Tokyo Women' s Medical University, Department of Nuclear Medicine, Tokyo (Japan); Kasama, Shu [Cardiovascular Hospital of Central Japan, Department of Cardiology, Shibukawa (Japan); Matsui, Toshiki [Social Insurance Shiga General Hospital, Department of Cardiology, Otsu (Japan); Travin, Mark I. [Albert Einstein Medical College, Department of Cardiology and Nuclear Medicine, Montefiore Medical Center, Bronx, NY (United States); Jacobson, Arnold F. [GE Healthcare, Medical Diagnostics, Princeton, NJ (United States)

    2014-09-15

    Prediction of mortality risk is important in the management of chronic heart failure (CHF). The aim of this study was to create a prediction model for 5-year cardiac death including assessment of cardiac sympathetic innervation using data from a multicenter cohort study in Japan. The original pooled database consisted of cohort studies from six sites in Japan. A total of 933 CHF patients who underwent {sup 123}I-metaiodobenzylguanidine (MIBG) imaging and whose 5-year outcomes were known were selected from this database. The late MIBG heart-to-mediastinum ratio (HMR) was used for quantification of cardiac uptake. Cox proportional hazard and logistic regression analyses were used to select appropriate variables for predicting 5-year cardiac mortality. The formula for predicting 5-year mortality was created using a logistic regression model. During the 5-year follow-up, 205 patients (22 %) died of a cardiac event including heart failure death, sudden cardiac death and fatal acute myocardial infarction (64 %, 30 % and 6 %, respectively). Multivariate logistic analysis selected four parameters, including New York Heart Association (NYHA) functional class, age, gender and left ventricular ejection fraction, without HMR (model 1) and five parameters with the addition of HMR (model 2). The net reclassification improvement analysis for all subjects was 13.8 % (p < 0.0001) by including HMR and its inclusion was most effective in the downward reclassification of low-risk patients. Nomograms for predicting 5-year cardiac mortality were created from the five-parameter regression model. Cardiac MIBG imaging had a significant additive value for predicting cardiac mortality. The prediction formula and nomograms can be used for risk stratifying in patients with CHF. (orig.)

  3. A Microstructure-Based Model to Characterize Micromechanical Parameters Controlling Compressive and Tensile Failure in Crystallized Rock

    Science.gov (United States)

    Kazerani, T.; Zhao, J.

    2014-03-01

    A discrete element model is proposed to examine rock strength and failure. The model is implemented by UDEC which is developed for this purpose. The material is represented as a collection of irregular-sized deformable particles interacting at their cohesive boundaries. The interface between two adjacent particles is viewed as a flexible contact whose stress-displacement law is assumed to control the material fracture and fragmentation process. To reproduce rock anisotropy, an innovative orthotropic cohesive law is developed for contact which allows the interfacial shear and tensile behaviours to be different from each other. The model is applied to a crystallized igneous rock and the individual and interactional effects of the microstructural parameters on the material compressive and tensile failure response are examined. A new methodical calibration process is also established. It is shown that the model successfully reproduces the rock mechanical behaviour quantitatively and qualitatively. Ultimately, the model is used to understand how and under what circumstances micro-tensile and micro-shear cracking mechanisms control the material failure at different loading paths.

  4. Dead or Alive? Using Membrane Failure and Chlorophyll a Fluorescence to Predict Plant Mortality from Drought.

    Science.gov (United States)

    Guadagno, Carmela R; Ewers, Brent E; Speckman, Heather N; Aston, Timothy Llewellyn; Huhn, Bridger J; DeVore, Stanley B; Ladwig, Joshua T; Strawn, Rachel N; Weinig, Cynthia

    2017-09-01

    Climate models predict widespread increases in both drought intensity and duration in the next decades. Although water deficiency is a significant determinant of plant survival, limited understanding of plant responses to extreme drought impedes forecasts of both forest and crop productivity under increasing aridity. Drought induces a suite of physiological responses; however, we lack an accurate mechanistic description of plant response to lethal drought that would improve predictive understanding of mortality under altered climate conditions. Here, proxies for leaf cellular damage, chlorophyll a fluorescence, and electrolyte leakage were directly associated with failure to recover from drought upon rewatering in Brassica rapa (genotype R500) and thus define the exact timing of drought-induced death. We validated our results using a second genotype (imb211) that differs substantially in life history traits. Our study demonstrates that whereas changes in carbon dynamics and water transport are critical indicators of drought stress, they can be unrelated to visible metrics of mortality, i.e. lack of meristematic activity and regrowth. In contrast, membrane failure at the cellular scale is the most proximate cause of death. This hypothesis was corroborated in two gymnosperms ( Picea engelmannii and Pinus contorta ) that experienced lethal water stress in the field and in laboratory conditions. We suggest that measurement of chlorophyll a fluorescence can be used to operationally define plant death arising from drought, and improved plant characterization can enhance surface model predictions of drought mortality and its consequences to ecosystem services at a global scale. © 2017 American Society of Plant Biologists. All Rights Reserved.

  5. Postoperative Prostate-Specific Antigen Velocity Independently Predicts for Failure of Salvage Radiotherapy After Prostatectomy

    International Nuclear Information System (INIS)

    King, Christopher R.; Presti, Joseph C.; Brooks, James D.; Gill, Harcharan; Spiotto, Michael T.

    2008-01-01

    Purpose: Identification of patients most likely to benefit from salvage radiotherapy (RT) using postoperative (postop) prostate-specific antigen (PSA) kinetics. Methods and Materials: From 1984 to 2004, 81 patients who fit the following criteria formed the study population: undetectable PSA after radical prostatectomy (RP); pathologically negative nodes; biochemical relapse defined as a persistently detectable PSA; salvage RT; and two or more postop PSAs available before salvage RT. Salvage RT included the whole pelvic nodes in 55 patients and 4 months of total androgen suppression in 56 patients. The median follow-up was >5 years. All relapses were defined as a persistently detectable PSA. Kaplan-Meier and Cox proportional hazards multivariable analysis were performed for all clinical, pathological, and treatment factors predicting for biochemical relapse-free survival (bRFS). Results: There were 37 biochemical relapses observed after salvage RT. The 5-year bRFS after salvage RT for patients with postop prostate-specific antigen velocity ≤1 vs. >1 ng/ml/yr was 59% vs. 29%, p = 0.002. In multivariate analysis, only postop PSAV (p = 0.0036), pre-RT PSA level ≤1 (p = 0.037) and interval-to-relapse >10 months (p = 0.012) remained significant, whereas pelvic RT, hormone therapy, and RT dose showed a trend (p = ∼0.06). PSAV, but not prostate-specific antigen doubling time, predicted successful salvage RT, suggesting an association of zero-order kinetics with locally recurrent disease. Conclusions: Postoperative PSA velocity independently predicts for the failure of salvage RT and can be considered in addition to high-risk features when selecting patients in need of systemic therapy following biochemical failure after RP. For well-selected patients, salvage RT can achieve high cure rates

  6. Failure Predictions for Graphite Reflector Bricks in the Very High Temperature Reactor with the Prismatic Core Design

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Gyanender, E-mail: sing0550@umn.edu [Department of Mechanical Engineering, University of Minnesota, 111, Church St. SE, Minneapolis, MN 55455 (United States); Fok, Alex [Minnesota Dental Research in Biomaterials and Biomechanics, School of Dentistry, University of Minnesota, 515, Delaware St. SE, Minneapolis, MN 55455 (United States); Department of Mechanical Engineering, University of Minnesota, 111, Church St. SE, Minneapolis, MN 55455 (United States); Mantell, Susan [Department of Mechanical Engineering, University of Minnesota, 111, Church St. SE, Minneapolis, MN 55455 (United States)

    2017-06-15

    Highlights: • Failure probability of VHTR reflector bricks predicted though crack modeling. • Criterion chosen for defining failure strongly affects the predictions. • Breaching of the CRC could be significantly delayed through crack arrest. • Capability to predict crack initiation and propagation demonstrated. - Abstract: Graphite is used in nuclear reactor cores as a neutron moderator, reflector and structural material. The dimensions and physical properties of graphite change when it is exposed to neutron irradiation. The non-uniform changes in the dimensions and physical properties lead to the build-up of stresses over the course of time in the core components. When the stresses reach the critical limit, i.e. the strength of the material, cracking occurs and ultimately the components fail. In this paper, an explicit crack modeling approach to predict the probability of failure of a VHTR prismatic reactor core reflector brick is presented. Firstly, a constitutive model for graphite is constructed and used to predict the stress distribution in the reflector brick under in-reactor conditions of high temperature and irradiation. Fracture simulations are performed as part of a Monte Carlo analysis to predict the probability of failure. Failure probability is determined based on two different criteria for defining failure time: A) crack initiation and B) crack extension to near control rod channel. A significant difference is found between the failure probabilities based on the two criteria. It is predicted that the reflector bricks will start cracking during the time range of 5–9 years, while breaching of the control rod channels will occur during the period of 11–16 years. The results show that, due to crack arrest, there is a significantly delay between crack initiation and breaching of the control rod channel.

  7. Baseline Hemodynamics and Response to Contrast Media During Diagnostic Cardiac Catheterization Predict Adverse Events in Heart Failure Patients.

    Science.gov (United States)

    Denardo, Scott J; Vock, David M; Schmalfuss, Carsten M; Young, Gregory D; Tcheng, James E; O'Connor, Christopher M

    2016-07-01

    Contrast media administered during cardiac catheterization can affect hemodynamic variables. However, little is documented about the effects of contrast on hemodynamics in heart failure patients or the prognostic value of baseline and changes in hemodynamics for predicting subsequent adverse events. In this prospective study of 150 heart failure patients, we measured hemodynamics at baseline and after administration of iodixanol or iopamidol contrast. One-year Kaplan-Meier estimates of adverse event-free survival (death, heart failure hospitalization, and rehospitalization) were generated, grouping patients by baseline measures of pulmonary capillary wedge pressure (PCWP) and cardiac index (CI), and by changes in those measures after contrast administration. We used Cox proportional hazards modeling to assess sequentially adding baseline PCWP and change in CI to 5 validated risk models (Seattle Heart Failure Score, ESCAPE [Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness], CHARM [Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity], CORONA [Controlled Rosuvastatin Multinational Trial in Heart Failure], and MAGGIC [Meta-Analysis Global Group in Chronic Heart Failure]). Median contrast volume was 109 mL. Both contrast media caused similarly small but statistically significant changes in most hemodynamic variables. There were 39 adverse events (26.0%). Adverse event rates increased using the composite metric of baseline PCWP and change in CI (Pcontrast correlated with the poorest prognosis. Adding both baseline PCWP and change in CI to the 5 risk models universally improved their predictive value (P≤0.02). In heart failure patients, the administration of contrast causes small but significant changes in hemodynamics. Calculating baseline PCWP with change in CI after contrast predicts adverse events and increases the predictive value of existing models. Patients with elevated baseline PCWP and

  8. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    DEFF Research Database (Denmark)

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

  9. Soil stochastic parameter correlation impact in the piping erosion failure estimation of riverine flood defences, doi:10.1016/j.strusafe.2016.01.004

    NARCIS (Netherlands)

    Aguilar Lopez, Juan Pablo; Warmink, Jord Jurriaan; Schielen, Ralph Mathias Johannes; Hulscher, Suzanne J.M.H.

    2016-01-01

    Piping erosion has been proved to be one of the failure mechanisms that contributes the most to the total probability of failure on the Dutch flood defence systems. The present study aimed to find the impact of correlation and tail dependence between soil parameters present in the Sellmeijer revised

  10. Clinical safety and parameters of maximum oxygen uptake (Vo/sub 2/ max) testing in pakistani patients with heart failure

    International Nuclear Information System (INIS)

    Hussain, S.; Kayani, A.M.

    2015-01-01

    To determine the parameters of maximum oxygen uptake (VO2 max) in a Pakistani systolic heart failure cohort and its safety in a clinical setting. Study Design: Descriptive study. Place and Duration of Study: Armed Forces Institute of Cardiology, National Institute of Heart Diseases, Rawalpindi, from June 2011 to January 2013. Methodology: Maximum oxygen uptake test was performed in patients with severe heart failure, who could perform the VO2 max treadmill test. Age, Body Mass Index (BMI) ejection fraction, VO2 max and respiratory exchange ratios and their correlations were determined. Results: Out of 135 patients, 77% (n=104) were males, with a mean age of 45.9 ± 15.7 years. Weight of patients ranged from 30 kg to 107 kg (mean 63.29 ± 13.6 kg); mean BMI was 23.16 ± 4.56 kg/m2. All patients presented with either NYHA class of III (50.3%; n=68) or IV (49.7%; n=67); mean ejection fraction was 22.54 ± 5.7% (10 - 35%, IQ:20 - 25). The VO2 max of the patients ranged from 3 to 32 ml/kg/minute (mean 12.85 ± 4.49 ml/kg/minute). Respiratory exchange ratio was over 1 for all patients (1.12 - 1.96, mean = 1.36 ± 0.187). There was a negative correlation with age (r = -0.204; p = 0.028) whereas a positive correlation was found with exercise time (r = 0.684; p = 0.000), hemoglobin (r = 0.190; p = 0.047) and ejection fraction (r = 0.187 ; p = 0.044). Conclusion: Cardiopulmonary exercise testing in a high-risk heart failure cohort is safe and provides information beyond the routine clinical evaluation of heart failure patients. (author)

  11. The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

    Science.gov (United States)

    Anil, Duygu

    2008-01-01

    In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

  12. Predictions and Experimental Microstructural Characterization of High Strain Rate Failure Modes in Layered Aluminum Composites

    Science.gov (United States)

    Khanikar, Prasenjit

    Different aluminum alloys can be combined, as composites, for tailored dynamic applications. Most investigations pertaining to metallic alloy layered composites, however, have been based on quasi-static approaches. The dynamic failure of layered metallic composites, therefore, needs to be characterized in terms of strength, toughness, and fracture response. A dislocation-density based crystalline plasticity formulation, finite-element techniques, rational crystallographic orientation relations and a new fracture methodology were used to predict the failure modes associated with the high strain rate behavior of aluminum layered composites. Two alloy layers, a high strength alloy, aluminum 2195, and an aluminum alloy 2139, with high toughness, were modeled with representative microstructures that included precipitates, dispersed particles, and different grain boundary (GB) distributions. The new fracture methodology, based on an overlap method and phantom nodes, is used with a fracture criteria specialized for fracture on different cleavage planes. One of the objectives of this investigation, therefore, was to determine the optimal arrangements of the 2139 and 2195 aluminum alloys for a metallic layered composite that would combine strength, toughness and fracture resistance for high strain-rate applications. Different layer arrangements were investigated for high strain-rate applications, and the optimal arrangement was with the high toughness 2139 layer on the bottom, which provided extensive shear strain localization, and the high strength 2195 layer on the top for high strength resistance. The layer thickness of the bottom high toughness layer also affected the bending behavior of the roll-boned interface and the potential delamination of the layers. Shear strain localization, dynamic cracking and delamination were the mutually competing failure mechanisms for the layered metallic composite, and control of these failure modes can be optimized for high strain

  13. Haemodialysis for post-traumatic acute renal failure - factors predicting outcome.

    Science.gov (United States)

    Machemehl, Thomas; Hsu, Peter; Pahad, Hussein; Williams, Paul; Yilmaz, Tugba H; Vassiliu, Pantelis; Boffard, Kenneth D; Degiannis, Elias; Doll, Dietrich

    2013-07-29

    Post-traumatic acute renal failure requiring renal replacement therapy in an intensive care unit (ICU) is associated with high mortality. To assess indicators of improved survival. This was a retrospective cohort study of 64 consecutive trauma patients (penetrating and blunt trauma and burns) who underwent haemodialysis (HD) over a period of 5 years. Information on pre-hospital and in-hospital resuscitation, trauma scores and physiological scores and daily ICU records were collected. The majority of the patients were dialysed with continuous venovenous haemofiltration in the early years of the study and later with sustained low-efficiency dialysis. Of the 64 patients 47 died, giving an overall mortality rate of 73%. Mortality was highest in the burns patients (84%). Survival in all patients, irrespective of injury, was unrelated to the Revised Trauma Score, Injury Severity Score, Acute Physiology and Chronic Health Evaluation Score or Trauma Injury Severity Score. The duration of HD did not differ significantly between the three trauma groups, and age was not a significant predictor of survival. Patients who were polyuric at the time of the initiation of HD had a lower mortality rate than those who were oliguric, anuric or normouric, although this did not reach statistical significance (p=0.09). Acute renal failure in trauma patients is associated with a low survival rate. Controversial conclusions have been presented in the literature. In this study, none of the parameters previously reported to affect survival proved to be valid, although the number of patients was comparable with those in other studies. Since understanding of the predictors and course of renal failure in trauma patients is still at an early stage, there is a need for multicentre prospective studies.

  14. Parameters affecting the resilience of scale-free networks to random failures.

    Energy Technology Data Exchange (ETDEWEB)

    Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran (University of New Mexico, Albuquerque, NM); Saia, Jared (University of New Mexico, Albuquerque, NM)

    2005-09-01

    It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

  15. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

    Directory of Open Access Journals (Sweden)

    Sun Zhangzhen

    2012-08-01

    Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

  16. Failure mode prediction for composite structural insulated panels with MgO board facings

    Science.gov (United States)

    Smakosz, Łukasz; Kreja, Ireneusz

    2018-01-01

    Sandwich panels are readily used in civil engineering due to their high strength to weight ratio and the ease and speed of assembly. The idea of a sandwich section is to combine thin and durable facings with a light-weight core and the choice of materials used allows obtaining the desired behaviour. Panels in consideration consist of MgO (magnesium oxide) board facings and expanded polystyrene core and are characterized by immunity to biological corrosion, a high thermal insulation and a relatively low impact on environment. Customizing the range of panels to meet market needs requires frequent size changes, leading to different failure modes, which are identified in a series of costly full-scale laboratory tests. A nonlinear numerical model was created with a use of a commercial ABAQUS code and a user-defined procedure, which is able to reproduce observed failure mechanisms; its parameters were established on the basis of small-scale tests and numerical experiments. The model was validated by a comparison with the results of the full-scale bending and compression tests. The results obtained were in satisfactory agreement with the test data.

  17. Toward an Efficient Prediction of Solar Flares: Which Parameters, and How?

    Directory of Open Access Journals (Sweden)

    Manolis K. Georgoulis

    2013-11-01

    Full Text Available Solar flare prediction has become a forefront topic in contemporary solar physics, with numerous published methods relying on numerous predictive parameters, that can even be divided into parameter classes. Attempting further insight, we focus on two popular classes of flare-predictive parameters, namely multiscale (i.e., fractal and multifractal and proxy (i.e., morphological parameters, and we complement our analysis with a study of the predictive capability of fundamental physical parameters (i.e., magnetic free energy and relative magnetic helicity. Rather than applying the studied parameters to a comprehensive statistical sample of flaring and non-flaring active regions, that was the subject of our previous studies, the novelty of this work is their application to an exceptionally long and high-cadence time series of the intensely eruptive National Oceanic and Atmospheric Administration (NOAA active region (AR 11158, observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Aiming for a detailed study of the temporal evolution of each parameter, we seek distinctive patterns that could be associated with the four largest flares in the AR in the course of its five-day observing interval. We find that proxy parameters only tend to show preflare impulses that are practical enough to warrant subsequent investigation with sufficient statistics. Combining these findings with previous results, we conclude that: (i carefully constructed, physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting; and (ii the time series of promising parameters may be as important as their instantaneous values. Value-based prediction is the only approach followed so far. Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities.

  18. Living Donor Liver Transplantation for Acute Liver Failure : Comparing Guidelines on the Prediction of Liver Transplantation.

    Science.gov (United States)

    Yoshida, Kazuhiro; Umeda, Yuzo; Takaki, Akinobu; Nagasaka, Takeshi; Yoshida, Ryuichi; Nobuoka, Daisuke; Kuise, Takashi; Takagi, Kosei; Yasunaka, Tetsuya; Okada, Hiroyuki; Yagi, Takahito; Fujiwara, Toshiyoshi

    2017-10-01

    Determining the indications for and timing of liver transplantation (LT) for acute liver failure (ALF) is essential. The King's College Hospital (KCH) guidelines and Japanese guidelines are used to predict the need for LT and the outcomes in ALF. These guidelines' accuracy when applied to ALF in different regional and etiological backgrounds may differ. Here we compared the accuracy of new (2010) Japanese guidelines that use a simple scoring system with the 1996 Japanese guidelines and the KCH criteria for living donor liver transplantation (LDLT). We retrospectively analyzed 24 adult ALF patients (18 acute type, 6 sub-acute type) who underwent LDLT in 1998-2009 at our institution. We assessed the accuracies of the 3 guidelines' criteria for ALF. The overall 1-year survival rate was 87.5%. The new and previous Japanese guidelines were superior to the KCH criteria for accurately predicting LT for acute-type ALF (72% vs. 17%). The new Japanese guidelines could identify 13 acute-type ALF patients for LT, based on the timing of encephalopathy onset. Using the previous Japanese guidelines, although the same 13 acute-type ALF patients (72%) had indications for LT, only 4 patients were indicated at the 1st step, and it took an additional 5 days to decide the indication at the 2nd step in the other 9 cases. Our findings showed that the new Japanese guidelines can predict the indications for LT and provide a reliable alternative to the previous Japanese and KCH guidelines.

  19. RSA prediction of high failure rate for the uncoated Interax TKA confirmed by meta-analysis.

    Science.gov (United States)

    Pijls, Bart G; Nieuwenhuijse, Marc J; Schoones, Jan W; Middeldorp, Saskia; Valstar, Edward R; Nelissen, Rob G H H

    2012-04-01

    In a previous radiostereometric (RSA) trial the uncoated, uncemented, Interax tibial components showed excessive migration within 2 years compared to HA-coated and cemented tibial components. It was predicted that this type of fixation would have a high failure rate. The purpose of this systematic review and meta-analysis was to investigate whether this RSA prediction was correct. We performed a systematic review and meta-analysis to determine the revision rate for aseptic loosening of the uncoated and cemented Interax tibial components. 3 studies were included, involving 349 Interax total knee arthroplasties (TKAs) for the comparison of uncoated and cemented fixation. There were 30 revisions: 27 uncoated and 3 cemented components. There was a 3-times higher revision rate for the uncoated Interax components than that for cemented Interax components (OR = 3; 95% CI: 1.4-7.2). This meta-analysis confirms the prediction of a previous RSA trial. The uncoated Interax components showed the highest migration and turned out to have the highest revision rate for aseptic loosening. RSA appears to enable efficient detection of an inferior design as early as 2 years postoperatively in a small group of patients.

  20. Proteins Annexin A2 and PSA in Prostate Cancer Biopsies Do Not Predict Biochemical Failure.

    Science.gov (United States)

    Lamb, David S; Sondhauss, Sven; Dunne, Jonathan C; Woods, Lisa; Delahunt, Brett; Ferguson, Peter; Murray, Judith; Nacey, John N; Denham, James W; Jordan, T William

    2017-12-01

    We previously reported the use of mass spectrometry and western blotting to identify proteins from tumour regions of formalin-fixed paraffin-embedded biopsies from 16 men who presented with apparently localized prostate cancer, and found that annexin A2 (ANXA2) appeared to be a better predictor of subsequent biochemical failure than prostate-specific antigen (PSA). In this follow-up study, ANXA2 and PSA were measured using western blotting of proteins extracted from biopsies from 37 men from a subsequent prostate cancer trial. No significant differences in ANXA2 and PSA levels were observed between men with and without biochemical failure. The statistical effect sizes were small, d=0.116 for ANXA2, and 0.266 for PSA. ANXA2 and PSA proteins measured from biopsy tumour regions are unlikely to be good biomarkers for prediction of the clinical outcome of prostate cancer presenting with apparently localized disease. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  1. Perineural invasion on prostate needle biopsy does not predict biochemical failure following brachytherapy for prostate cancer

    International Nuclear Information System (INIS)

    Weight, Christopher J.; Ciezki, Jay P.; Reddy, Chandana A.; Zhou Ming; Klein, Eric A.

    2006-01-01

    Purpose: To determine if the presence of perineural invasion (PNI) predicts biochemical recurrence in patients who underwent low-dose-rate brachytherapy for the treatment of localized prostate cancer. Methods and Materials: A retrospective case control matching study was performed. The records of 651 patients treated with brachytherapy between 1996 and 2003 were reviewed. Sixty-three of these patients developed biochemical failure. These sixty-three patients were then matched in a one-to-one ratio to patients without biochemical failure, controlling for biopsy Gleason score, clinical stage, initial prostate-specific antigen, age, and the use of androgen deprivation. The pathology of the entire cohort was then reviewed for evidence of perineural invasion on initial prostate biopsy specimens. The biochemical relapse free survival rates for these two groups were compared. Results: Cases and controls were well matched, and there were no significant differences between the two groups in age, Gleason grade, clinical stage, initial prostate-specific antigen, and the use of androgen deprivation. PNI was found in 19 (17%) patients. There was no significant difference in the rates of PNI between cases and controls, 19.6% and 14.3% respectively (p 0.45). PNI did not correlate with biochemical relapse free survival (p 0.40). Conclusion: Perineural invasion is not a significant predictor of biochemical recurrence in patients undergoing brachytherapy for prostate cancer

  2. Lack of pro-inflammatory cytokine mobilization predicts poor prognosis in patients with acute heart failure.

    Science.gov (United States)

    Vistnes, M; Høiseth, A D; Røsjø, H; Nygård, S; Pettersen, E; Søyseth, V; Hurlen, P; Christensen, G; Omland, T

    2013-03-01

    The aim of this study was to gain insight in the inflammatory response in acute heart failure (AHF) by assessing (1) plasma cytokine profiles and (2) prognostic value of circulating cytokines in AHF patients. Plasma levels of 26 cytokines were quantified by multiplex protein arrays in 36 patients with congestive AHF, characterized by echocardiographic, radiologic, and clinical examinations on admission, during hospitalization and at discharge. Recurrent AHF leading to death or readmission constituted the combined end point, and all patients were followed for 120 days after discharge. Levels of 15 of the measured cytokines were higher in AHF than in healthy subjects (n=22) on admission. Low levels of MCP-1, IL-1β and a low IL-1β/IL-1ra ratio predicted fatal and non-fatal AHF within 120 days. Patients with low circulating levels of IL-1β had lower left ventricular ejection fraction and higher levels of N-terminal pro-B-type natriuretic peptide, while patients with low levels of MCP-1 had higher E/E' and inferior caval vein diameter, than patients with high levels. Immune activation, reflected in increased cytokine levels, is present in AHF patients. Interestingly, failure to increase secretion of IL-1β and MCP-1 during AHF is associated with poor outcome. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Development of computer code for determining prediction parameters of radionuclide migration in soil layer

    International Nuclear Information System (INIS)

    Ogawa, Hiromichi; Ohnuki, Toshihiko

    1986-07-01

    A computer code (MIGSTEM-FIT) has been developed to determine the prediction parameters, retardation factor, water flow velocity, dispersion coefficient, etc., of radionuclide migration in soil layer from the concentration distribution of radionuclide in soil layer or in effluent. In this code, the solution of the predicting equation for radionuclide migration is compared with the concentration distribution measured, and the most adequate values of parameter can be determined by the flexible tolerance method. The validity of finite differential method, which was one of the method to solve the predicting equation, was confirmed by comparison with the analytical solution, and also the validity of fitting method was confirmed by the fitting of the concentration distribution calculated from known parameters. From the examination about the error, it was found that the error of the parameter obtained by using this code was smaller than that of the concentration distribution measured. (author)

  4. Prediction of betavoltaic battery output parameters based on SEM measurements and Monte Carlo simulation

    International Nuclear Information System (INIS)

    Yakimov, Eugene B.

    2016-01-01

    An approach for a prediction of "6"3Ni-based betavoltaic battery output parameters is described. It consists of multilayer Monte Carlo simulation to obtain the depth dependence of excess carrier generation rate inside the semiconductor converter, a determination of collection probability based on the electron beam induced current measurements, a calculation of current induced in the semiconductor converter by beta-radiation, and SEM measurements of output parameters using the calculated induced current value. Such approach allows to predict the betavoltaic battery parameters and optimize the converter design for any real semiconductor structure and any thickness and specific activity of beta-radiation source. - Highlights: • New procedure for betavoltaic battery output parameters prediction is described. • A depth dependence of beta particle energy deposition for Si and SiC is calculated. • Electron trajectories are assumed isotropic and uniformly started under simulation.

  5. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

    Science.gov (United States)

    Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

    2015-09-15

    The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Radioisotope albumin flux measurement of microvascular lung permeability: an independent parameter in acute respiratory failure?

    International Nuclear Information System (INIS)

    Hoegerle, S.; Nitzsche, E.U.; Reinhardt, M.J.; Moser, E.; Benzing, A.; Geiger, K.; Schulte Moenting, J.

    2001-01-01

    Aim: To evaluate the extent to which single measurements of microvascular lung permeability may be relevant as an additional parameter in a heterogenous clinical patient collective with Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS). Methods: In 36 patients with pneumonia (13), non pneumogenic sepsis (9) or trauma (14) meeting the consensus conference criteria of ALI or ARDS double-isotope protein flux measurements ( 51 Cr erythrocytes as intravascular tracer, Tc-99m human albumin as diffusible tracer) of microvascular lung permeability were performed using the Normalized Slope Index (NSI). The examination was to determine whether there is a relationship between the clinical diagnosis of ALI/ARDS, impaired permeability and clinical parameters, that is the underlying disease, oxygenation, duration of mechanical ventilation and mean pulmonary-artery pressure (PAP). Results: At the time of study, 25 patients presented with increased permeability (NSI > 1 x 10 -3 min -1 ) indicating an exudative stage of disease, and 11 patients with normal permeability. The permeability impairment correlated with the underlying disease (p > 0.05). With respect to survival, there was a negative correlation to PAP (p [de

  7. Prediction of pork quality parameters by applying fractals and data mining on MRI

    DEFF Research Database (Denmark)

    Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés

    2017-01-01

    This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One...... Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear...... regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate...

  8. Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

    Science.gov (United States)

    Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

    2016-01-01

    Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

  9. Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

    Directory of Open Access Journals (Sweden)

    Zhigang Li

    Full Text Available Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

  10. [The Predictive Factors of Stent Failure in the Treatment of Malignant Extrinsc Ureteral Obstruction Using Internal Ureteral Stents].

    Science.gov (United States)

    Matsuura, Hiroshi; Arase, Shigeki; Hori, Yasuhide; Tochigi, Hiromi

    2017-12-01

    In this study, we retrospectively reviewed the experiences at our single institute in the treatment of malignant extrinsic ureteral obstruction (MUO) using ureteral stents to investigate the clinical outcomes and the predictive factors of stent failure. In 52 ureters of 38 patients who had radiologically significant hydronephrosis due to MUO, internal ureteral stents (The BARD(R) INLAY(TM) ureteral stent set) were inserted. The median follow-up interval after the initial stent insertion was 124.5 days (4-1,120). Stent failure occurred in 8 ureters (15.4%) of the 7 patients. The median interval from the first stent insertion to stent failure was 88 days (1-468). A Cox regression multivariate analysis showed that the significant predictors of stent failure were bladder invasion. Based on the possibility of stent failure, the adaptation of the internal ureteral stent placement should be considered especially in a patient with MUO combined with bladder invasion.

  11. An Illustration of Determining Quantitatively the Rock Mass Quality Parameters of the Hoek-Brown Failure Criterion

    Science.gov (United States)

    Wu, Li; Adoko, Amoussou Coffi; Li, Bo

    2018-04-01

    In tunneling, determining quantitatively the rock mass strength parameters of the Hoek-Brown (HB) failure criterion is useful since it can improve the reliability of the design of tunnel support systems. In this study, a quantitative method is proposed to determine the rock mass quality parameters of the HB failure criterion, namely the Geological Strength Index (GSI) and the disturbance factor ( D) based on the structure of drilling core and weathering condition of rock mass combined with acoustic wave test to calculate the strength of rock mass. The Rock Mass Structure Index and the Rock Mass Weathering Index are used to quantify the GSI while the longitudinal wave velocity ( V p) is employed to derive the value of D. The DK383+338 tunnel face of Yaojia tunnel of Shanghai-Kunming passenger dedicated line served as illustration of how the methodology is implemented. The values of the GSI and D are obtained using the HB criterion and then using the proposed method. The measured in situ stress is used to evaluate their accuracy. To this end, the major and minor principal stresses are calculated based on the GSI and D given by HB criterion and the proposed method. The results indicated that both methods were close to the field observation which suggests that the proposed method can be used for determining quantitatively the rock quality parameters, as well. However, these results remain valid only for rock mass quality and rock type similar to those of the DK383+338 tunnel face of Yaojia tunnel.

  12. Implicit and Explicit Attitudes Predict Smoking Cessation: Moderating Effects of Experienced Failure to Control Smoking and Plans to Quit

    OpenAIRE

    Chassin, Laurie; Presson, Clark C.; Sherman, Steven J.; Seo, Dong-Chul; Macy, Jon

    2010-01-01

    The current study tested implicit and explicit attitudes as prospective predictors of smoking cessation in a Midwestern community sample of smokers. Results showed that the effects of attitudes significantly varied with levels of experienced failure to control smoking and plans to quit. Explicit attitudes significantly predicted later cessation among those with low (but not high or average) levels of experienced failure to control smoking. Conversely, however, implicit attitudes significantly...

  13. Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

    OpenAIRE

    Stoeber, Joachim; Kobori, Osamu; Tanno, Yoshihiko

    2013-01-01

    Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were as...

  14. Reflow Process Parameters Analysis and Reliability Prediction Considering Multiple Characteristic Values

    Directory of Open Access Journals (Sweden)

    Guo Yu

    2016-01-01

    Full Text Available As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.

  15. Determination of rock fracture parameters from crack models for failure in compression

    International Nuclear Information System (INIS)

    Kemeny, J.M.; Cook, N.G.W.

    1987-01-01

    Micromechanical models for axial splitting and for shear faulting are used to investigate parameters associated with rock fracture under compressive stresses. The fracture energies to create splitting fractures and shear faults are calculated using laboratory triaxial data. These energies are compared with the fracture energies for the propagation of microcracks that coalesce to form the larger scale fractures. It is found that for Westerly granite, the energies to create splitting fractures and shear faults are about three orders of magnitude greater than the energy needed to drive the tensile microcracks, due to the large amount of subsidiary crack surface area created in forming the larger scale fractures. A similar scale effect can be expected when extrapolating the laboratory results to field scale problems

  16. Bayesian framework for prediction of future number of failures from a single group of units in the field

    International Nuclear Information System (INIS)

    Ebrahimi, Nader

    2009-01-01

    This paper considers prediction of unknown number of failures in a future inspection of a group of in-service units based on number of failures observed from an earlier inspection. We develop a flexible Bayesian model and calculate Bayesian estimator for this unknown number and other quantities of interest. The paper also includes an illustration of our method in an example about heat exchanger. A main advantage of our approach is in its nonparametric nature. By nonparametric here we simply mean that no assumption is required about the failure time distribution of a unit

  17. Gender and age related predictive value of walk test in heart failure: do anthropometrics matter in clinical practice?

    Science.gov (United States)

    Frankenstein, L; Remppis, A; Graham, J; Schellberg, D; Sigg, C; Nelles, M; Katus, H A; Zugck, C

    2008-07-21

    The six-minute walk test (6 WT) is a valid and reliable predictor of morbidity and mortality in chronic heart failure (CHF) patients, frequently used as an endpoint or target in clinical trials. As opposed to spiroergometry, improvement of its prognostic accuracy by correction for height, weight, age and gender has not yet been attempted comprehensively despite known influences of these parameters. We recorded the 6 WT of 1035 CHF patients, attending clinic from 1995 to 2005. The 1-year prognostic value of 6 WT was calculated, alone and after correction for height, weight, BMI and/or age. Analysis was performed on the entire cohort, on males and females separately and stratified according to BMI (30 kg/m(2)). 6 WT weakly correlated with age (r=-0.32; p<0.0001), height (r=0.2; p<0.0001), weight (r=0.11; p<0.001), not with BMI (r=0.01; p=ns). The 6 WT was a strong predictor of 1-year mortality in both genders, both as a single and age corrected parameter. Parameters derived from correction of 6 WT for height, weight or BMI did not improve the prognostic value in univariate analysis for either gender. Comparison of the receiver operated characteristics showed no significant gain in prognostic accuracy from any derived variable, either for males or females. The six-minute walk test is a valid tool for risk prediction in both male and female CHF patients. In both genders, correcting 6 WT distance for height, weight or BMI alone, or adjusting for age, does not increase the prognostic power of this tool.

  18. Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

    Science.gov (United States)

    Hsu, Ling-Yuan; Chen, Tsung-Lin

    2012-01-01

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

  19. Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure.

    Science.gov (United States)

    Wanvarie, Samkaew; Sathapatayavongs, Boonmee

    2007-09-01

    The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.

  20. Do plasma concentrations of apelin predict prognosis in patients with advanced heart failure?

    Science.gov (United States)

    Dalzell, Jonathan R; Jackson, Colette E; Chong, Kwok S; McDonagh, Theresa A; Gardner, Roy S

    2014-01-01

    Apelin is an endogenous vasodilator and inotrope, plasma concentrations of which are reduced in advanced heart failure (HF). We determined the prognostic significance of plasma concentrations of apelin in advanced HF. Plasma concentrations of apelin were measured in 182 patients with advanced HF secondary to left ventricular systolic dysfunction. The predictive value of apelin for the primary end point of all-cause mortality was assessed over a median follow-up period of 544 (IQR: 196-923) days. In total, 30 patients (17%) reached the primary end point. Of those patients with a plasma apelin concentration above the median, 14 (16%) reached the primary end point compared with 16 (17%) of those with plasma apelin levels below the median (p = NS). NT-proBNP was the most powerful prognostic marker in this population (log rank statistic: 10.37; p = 0.001). Plasma apelin concentrations do not predict medium to long-term prognosis in patients with advanced HF secondary to left ventricular systolic dysfunction.

  1. Some clinical and laboratory parameters of hemorrhagic fever with renal failure syndrome against the background correction of hyperhomocysteinemia

    Directory of Open Access Journals (Sweden)

    G. R. Syrtlanova

    2013-01-01

    Full Text Available The aim of the research was to curry out correction of hyperhomocysteinemia in patients with hemorrhagic fever with renal failure syndrome (HFRS and evaluate some clinical and laboratory parameters. The results of examining 91 men patients with HFRS at the age of 18 till 50 years old are presented. The vitamin complex «Angiovit» was used for hyperhomocysteinemia correction. The correction of hyperhomocysteinemia using the vitamin complex in all the three considered forms (mid, severs, complicated there are statistically significant decreases of homocysteine, the duration of oliguric stage and the concentration of creatinine in oliguric and diuretic stages in patients with HFRS compared with the group of patients having the generally basically therapy.

  2. Calculation of Core Damage Frequency for the Change of the Common Cause Failure Parameters According to the Testing Strategies

    International Nuclear Information System (INIS)

    Kang, Dae Il; Kim, Kil You; Jin, Young Ho; Kim, Tae Woon

    2011-01-01

    Common cause failure (CCF) probabilities are differently estimated according to testing strategies. There are two representative testing schemes; staggered testing and non-staggered testing schemes. For the cases where trains or channels of standby safety systems consisting of more than two redundant components are tested in a staggered manner, the standby safety components within a train can be tested simultaneously or consecutively. In this case, mixed testing scheme, staggered and non-staggered testing schemes, are used for testing the components. Kang et al. derived the formulas for the estimations of the CCF probabilities of the components under the mixed testing scheme. This paper presents the sensitivity study results on the core damage frequency (CDF) of the SMART (System-integrated Modular Advanced Reactor) for the changes of the CCF parameters according to the testing strategies

  3. The influence of temperature on low cycle fatigue behavior of prior cold worked 316L stainless steel (II) : life prediction and failure mechanism

    International Nuclear Information System (INIS)

    Hong, Seong Gu; Yoon, Sam Son; Lee, Soon Bok

    2003-01-01

    Tensile and low cycle fatigue tests on prior cold worked 316L stainless steel were carried out at various temperatures from room temperature to 650 deg. C. Fatigue resistance was decreased with increasing temperature and decreasing strain rate. Cyclic plastic deformation, creep, oxidation and interactions with each other are thought to be responsible for the reduction in fatigue resistance. Currently favored life prediction models were examined and it was found that it is important to select a proper life prediction parameter since stress-strain relation strongly depends on temperature. A phenomenological life prediction model was proposed to account for the influence of temperature on fatigue life and assessed by comparing with experimental result. LCF failure mechanism was investigated by observing fracture surfaces of LCF failed specimens with SEM

  4. Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2015-07-01

    Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.

  5. Stochastic failure modelling of unidirectional composite ply failure

    International Nuclear Information System (INIS)

    Whiteside, M.B.; Pinho, S.T.; Robinson, P.

    2012-01-01

    Stochastic failure envelopes are generated through parallelised Monte Carlo Simulation of a physically based failure criteria for unidirectional carbon fibre/epoxy matrix composite plies. Two examples are presented to demonstrate the consequence on failure prediction of both statistical interaction of failure modes and uncertainty in global misalignment. Global variance-based Sobol sensitivity indices are computed to decompose the observed variance within the stochastic failure envelopes into contributions from physical input parameters. The paper highlights a selection of the potential advantages stochastic methodologies offer over the traditional deterministic approach.

  6. Can we predict uranium bioavailability based on soil parameters? Part 1: effect of soil parameters on soil solution uranium concentration.

    Science.gov (United States)

    Vandenhove, H; Van Hees, M; Wouters, K; Wannijn, J

    2007-01-01

    Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for (238)U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K(d), L kg(-1)) and the organic matter content (R(2)=0.70) and amorphous Fe content (R(2)=0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH=6, log(K(d)) was linearly related with pH [log(K(d))=-1.18 pH+10.8, R(2)=0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex.

  7. Assessment of heart rate, acidosis, consciousness, oxygenation, and respiratory rate to predict noninvasive ventilation failure in hypoxemic patients.

    Science.gov (United States)

    Duan, Jun; Han, Xiaoli; Bai, Linfu; Zhou, Lintong; Huang, Shicong

    2017-02-01

    To develop and validate a scale using variables easily obtained at the bedside for prediction of failure of noninvasive ventilation (NIV) in hypoxemic patients. The test cohort comprised 449 patients with hypoxemia who were receiving NIV. This cohort was used to develop a scale that considers heart rate, acidosis, consciousness, oxygenation, and respiratory rate (referred to as the HACOR scale) to predict NIV failure, defined as need for intubation after NIV intervention. The highest possible score was 25 points. To validate the scale, a separate group of 358 hypoxemic patients were enrolled in the validation cohort. The failure rate of NIV was 47.8 and 39.4% in the test and validation cohorts, respectively. In the test cohort, patients with NIV failure had higher HACOR scores at initiation and after 1, 12, 24, and 48 h of NIV than those with successful NIV. At 1 h of NIV the area under the receiver operating characteristic curve was 0.88, showing good predictive power for NIV failure. Using 5 points as the cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for NIV failure were 72.6, 90.2, 87.2, 78.1, and 81.8%, respectively. These results were confirmed in the validation cohort. Moreover, the diagnostic accuracy for NIV failure exceeded 80% in subgroups classified by diagnosis, age, or disease severity and also at 1, 12, 24, and 48 h of NIV. Among patients with NIV failure with a HACOR score of >5 at 1 h of NIV, hospital mortality was lower in those who received intubation at ≤12 h of NIV than in those intubated later [58/88 (66%) vs. 138/175 (79%); p = 0.03). The HACOR scale variables are easily obtained at the bedside. The scale appears to be an effective way of predicting NIV failure in hypoxemic patients. Early intubation in high-risk patients may reduce hospital mortality.

  8. Peak Exercise Oxygen Uptake Predicts Recurrent Admissions in Heart Failure With Preserved Ejection Fraction.

    Science.gov (United States)

    Palau, Patricia; Domínguez, Eloy; Núñez, Eduardo; Ramón, José María; López, Laura; Melero, Joana; Sanchis, Juan; Bellver, Alejandro; Santas, Enrique; Bayes-Genis, Antoni; Chorro, Francisco J; Núñez, Julio

    2018-04-01

    Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent syndrome with an elevated risk of morbidity and mortality. To date, there is scarce evidence on the role of peak exercise oxygen uptake (peak VO 2 ) for predicting the morbidity burden in HFpEF. We sought to evaluate the association between peak VO 2 and the risk of recurrent hospitalizations in patients with HFpEF. A total of 74 stable symptomatic patients with HFpEF underwent a cardiopulmonary exercise test between June 2012 and May 2016. A negative binomial regression method was used to determine the association between the percentage of predicted peak VO 2 (pp-peak VO 2 ) and recurrent hospitalizations. Risk estimates are reported as incidence rate ratios. The mean age was 72.5 ± 9.1 years, 53% were women, and all patients were in New York Heart Association functional class II to III. Mean peak VO 2 and median pp-peak VO 2 were 10 ± 2.8mL/min/kg and 60% (range, 47-67), respectively. During a median follow-up of 276 days [interquartile range, 153-1231], 84 all-cause hospitalizations in 31 patients (41.9%) were registered. A total of 15 (20.3%) deaths were also recorded. On multivariate analysis, accounting for mortality as a terminal event, pp-peak VO 2 was independently and linearly associated with the risk of recurrent admission. Thus, and modeled as continuous, a 10% decrease of pp-peak VO 2 increased the risk of recurrent hospitalizations by 32% (IRR, 1.32; 95%CI, 1.03-1.68; P = .028). In symptomatic elderly patients with HFpEF, pp-peak VO 2 predicts all-cause recurrent admission. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

  9. Prediction factors for failure to seek treatment following traumatic dental injuries to primary teeth

    Directory of Open Access Journals (Sweden)

    Ramon Targino Firmino

    2014-06-01

    Full Text Available The objective of this study was to evaluate prediction factors for failure to seek treatment following a traumatic dental injury (TDI to primary teeth among preschool children in the city of Campina Grande, Brazil. A cross-sectional study was carried out involving 277 children 3 to 5 years of age, with TDI, enrolled in public and private preschools. Parents filled out a form addressing demographic data and whether or not they had sought treatment. Clinical examinations were performed by three dentists who had undergone a calibration exercise (Kappa: 0.85 to 0.90 for the evaluation of TDI. Bivariate and multivariate Poisson regression models were constructed (α = 5%. Enamel fracture was the most prevalent type of TDI (48.7% and the upper central incisors were the most affected teeth (88.4%. The frequency of seeking dental treatment was low (9.7%. The following variables were associated with failure to seek treatment following TDI: a household income greater than one minimum wage (PR = 1.170; 95%CI 1.018-1.341, parents/caregivers’ perception of a child’s oral health as poor (PR = 1.100; 95%CI 1.026-1.176, and the non-perception of TDI by parents/caregivers (PR = 1.250; 95%CI 1.142-1.360. In the present study, the frequency of seeking treatment following TDI was low, and parents/caregivers with a higher income, a poor perception of their child’s oral health and a lack of awareness regarding the trauma were more likely to fail to seek treatment following TDI to primary teeth.

  10. PREDICTION AND PREVENTION OF LIVER FAILURE AFTER MAJOR LIVER PRIMARY AND METASTATIC TUMORS RESECTION

    Directory of Open Access Journals (Sweden)

    A. D. Kaprin

    2016-01-01

    Full Text Available Abstract Purpose of the study. Improvement of results of treatment in patients with primary and metastatic liver cancer by decreasing the risk of post-resection liver failure on the basis of the evaluation of the functional reserves of the liver.Materials and Methods. The study included two independent samples of patients operated about primary or metastatic lesions of the liver at the Department of abdominal Oncology, P. A. Hertsen MORI. The first group included 53 patients who carried out 13C-breath test metallimovie and dynamic scintigraphy of the liver in the preoperative stage in addition to the standard algorithm of examination. Patients of the 2nd group (n=35 had a standard clinical and laboratory examination, the patients were not performed the preoperative evaluation of the functional reserve of the liver, the incidences of total bilirubin, albumin and prothrombin time did not reveal a reduction of liver function. Post-resection liver failure have been established on the basis of the 50/50 criterion in the evaluation on day 5 after surgery.Results. Analysis of operating characteristics of the functional tests showed the absolute methacin breath test sensitivity (SE≥100%, high specificity (SP≥67% of scintigraphy of the liver and the negative predictive value of outcome (VP≥100% at complex use of two diagnostic methods. The incidence of PROPS in the study group was significantly 2 times higher in the control group –15,1% and 26.8%, respectively (p<0.001.Conclusion. The combination of preoperative dynamic scintigraphy of the liver with carrying out 13C-breath methacin test allows you to conduct a comprehensive evaluation of the liver functional reserve and can significantly improve preoperative evaluation and postoperative results of anatomic resection in patients with primary and metastatic liver lesions.

  11. Systolic Strain Abnormalities to Predict Hospital Readmission in Patients With Heart Failure and Normal Ejection Fraction

    Science.gov (United States)

    Borer, Steven M.; Kokkirala, Aravind; O'Sullivan, David M.; Silverman, David I.

    2011-01-01

    Background Despite intensive investigation, the pathogenesis of heart failure with normal ejection fraction (HFNEF) remains unclear. We hypothesized that subtle abnormalities of systolic function might play a role, and that abnormal systolic strain and strain rate would provide a marker for adverse outcomes. Methods Patients of new CHF and left ventricular ejection fraction > 50% were included. Exclusion criteria were recent myocardial infarction, severe valvular heart disease, severe left ventricular hypertrophy (septum >1.8 cm), or a technically insufficient echocardiogram. Average peak systolic strain and strain rate were measured using an off-line grey scale imaging technique. Systolic strain and strain rate for readmitted patients were compared with those who remained readmission-free. Results One hundred consecutive patients with a 1st admission for HFNEF from January 1, 2004 through December 31, 2007, inclusive, were analyzed. Fifty two patients were readmitted with a primary diagnosis of heart failure. Systolic strain and strain rates were reduced in both study groups compared to controls. However, systolic strain did not differ significantly between the two groups (-11.7% for those readmitted compared with -12.9% for those free from readmission, P = 0.198) and systolic strain rates also were similar (-1.05 s-1 versus -1.09 s-1, P = 0.545). E/e’ was significantly higher in readmitted patients compared with those who remained free from readmission (14.5 versus 11.0, P = 0.013). E/e’ (OR 1.189, 95% CI 1.026-1.378; P = 0.021) was found to be an independent predictor for HFNEF readmission. Conclusions Among patients with new onset HFNEF, SS and SR rates are reduced compared with patients free of HFNEF, but do not predict hospital readmission. Elevated E/e’ is a predictor of readmission in these patients. PMID:28352395

  12. Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

    International Nuclear Information System (INIS)

    Hao, Ke; Sham, Pak C; Poon, Ronnie TP; Luk, John M; Lee, Nikki PY; Mao, Mao; Zhang, Chunsheng; Ferguson, Mark D; Lamb, John; Dai, Hongyue; Ng, Irene O

    2009-01-01

    Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new

  13. Predictions of the marviken subcooled critical mass flux using the critical flow scaling parameters

    Energy Technology Data Exchange (ETDEWEB)

    Park, Choon Kyung; Chun, Se Young; Cho, Seok; Yang, Sun Ku; Chung, Moon Ki [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

  14. Predictions of the marviken subcooled critical mass flux using the critical flow scaling parameters

    Energy Technology Data Exchange (ETDEWEB)

    Park, Choon Kyung; Chun, Se Young; Cho, Seok; Yang, Sun Ku; Chung, Moon Ki [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

  15. Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Ahmed M. Al Rajeh

    2016-11-01

    Full Text Available Introduction: The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review. Objectives: To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation. Methods: An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline, Excerpta Medica dataBASE (Embase, Allied and Complementary Medicine Database (AMED, Cumulative Index of Nursing and Allied Health Literature (CINAHL and the Cochrane clinical trials database. Results: Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO2% prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients. Conclusion: There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.

  16. Computer simulation for prediction of performance and thermodynamic parameters of high energy materials

    International Nuclear Information System (INIS)

    Muthurajan, H.; Sivabalan, R.; Talawar, M.B.; Asthana, S.N.

    2004-01-01

    A new code viz., Linear Output Thermodynamic User-friendly Software for Energetic Systems (LOTUSES) developed during this work predicts the theoretical performance parameters such as density, detonation factor, velocity of detonation, detonation pressure and thermodynamic properties such as heat of detonation, heat of explosion, volume of explosion gaseous products. The same code also assists in the prediction of possible explosive decomposition products after explosion and power index. The developed code has been validated by calculating the parameters of standard explosives such as TNT, PETN, RDX, and HMX. Theoretically predicated parameters are accurate to the order of ±5% deviation. To the best of our knowledge, no such code is reported in literature which can predict a wide range of characteristics of known/unknown explosives with minimum input parameters. The code can be used to obtain thermochemical and performance parameters of high energy materials (HEMs) with reasonable accuracy. The code has been developed in Visual Basic having enhanced windows environment, and thereby advantages over the conventional codes, written in Fortran. The theoretically predicted HEMs performance can be directly printed as well as stored in text (.txt) or HTML (.htm) or Microsoft Word (.doc) or Adobe Acrobat (.pdf) format in the hard disk. The output can also be copied into the Random Access Memory as clipboard text which can be imported/pasted in other software as in the case of other codes

  17. Prediction of detonation and JWL eos parameters of energetic materials using EXPLO5 computer code

    CSIR Research Space (South Africa)

    Peter, Xolani

    2016-09-01

    Full Text Available Ballistic Organization Cape Town, South Africa 27-29 September 2016 1 PREDICTION OF DETONATION AND JWL EOS PARAMETERS OF ENERGETIC MATERIALS USING EXPLO5 COMPUTER CODE X. Peter*, Z. Jiba, M. Olivier, I.M. Snyman, F.J. Mostert and T.J. Sono.... Nowadays many numerical methods and programs are being used for carrying out thermodynamic calculations of the detonation parameters of condensed explosives, for example a BKW Fortran (Mader, 1967), Ruby (Cowperthwaite and Zwisler, 1974) TIGER...

  18. Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

    International Nuclear Information System (INIS)

    Gershgorin, B.; Harlim, J.; Majda, A.J.

    2010-01-01

    The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

  19. Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages

    OpenAIRE

    Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

    2016-01-01

    This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies ...

  20. Application of ann for predicting water quality parameters in the mediterranean sea along gaza-palestine

    International Nuclear Information System (INIS)

    Zaqoot, H.A.; Unar, M.A.

    2008-01-01

    Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

  1. Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Gergely Takács

    2014-01-01

    Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

  2. Parameter estimation techniques and uncertainty in ground water flow model predictions

    International Nuclear Information System (INIS)

    Zimmerman, D.A.; Davis, P.A.

    1990-01-01

    Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

  3. Comparison of clinical and laboratory parameters in patients with end-stage renal failure in the outcome of chronic glomerulonephritis and patients with end-stage renal failure in the outcome of other diseases.

    Science.gov (United States)

    Popova, J A; Yadrihinskaya, V N; Krylova, M I; Sleptsovа, S S; Borisovа, N V

    frequent complications of hemodialysis treatments are coagulation disorders. This is due to activation of the coagulation of blood flow in the interaction with a dialysis membrane material vascular prostheses and extracorporeal circuit trunks. In addition, in hemodialysis patients receiving heparin for years, there is depletion of stocks in endothelial cells in tissue factor inhibitor, inhibits the activity of an external blood clotting mechanism. the aim of our study was to evaluate the hemostatic system parameters in patients with end-stage renal failure, depending on the cause of renal failure. to evaluate the hemostatic system parameters in patients with end-stage renal failure, depending on the cause of renal failure and hemodialysis treatment duration conducted a study that included 100 patients observed in the department of chronic hemodialysis and nephrology hospital №1 Republican National Medical Center in the period of 2013-2016. in patients with end-stage renal failure in the outcome of chronic glomerulonephritis, a great expression of activation of blood coagulation confirm increased the mean concentration of fibrinogen, whereas in the group, which included patients with end-stage renal failure in the outcome of other diseases, such is not different from the norm, and a higher rate of hyperfibrinogenemia, identified in 2/3 patients in this group. it was revealed that the state of homeostasis in patients with end-stage renal failure in increasingly characterizes the level of fibrinogen and the activation of the hemostatic markers: soluble fibrin monomer complexes, D-dimers.

  4. Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

    Directory of Open Access Journals (Sweden)

    Bo Zeng

    2014-01-01

    Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

  5. Prediction of ppm level electrical failure by using physical variation analysis

    Science.gov (United States)

    Hou, Hsin-Ming; Kung, Ji-Fu; Hsu, Y.-B.; Yamazaki, Y.; Maruyama, Kotaro; Toyoshima, Yuya; Chen, Chu-en

    2016-03-01

    their spatial correlation distance. For local variations (LV) there is no correlation, whereas for global variations (GV) the correlation distance is very large [7]-[9]. This is the first time to certificate the validation of spatial distribution from the affordable bias contour big data fundamental infrastructures. And then apply statistical techniques to dig out the variation sources. The GV come from systematic issue, which could be compensated by adaptive LT condition or OPC correction. But LV comes from random issue, which being considered as intrinsic problem such as structure, material, tool capability… etc. In this paper studying, we can find out the advanced technology node SRAM contact CD local variation (LV) dominates in total variation, about 70%. It often plays significant in-line real time catching WP-DPMO role of the product yield loss, especially for wafer edge is the worst loss within wafer distribution and causes serious reliability concern. The major root cause of variations comes from the PR material induced burr defect (LV), the second one comes from GV enhanced wafer edge short opportunity, which being attributed to three factors, first one factor is wafer edge CD deliberated enlargement for yield improvement as shown in Fig. 10. Second factor is overlaps/AA shifts due to tool capability dealing with incoming wafer's war page issue and optical periphery layout dependent working pitch issue as shown in Fig. 9 (1)., the last factor comes from wafer edge burr enhanced by wafer edge larger Photo Resistance (PR) spin centrifugal force. After implementing KPIs such as GV related AA/CD indexes as shown in Fig. 9 (1) and 10, respectively, and LV related burr index as shown in Fig. 11., we can construct the parts per million (PPM) level short probability model via multi-variables regression, canonical correlation analysis and logistic transformation. The model provides prediction of PPM level electrical failure by using in-line real time physical

  6. Using ANFIS for selection of more relevant parameters to predict dew point temperature

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Shamshirband, Shahaboddin; Petković, Dalibor; Yee, Por Lip; Mansor, Zulkefli

    2016-01-01

    Highlights: • ANFIS is used to select the most relevant variables for dew point temperature prediction. • Two cities from the central and south central parts of Iran are selected as case studies. • Influence of 5 parameters on dew point temperature is evaluated. • Appropriate selection of input variables has a notable effect on prediction. • Considering the most relevant combination of 2 parameters would be more suitable. - Abstract: In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (T_d_e_w). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (T_m_i_n, T_m_a_x and T_a_v_g), relative humidity (R_h), atmospheric pressure (P), water vapor pressure (V_P), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on T_d_e_w. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, V_P is the most influential variable while R_h is the least relevant element. Furthermore, the combination of T_m_i_n and V_P is recognized as the most influential set to predict T_d_e_w. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable

  7. Implicit and explicit attitudes predict smoking cessation: moderating effects of experienced failure to control smoking and plans to quit.

    Science.gov (United States)

    Chassin, Laurie; Presson, Clark C; Sherman, Steven J; Seo, Dong-Chul; Macy, Jonathan T

    2010-12-01

    The current study tested implicit and explicit attitudes as prospective predictors of smoking cessation in a Midwestern community sample of smokers. Results showed that the effects of attitudes significantly varied with levels of experienced failure to control smoking and plans to quit. Explicit attitudes significantly predicted later cessation among those with low (but not high or average) levels of experienced failure to control smoking. Conversely, however, implicit attitudes significantly predicted later cessation among those with high levels of experienced failure to control smoking, but only if they had a plan to quit. Because smoking cessation involves both controlled and automatic processes, interventions may need to consider attitude change interventions that focus on both implicit and explicit attitudes. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

  8. Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

    OpenAIRE

    Muluken Alemu Yehuala

    2015-01-01

    Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The ...

  9. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

    Science.gov (United States)

    Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

    2015-04-01

    This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

  10. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    The understanding and mitigation of downhole vibration has been a heavily researched subject in the oil industry as it results in more expensive drilling operations, as vibrations significantly diminish the amount of effective drilling energy available to the bit and generate forces that can push the bit or the Bottom Hole Assembly (BHA) off its concentric axis of rotation, producing high magnitude impacts with the borehole wall. In order to drill ahead, a sufficient amount of energy must be supplied by the rig to overcome the resistance of the drilling system, including the reactive torque of the system, drag forces, fluid pressure losses and energy dissipated by downhole vibrations, then providing the bit with the energy required to fail the rock. If the drill string enters resonant modes of vibration, not only does it decreases the amount of available energy to drill, but increases the potential for catastrophic downhole equipment and drilling bit failures. In this sense, the mitigation of downhole vibrations will result in faster, smoother, and cheaper drilling operations. A software tool using Finite Element Analysis (FEA) has been developed to provide better understanding of downhole vibration phenomena in drilling environments. The software tool calculates the response of the drilling system at various input conditions, based on the design of the wellbore along with the geometry of the Bottom Hole Assembly (BHA) and the drill string. It identifies where undesired levels of resonant vibration will be driven by certain combinations of specific drilling parameters, and also which combinations of drilling parameters will result in lower levels of vibration, so the least shocks, the highest penetration rate and the lowest cost per foot can be achieved. With the growing performance of personal computers, complex software systems modeling the drilling vibrations using FEA has been accessible to a wider audience of field users, further complimenting with real time

  11. Characterization of acute-on-chronic liver failure and prediction of mortality in Asian patients with active alcoholism.

    Science.gov (United States)

    Kim, Hwi Young; Chang, Young; Park, Jae Yong; Ahn, Hongkeun; Cho, Hyeki; Han, Seung Jun; Oh, Sohee; Kim, Donghee; Jung, Yong Jin; Kim, Byeong Gwan; Lee, Kook Lae; Kim, Won

    2016-02-01

    Alcoholic liver diseases often evolve to acute-on-chronic liver failure (ACLF), which increases the risk of (multi-)organ failure and death. We investigated the development and characteristics of alcohol-related ACLF and evaluated prognostic scores for prediction of mortality in Asian patients with active alcoholism. A total of 205 patients who were hospitalized with severe alcoholic liver disease were included in this retrospective cohort study, after excluding those with serious cardiovascular diseases, malignancy, or co-existing viral hepatitis. The Chronic Liver Failure (CLIF) Consortium Organ Failure score was used in the diagnosis and grading of ACLF, and the CLIF Consortium ACLF score (CLIF-C ACLFs) was used to predict mortality. Patients with ACLF had higher Maddrey discriminant function, model for end-stage liver disease (MELD), and MELD-sodium scores than those without ACLF. Infections were more frequently documented in patients with ACLF (33.3% vs 53.0%; P = 0.004). Predictive factors for ACLF development were systemic inflammatory response syndrome (odds ratio [OR], 2.239; P alcohol-related ACLF in Asian patients with active alcoholism. The CLIF-C ACLFs may be more useful for predicting mortality in ACLF cases than liver-specific scoring systems. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

  12. Usefulness of Serum Triiodothyronine (T3) to Predict Outcomes in Patients Hospitalized With Acute Heart Failure.

    Science.gov (United States)

    Rothberger, Gary D; Gadhvi, Sonya; Michelakis, Nickolaos; Kumar, Amit; Calixte, Rose; Shapiro, Lawrence E

    2017-02-15

    Thyroid hormone plays an important role in cardiac function. Low levels of serum triiodothyronine (T 3 ) due to nonthyroidal illness syndrome may have adverse effects in heart failure (HF). This study was designed to assess the ability of T 3 to predict in-hospital outcomes in patients with acute HF. In total, 137 patients without thyroid disease or treatment with drugs which affect TH levels, who were hospitalized with acute HF were prospectively enrolled and studied. TH levels were tested upon hospital admission, and outcomes were compared between patients with low (<2.3 pg/ml) and normal (≥2.3 pg/ml) free T 3 levels as well as between those with low (<0.6 ng/ml) and normal (≥0.6 ng/ml) total T 3 levels. Low free T 3 correlated with an increased length of stay in the hospital (median 11 vs 7 days, p <0.001) and higher rates of intensive care unit admission (31.8% vs 16.9%, p = 0.047), with a trend toward increased need for invasive mechanical ventilation (9.0% vs 1.4%, p = 0.056). Low total T3 correlated with an increased length of stay in the hospital (median 11 vs 7 days, p <0.001) and increased need for invasive mechanical ventilation (9.8% vs 1.3%, p = 0.045). In conclusion, low T 3 predicts worse hospital outcomes in patients with acute HF and can be useful in the risk stratification of these patients. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Aldosterone Does Not Predict Cardiovascular Events Following Acute Coronary Syndrome in Patients Initially Without Heart Failure.

    Science.gov (United States)

    Pitts, Reynaria; Gunzburger, Elise; Ballantyne, Christie M; Barter, Philip J; Kallend, David; Leiter, Lawrence A; Leitersdorf, Eran; Nicholls, Stephen J; Shah, Prediman K; Tardif, Jean-Claude; Olsson, Anders G; McMurray, John J V; Kittelson, John; Schwartz, Gregory G

    2017-01-10

    Aldosterone may have adverse effects in the myocardium and vasculature. Treatment with an aldosterone antagonist reduces cardiovascular risk in patients with acute myocardial infarction complicated by heart failure (HF) and left ventricular systolic dysfunction. However, most patients with acute coronary syndrome do not have advanced HF. Among such patients, it is unknown whether aldosterone predicts cardiovascular risk. To address this question, we examined data from the dal-OUTCOMES trial that compared the cholesteryl ester transfer protein inhibitor dalcetrapib with placebo, beginning 4 to 12 weeks after an index acute coronary syndrome. Patients with New York Heart Association class II (with LVEF coronary heart disease death, nonfatal myocardial infarction, stroke, hospitalization for unstable angina, or resuscitated cardiac arrest. Hospitalization for HF was a secondary endpoint. Over a median follow-up of 37 months, the primary outcome occurred in 366 patients (9.0%), and hospitalization for HF occurred in 72 patients (1.8%). There was no association between aldosterone and either the time to first occurrence of a primary outcome (hazard ratio for doubling of aldosterone 0.92, 95% confidence interval 0.78-1.09, P=0.34) or hospitalization for HF (hazard ratio 1.38, 95% CI 0.96-1.99, P=0.08) in Cox regression models adjusted for covariates. In patients with recent acute coronary syndrome but without advanced HF, aldosterone does not predict major cardiovascular events. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00658515. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  14. Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients.

    Science.gov (United States)

    Pacho, Cristina; Domingo, Mar; Núñez, Raquel; Lupón, Josep; Núñez, Julio; Barallat, Jaume; Moliner, Pedro; de Antonio, Marta; Santesmases, Javier; Cediel, Germán; Roura, Santiago; Pastor, M Cruz; Tor, Jordi; Bayes-Genis, Antoni

    2018-05-09

    Heart failure (HF) is associated with a high rate of readmissions within 30 days post-discharge and in the following year, especially in frail elderly patients. Biomarker data are scarce in this high-risk population. This study assessed the value of early post-discharge circulating levels of ST2, NT-proBNP, CA125, and hs-TnI for predicting 30-day and 1-year outcomes in comorbid frail elderly patients with HF with mainly preserved ejection fraction (HFpEF). Blood samples were obtained at the first visit shortly after discharge (4.9 ± 2 days). The primary endpoint was the composite of all-cause mortality or HF-related rehospitalization at 30 days and at 1 year. All-cause mortality alone at one year was also a major endpoint. HF-related rehospitalizations alone were secondary end-points. From February 2014 to November 2016, 522 consecutive patients attending the STOP-HF Clinic were included (57.1% women, age 82 ± 8.7 years, mean Barthel index 70 ± 25, mean Charlson comorbidity index 5.6 ± 2.2). The composite endpoint occurred in 8.6% patients at 30 days and in 38.5% at 1 year. In multivariable analysis, ST2 [hazard ratio (HR) 1.53; 95% CI 1.19-1.97; p = 0.001] was the only predictive biomarker at 30 days; at 1 year, both ST2 (HR 1.34; 95% CI 1.15-1.56; p < 0.001) and NT-proBNP (HR 1.19; 95% CI 1.02-1.40; p = 0.03) remained significant. The addition of ST2 and NT-proBNP into a clinical predictive model increased the AUC from 0.70 to 0.75 at 30 days (p = 0.02) and from 0.71 to 0.74 at 1 year (p < 0.05). For all-cause death at 1 year, ST2 (HR 1.50; 95% CI 1.26-1.80; p < 0.001), and CA125 (HR 1.41; 95% CI 1.21-1.63; p < 0.001) remained independent predictors in multivariable analysis. The addition of ST2 and CA125 into a clinical predictive model increased the AUC from 0.74 to 0.78 (p = 0.03). For HF-related hospitalizations, ST2 was the only predictive biomarker in multivariable analyses, both at 30

  15. Failure location prediction by finite element analysis for an additive manufactured mandible implant.

    Science.gov (United States)

    Huo, Jinxing; Dérand, Per; Rännar, Lars-Erik; Hirsch, Jan-Michaél; Gamstedt, E Kristofer

    2015-09-01

    In order to reconstruct a patient with a bone defect in the mandible, a porous scaffold attached to a plate, both in a titanium alloy, was designed and manufactured using additive manufacturing. Regrettably, the implant fractured in vivo several months after surgery. The aim of this study was to investigate the failure of the implant and show a way of predicting the mechanical properties of the implant before surgery. All computed tomography data of the patient were preprocessed to remove metallic artefacts with metal deletion technique before mandible geometry reconstruction. The three-dimensional geometry of the patient's mandible was also reconstructed, and the implant was fixed to the bone model with screws in Mimics medical imaging software. A finite element model was established from the assembly of the mandible and the implant to study stresses developed during mastication. The stress distribution in the load-bearing plate was computed, and the location of main stress concentration in the plate was determined. Comparison between the fracture region and the location of the stress concentration shows that finite element analysis could serve as a tool for optimizing the design of mandible implants. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  16. Ambient seismic noise monitoring of a clay landslide: Toward failure prediction

    Science.gov (United States)

    Mainsant, Guénolé; Larose, Eric; Brönnimann, Cornelia; Jongmans, Denis; Michoud, Clément; Jaboyedoff, Michel

    2012-03-01

    Given that clay-rich landslides may become mobilized, leading to rapid mass movements (earthflows and debris flows), they pose critical problems in risk management worldwide. The most widely proposed mechanism leading to such flow-like movements is the increase in water pore pressure in the sliding mass, generating partial or complete liquefaction. This solid-to-liquid transition results in a dramatic reduction of mechanical rigidity in the liquefied zones, which could be detected by monitoring shear wave velocity variations. With this purpose in mind, the ambient seismic noise correlation technique has been applied to measure the variation in the seismic surface wave velocity in the Pont Bourquin landslide (Swiss Alps). This small but active composite earthslide-earthflow was equipped with continuously recording seismic sensors during spring and summer 2010. An earthslide of a few thousand cubic meters was triggered in mid-August 2010, after a rainy period. This article shows that the seismic velocity of the sliding material, measured from daily noise correlograms, decreased continuously and rapidly for several days prior to the catastrophic event. From a spectral analysis of the velocity decrease, it was possible to determine the location of the change at the base of the sliding layer. These results demonstrate that ambient seismic noise can be used to detect rigidity variations before failure and could potentially be used to predict landslides.

  17. Prediction of the Chemoreflex Gain by Common Clinical Variables in Heart Failure.

    Directory of Open Access Journals (Sweden)

    Gianluca Mirizzi

    Full Text Available Peripheral and central chemoreflex sensitivity, assessed by the hypoxic or hypercapnic ventilatory response (HVR and HCVR, respectively, is enhanced in heart failure (HF patients, is involved in the pathophysiology of the disease, and is under investigation as a potential therapeutic target. Chemoreflex sensitivity assessment is however demanding and, therefore, not easily applicable in the clinical setting. We aimed at evaluating whether common clinical variables, broadly obtained by routine clinical and instrumental evaluation, could predict increased HVR and HCVR.191 patients with systolic HF (left ventricular ejection fraction--LVEF--<50% underwent chemoreflex assessment by rebreathing technique to assess HVR and HCVR. All patients underwent clinical and neurohormonal evaluation, comprising: echocardiogram, cardiopulmonary exercise test (CPET, daytime cardiorespiratory monitoring for breathing pattern evaluation. Regarding HVR, multivariate penalized logistic regression, Bayesian Model Averaging (BMA logistic regression and random forest analysis identified, as predictors, the presence of periodic breathing and increased slope of the relation between ventilation and carbon dioxide production (VE/VCO2 during exercise. Again, the above-mentioned statistical tools identified as HCVR predictors plasma levels of N-terminal fragment of proBNP and VE/VCO2 slope.In HF patients, the simple assessment of breathing pattern, alongside with ventilatory efficiency during exercise and natriuretic peptides levels identifies a subset of patients presenting with increased chemoreflex sensitivity to either hypoxia or hypercapnia.

  18. Failure and reliability prediction by support vector machines regression of time series data

    International Nuclear Information System (INIS)

    Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

    2011-01-01

    Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

  19. Decreases in daily physical activity predict acute decline in attention and executive function in heart failure.

    Science.gov (United States)

    Alosco, Michael L; Spitznagel, Mary Beth; Cohen, Ronald; Sweet, Lawrence H; Hayes, Scott M; Josephson, Richard; Hughes, Joel; Gunstad, John

    2015-04-01

    Reduced physical activity (PA) may be one factor that contributes to cognitive decline and dementia in heart failure (HF). Yet, the longitudinal relationship between PA and cognition in HF is poorly understood owing to limitations of past work, including single-time assessments of PA. This is the first study to examine changes in objectively measured PA and cognition over time in HF. At baseline and 12 weeks, 57 HF patients completed psychosocial self-report measures and a neuropsychological battery and wore an accelerometer for 7 days. At baseline, HF patients spent an average of 597.83 (SD 75.91) minutes per day sedentary. Steps per day declined from baseline to the 12-week follow-up; there was also a trend for declines in moderate-vigorous PA. Regression analyses controlling for sex, HF severity, and depressive symptoms showed that decreases in light (P = .08) and moderate-vigorous (P = .04) daily PA emerged as strong predictors of declines in attention/executive function over the 12-week period, but not of memory or language. Reductions in daily PA predicted acute decline in attention/executive function in HF, but not of memory or language. Modifications to daily PA may attenuate cognitive decline, and prospective studies are needed to test this possibility. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Identification of optimal soil hydraulic functions and parameters for predicting soil moisture

    Science.gov (United States)

    We examined the accuracy of several commonly used soil hydraulic functions and associated parameters for predicting observed soil moisture data. We used six combined methods formed by three commonly used soil hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...

  1. An improved robust model predictive control for linear parameter-varying input-output models

    NARCIS (Netherlands)

    Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

    2018-01-01

    This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

  2. A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

    2015-11-01

    This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

  3. Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver.

    Science.gov (United States)

    Miyamoto, Maki; Iwasaki, Shinji; Chisaki, Ikumi; Nakagawa, Sayaka; Amano, Nobuyuki; Hirabayashi, Hideki

    2017-12-01

    1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

  4. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    International Nuclear Information System (INIS)

    Fendler, Wolfgang Peter; Ilhan, Harun; Paprottka, Philipp M.; Jakobs, Tobias F.; Heinemann, Volker; Bartenstein, Peter; Haug, Alexander R.; Khalaf, Feras; Ezziddin, Samer; Hacker, Marcus

    2015-01-01

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  5. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Fendler, Wolfgang Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Klinik und Poliklinik fuer Nuklearmedizin, Munich (Germany); Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Paprottka, Philipp M. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Jakobs, Tobias F. [Hospital Barmherzige Brueder, Department of Diagnostic and Interventional Radiology, Munich (Germany); Heinemann, Volker [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter; Haug, Alexander R. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Khalaf, Feras [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Ezziddin, Samer [Saarland University Medical Center, Department of Nuclear Medicine, Homburg (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

    2015-09-15

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  6. Pharmacokinetic parameters derived from dynamic contrast enhanced MRI of cervical cancers predict chemoradiotherapy outcome

    International Nuclear Information System (INIS)

    Andersen, Erlend K.F.; Hole, Knut Håkon; Lund, Kjersti V.; Sundfør, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

    2013-01-01

    Purpose: To assess the prognostic value of pharmacokinetic parameters derived from pre-chemoradiotherapy dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of cervical cancer patients. Materials and methods: Seventy-eight patients with locally advanced cervical cancer underwent DCE-MRI with Gd-DTPA before chemoradiotherapy. The pharmacokinetic Brix and Tofts models were fitted to contrast enhancement curves in all tumor voxels, providing histograms of several pharmacokinetic parameters (Brix: A Brix , k ep , k el , Tofts: K trans , ν e ). A percentile screening approach including log-rank survival tests was undertaken to identify the clinically most relevant part of the intratumoral parameter distribution. Clinical endpoints were progression-free survival (PFS) and locoregional control (LRC). Multivariate analysis including FIGO stage and tumor volume was used to assess the prognostic significance of the imaging parameters. Results: A Brix , k el , and K trans were significantly (P e was significantly positively correlated with PFS only. k ep showed no association with any endpoint. A Brix was positively correlated with K trans and ν e , and showed the strongest association with endpoint in the log-rank testing. k el and K trans were independent prognostic factors in multivariate analysis with LRC as endpoint. Conclusions: Parameters estimated by pharmacokinetic analysis of DCE-MR images obtained prior to chemoradiotherapy may be used for identifying patients at risk of treatment failure

  7. Lactate Parameters Predict Clinical Outcomes in Patients with Nonvariceal Upper Gastrointestinal Bleeding.

    Science.gov (United States)

    Lee, Seung Hoon; Min, Yang Won; Bae, Joohwan; Lee, Hyuk; Min, Byung Hoon; Lee, Jun Haeng; Rhee, Poong Lyul; Kim, Jae J

    2017-11-01

    The predictive role of lactate in patients with nonvariceal upper gastrointestinal bleeding (NVUGIB) has been suggested. This study evaluated several lactate parameters in terms of predicting outcomes of bleeding patients and sought to establish a new scoring model by combining lactate parameters and the AIMS65 score. A total of 114 patients with NVUGIB who underwent serum lactate level testing at least twice and endoscopic hemostasis within 24 hours after admission were retrospectively analyzed. The associations between five lactate parameters and clinical outcomes were evaluated and the predictive power of lactate parameter combined AIMS65s (L-AIMS65s) and AIMS56 scoring was compared. The most common cause of bleeding was gastric ulcer (48.2%). Lactate clearance rate (LCR) was associated with 30-day rebleeding (odds ratio [OR], 0.931; 95% confidence interval [CI], 0.872-0.994; P = 0.033). Initial lactate (OR, 1.313; 95% CI, 1.050-1.643; P = 0.017), maximal lactate (OR, 1.277; 95% CI, 1.037-1.573; P = 0.021), and average lactate (OR, 1.535; 95% CI, 1.137-2.072; P = 0.005) levels were associated with 30-day mortality. Initial lactate (OR, 1.213; 95% CI, 1.027-1.432; P = 0.023), maximal lactate (OR, 1.271; 95% CI, 1.074-1.504; P = 0.005), and average lactate (OR, 1.501; 95% CI, 1.150-1.959; P = 0.003) levels were associated with admission over 7 days. Although L-AIMS65s showed the highest area under the curve for prediction of each outcome, differences between L-AIMS65s and AIMS65 did not reach statistical significance. In conclusion, lactate parameters have a prognostic role in patients with NVUGIB. However, they do not increase the predictive power of AIMS65 when combined. © 2017 The Korean Academy of Medical Sciences.

  8. Length of Barrett's segment predicts failure of eradication in radiofrequency ablation for Barrett's esophagus: a retrospective cohort study.

    Science.gov (United States)

    Luckett, Tyler; Allamneni, Chaitanya; Cowley, Kevin; Eick, John; Gullick, Allison; Peter, Shajan

    2018-05-21

    We aim to investigate factors that may contribute to failure of eradication of dysplastic Barrett's Esophagus among patients undergoing radiofrequency ablation treatment. A retrospective review of patients undergoing radiofrequency ablation for treatment of Barrett's Esophagus was performed. Data analyzed included patient demographics, medical history, length of Barrett's Esophagus, number of radiofrequency ablation sessions, and histopathology. Subsets of patients achieving complete eradication were compared with those not achieving complete eradication. A total of 107 patients underwent radiofrequency ablation for Barrett's Esophagus, the majority white, overweight, and male. Before treatment, 63 patients had low-grade dysplasia, and 44 patients had high-grade dysplasia or carcinoma. Complete eradication was achieved in a majority of patients (57% for metaplasia, and 76.6% for dysplasia). Failure of eradication occurred in 15.7% of patients. The median number of radiofrequency ablation treatments in patients achieving complete eradication was 3 sessions, compared to 4 sessions for failure of eradication (p = 0.06). Barrett's esophagus length of more than 5 cm was predictive of failure of eradication (p Radiofrequency ablation for dysplastic Barrett's Esophagus is a proven and effective treatment modality, associated with a high rate of complete eradication. Our rates of eradication from a center starting an ablation program are comparable to previously published studies. Length of Barrett's segment > 5 cm was found to be predictive of failure of eradication in patients undergoing radiofrequency ablation.

  9. Simple motor tasks independently predict extubation failure in critically ill neurological patients.

    Science.gov (United States)

    Kutchak, Fernanda Machado; Rieder, Marcelo de Mello; Victorino, Josué Almeida; Meneguzzi, Carla; Poersch, Karla; Forgiarini, Luiz Alberto; Bianchin, Marino Muxfeldt

    2017-01-01

    To evaluate the usefulness of simple motor tasks such as hand grasping and tongue protrusion as predictors of extubation failure in critically ill neurological patients. This was a prospective cohort study conducted in the neurological ICU of a tertiary care hospital in the city of Porto Alegre, Brazil. Adult patients who had been intubated for neurological reasons and were eligible for weaning were included in the study. The ability of patients to perform simple motor tasks such as hand grasping and tongue protrusion was evaluated as a predictor of extubation failure. Data regarding duration of mechanical ventilation, length of ICU stay, length of hospital stay, mortality, and incidence of ventilator-associated pneumonia were collected. A total of 132 intubated patients who had been receiving mechanical ventilation for at least 24 h and who passed a spontaneous breathing trial were included in the analysis. Logistic regression showed that patient inability to grasp the hand of the examiner (relative risk = 1.57; 95% CI: 1.01-2.44; p commands is predictive of extubation failure in critically ill neurological patients. Hand grasping and tongue protrusion on command might be quick and easy bedside tests to identify neurocritical care patients who are candidates for extubation. Avaliar a utilidade de tarefas motoras simples, tais como preensão de mão e protrusão da língua, para predizer extubação malsucedida em pacientes neurológicos críticos. Estudo prospectivo de coorte realizado na UTI neurológica de um hospital terciário em Porto Alegre (RS). Pacientes adultos que haviam sido intubados por motivos neurológicos e que eram candidatos ao desmame foram incluídos no estudo. O estudo avaliou se a capacidade dos pacientes de realizar tarefas motoras simples como apertar as mãos do examinador e pôr a língua para fora seria um preditor de extubação malsucedida. Foram coletados dados referentes ao tempo de ventilação mecânica, tempo de internação na

  10. Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects

    Directory of Open Access Journals (Sweden)

    Lina Zhao

    2015-09-01

    Full Text Available Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N. However, no specific guideline exists on how to determine the appropriate parameter values for distinguishing congestive heart failure (CHF from normal sinus rhythm (NSR subjects in clinical application. In the present study, a thorough analysis on the selection of appropriate values of m, r and N for sample entropy (SampEn and recently proposed fuzzy measure entropy (FuzzyMEn is presented for distinguishing two group subjects. 44 long-term NRS and 29 long-term CHF RR interval recordings from http://www.physionet.org were used as the non-pathological and pathological data respectively. Extreme (>2 s and abnormal heartbeat RR intervals were firstly removed from each RR recording and then the recording was segmented with a non-overlapping segment length N of 300 and 1000, respectively. SampEn and FuzzyMEn were performed for each RR segment under different parameter combinations: m of 1, 2, 3 and 4, and r of 0.10, 0.15, 0.20 and 0.25 respectively. The statistical significance between NSR and CHF groups under each combination of m, r and N was observed. The results demonstrated that the selection of m, r and N plays a critical role in determining the SampEn and FuzzyMEn outputs. Compared with SampEn, FuzzyMEn shows a better regularity when selecting the parameters m and r. In addition, FuzzyMEn shows a better relative consistency for distinguishing the two groups, that is, the results of FuzzyMEn in the NSR group were consistently lower than those in the CHF group while SampEn were not. The selections of m of 2 and 3 and r of 0.10 and 0.15 for SampEn and the selections of m of 1 and 2 whenever r (herein

  11. Prediction of the effect of atrasentan on renal and heart failure outcomes based on short-term changes in multiple risk markers

    DEFF Research Database (Denmark)

    Schievink, Bauke; de Zeeuw, Dick; Smink, Paul A

    2016-01-01

    from the RADAR/JAPAN study to predict the effect of atrasentan on renal and heart failure outcomes. METHODS: We performed a post-hoc analysis of the RADAR/JAPAN randomized clinical trials in which 211 patients with type-2 diabetes and nephropathy were randomly assigned to atrasentan 0.75 mg/day, 1......BACKGROUND: A recent phase II clinical trial (Reducing Residual Albuminuria in Subjects with Diabetes and Nephropathy with AtRasentan trial and an identical trial in Japan (RADAR/JAPAN)) showed that the endothelin A receptor antagonist atrasentan lowers albuminuria, blood pressure, cholesterol......, hemoglobin, and increases body weight in patients with type 2 diabetes and nephropathy. We previously developed an algorithm, the Parameter Response Efficacy (PRE) score, which translates short-term drug effects into predictions of long-term effects on clinical outcomes. DESIGN: We used the PRE score on data...

  12. Sensitivity, Specificity, Predictive Values, and Accuracy of Three Diagnostic Tests to Predict Inferior Alveolar Nerve Blockade Failure in Symptomatic Irreversible Pulpitis

    Directory of Open Access Journals (Sweden)

    Daniel Chavarría-Bolaños

    2017-01-01

    Full Text Available Introduction. The inferior alveolar nerve block (IANB is the most common anesthetic technique used on mandibular teeth during root canal treatment. Its success in the presence of preoperative inflammation is still controversial. The aim of this study was to evaluate the sensitivity, specificity, predictive values, and accuracy of three diagnostic tests used to predict IANB failure in symptomatic irreversible pulpitis (SIP. Methodology. A cross-sectional study was carried out on the mandibular molars of 53 patients with SIP. All patients received a single cartridge of mepivacaine 2% with 1 : 100000 epinephrine using the IANB technique. Three diagnostic clinical tests were performed to detect anesthetic failure. Anesthetic failure was defined as a positive painful response to any of the three tests. Sensitivity, specificity, predictive values, accuracy, and ROC curves were calculated and compared and significant differences were analyzed. Results. IANB failure was determined in 71.7% of the patients. The sensitivity scores for the three tests (lip numbness, the cold stimuli test, and responsiveness during endodontic access were 0.03, 0.35, and 0.55, respectively, and the specificity score was determined as 1 for all of the tests. Clinically, none of the evaluated tests demonstrated a high enough accuracy (0.30, 0.53, and 0.68 for lip numbness, the cold stimuli test, and responsiveness during endodontic access, resp.. A comparison of the areas under the curve in the ROC analyses showed statistically significant differences between the three tests (p<0.05. Conclusion. None of the analyzed tests demonstrated a high enough accuracy to be considered a reliable diagnostic tool for the prediction of anesthetic failure.

  13. Utility of the Instability Severity Index Score in Predicting Failure After Arthroscopic Anterior Stabilization of the Shoulder.

    Science.gov (United States)

    Phadnis, Joideep; Arnold, Christine; Elmorsy, Ahmed; Flannery, Mark

    2015-08-01

    The redislocation rate after arthroscopic stabilization for anterior glenohumeral instability is up to 30%. The Instability Severity Index Score (ISIS) was developed to preoperatively rationalize the risk of failure, but it has not yet been validated by an independent group. To assess the utility of the ISIS in predicting failure of arthroscopic anterior shoulder stabilization and to identify other preoperative factors for failure. Case-control study; Level of evidence, 3. A case-control study was performed on 141 consecutive patients, comparing those who suffered failure of arthroscopic stabilization with those who had successful arthroscopic stabilization. The mean follow-up time was 47 months (range, 24-132 months). The ISIS was applied retrospectively, and an analysis was performed to establish independent risk factors for failure. A receiver operator coefficient curve was constructed to set a threshold ISIS for considering alternative surgery. Of 141 patients, 19 (13.5%) suffered recurrent instability. The mean ISIS of the failed stabilization group was higher than that of the successful stabilization group (5.1 vs 1.7; P surgery (P < .001), age at first dislocation (P = .01), competitive-level participation in sports (P < .001), and participation in contact or overhead sports (P = .03). The presence of glenoid bone loss carried the highest risk of failure (70%). There was a 70% risk of failure if the ISIS was ≥4, as opposed to a 4% risk of failure if the ISIS was <4. This is the first completely independent study to confirm that the ISIS is a useful preoperative tool. It is recommended that surgeons consider alternative forms of stabilization if the ISIS is ≥4. © 2015 The Author(s).

  14. Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

    Science.gov (United States)

    Orini, Michele; Mincholé, Ana; Monasterio, Violeta; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Martínez, Juan Pablo

    2017-01-01

    Background Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. Methods The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. Results The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. Conclusion The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients. PMID:29020031

  15. Prediction of yield and long-term failure of oriented polypropylene: kinetics and anisotropy

    NARCIS (Netherlands)

    van Erp, T.B.; Reynolds, C.T.; Peijs, T.; van Dommelen, J.A.W.; Govaert, L.E.

    2009-01-01

    The time-dependent yield and failure behavior of off-axis loaded uniaxially oriented polypropy-lene tape is investigated. The yield and failure behavior is described with an anisotropic vis-coplastic model. A viscoplastic flow rule is used with an equivalent stress, based on Hill’sanisotropic yield

  16. Plasma copeptin levels and prediction of outcome in heart failure outpatients

    DEFF Research Database (Denmark)

    Balling, Louise; Kistorp, Caroline; Schou, Morten

    2012-01-01

    Copeptin, a stable fragment of the vasopressin prohormone, has been shown to be a significant biomarker for morbidity and mortality in heart failure. The aims of this study were to evaluate the influence of plasma sodium on the prognostic significance of copeptin concentrations in heart failure o...

  17. The predicted influence of climate change on lesser prairie-chicken reproductive parameters

    Science.gov (United States)

    Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

    2013-01-01

    The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

  18. The prediction of failure of welded steel plates for pressure vessels

    International Nuclear Information System (INIS)

    Gulvin, T.F.; Terry, P.; Webster, S.E.

    1980-01-01

    The avoidance of brittle fracture in pressure vessels and other welded steel fabrications is a matter of considerable importance. The application of fracture mechanics to this problem has led to the evolution of a philosophy which, among other things, permits estimation of tolerable crack sizes so that practical working limits can be set on inspection procedures and on material and welded joint toughness levels. The use of small-scale fracture mechanics tests, particularly crack opening displacement (COD) tests, to make the necessary material and/or joint property assessments is now commonplace. A number of possible analytical techniques have been considered. Initially, the COD data have been used with the Burdekin-Dawes design curve approach taking into account all the possible stress conditions and, it can be demonstrated, that consistently safe predictions of allowable flaw sizes can be made. Also considered, is the fracture analysis diagram approach of Harrison, Loosemore, and Milne in which a combination of fracture toughness data and mechanical property data is used to assess the probability of failure. Similarly the approach defined by Irvine and Quirk which makes use of mechanical property data without resorting to fracture mechanics and finally, a simple approach using uniaxial tensile property data only has also been examined. Comparisons have been made between these four approaches using, initially, the body of data referring to fracture tests on a single material with various defect sizes and aspect ratios, etc. and latterly, to the specific cases in the body of data on high strength steels. The results are discussed. (author)

  19. Improving weather predictability by including land-surface model parameter uncertainty

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

    2016-04-01

    The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

  20. A statistical model for prediction of fuel element failure using the Markov process and entropy minimax principles

    International Nuclear Information System (INIS)

    Choi, K.Y.; Yoon, Y.K.; Chang, S.H.

    1991-01-01

    This paper reports on a new statistical fuel failure model developed to take into account the effects of damaging environmental conditions and the overall operating history of the fuel elements. The degradation of material properties and damage resistance of the fuel cladding is mainly caused by the combined effects of accumulated dynamic stresses, neutron irradiation, and chemical and stress corrosion at operating temperature. Since the degradation of material properties due to these effects can be considered as a stochastic process, a dynamic reliability function is derived based on the Markov process. Four damage parameters, namely, dynamic stresses, magnitude of power increase from the preceding power level and with ramp rate, and fatigue cycles, are used to build this model. The dynamic reliability function and damage parameters are used to obtain effective damage parameters. The entropy maximization principle is used to generate a probability density function of the effective damage parameters. The entropy minimization principle is applied to determine weighting factors for amalgamation of the failure probabilities due to the respective failure modes. In this way, the effects of operating history, damaging environmental conditions, and damage sequence are taken into account

  1. Adaptability and Prediction of Anticipatory Muscular Activity Parameters to Different Movements in the Sitting Position.

    Science.gov (United States)

    Chikh, Soufien; Watelain, Eric; Faupin, Arnaud; Pinti, Antonio; Jarraya, Mohamed; Garnier, Cyril

    2016-08-01

    Voluntary movement often causes postural perturbation that requires an anticipatory postural adjustment to minimize perturbation and increase the efficiency and coordination during execution. This systematic review focuses specifically on the relationship between the parameters of anticipatory muscular activities and movement finality in sitting position among adults, to study the adaptability and predictability of anticipatory muscular activities parameters to different movements and conditions in sitting position in adults. A systematic literature search was performed using PubMed, Science Direct, Web of Science, Springer-Link, Engineering Village, and EbscoHost. Inclusion and exclusion criteria were applied to retain the most rigorous and specific studies, yielding 76 articles, Seventeen articles were excluded at first reading, and after the application of inclusion and exclusion criteria, 23 were retained. In a sitting position, central nervous system activity precedes movement by diverse anticipatory muscular activities and shows the ability to adapt anticipatory muscular activity parameters to the movement direction, postural stability, or charge weight. In addition, these parameters could be adapted to the speed of execution, as found for the standing position. Parameters of anticipatory muscular activities (duration, order, and amplitude of muscle contractions constituting the anticipatory muscular activity) could be used as a predictive indicator of forthcoming movement. In addition, this systematic review may improve methodology in empirical studies and assistive technology for people with disabilities. © The Author(s) 2016.

  2. Time shift in slope failure prediction between unimodal and bimodal modeling approaches

    Science.gov (United States)

    Ciervo, Fabio; Casini, Francesca; Nicolina Papa, Maria; Medina, Vicente

    2016-04-01

    within the Bishop stress theory framework (Ciervo et al., 2015). The proposed work tends to emphasize how a more accurate slope stability analysis that accounts dual-structure could be useful to reach a more accurate definition of the stability conditions. The effects in practical analysis may be significant. The highlighted discrepancies between the different approaches in describing the timing processes and strength contribution due to capillary forces may entail no negligible differences in slope stability predictions, especially in those cases where the possibility of a failure in unsaturated terrains is contemplated.

  3. Predictive value of ADAMTS-13 on concealed chronic renal failure in COPD patients

    Science.gov (United States)

    Zeng, Mian; Chen, Qingui; Liang, Wenjie; He, Wanmei; Zheng, Haichong; Huang, Chunrong

    2017-01-01

    Background Impaired renal function is often neglected in COPD patients. Considering that COPD patients usually have an ongoing prothrombotic state and systemic inflammation status, we investigated the association among them and explored the predictive value of a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS-13), on concealed chronic renal failure (CRF) in COPD patients. Methods COPD patients were recruited from the First Affiliated Hospital of Sun Yat-Sen University between January 2015 and December 2016. Control was selected from contemporaneous hospitalized patients without COPD and matched by age and gender at a ratio of 1:1. Estimated glomerular filtration rate (eGFR) was calculated by using the Chronic Kidney Disease Epidemiology Collaboration formula, and all subjects were categorized as having normal renal function (eGFR ≥60 mL min−1 1.73 m−2) and having concealed CRF (normal serum creatinine while eGFR <60 mL min−1 1.73 m−2). Independent correlates of concealed CRF were investigated by logistic regression analysis, and receiver operating characteristic (ROC) curves were used to determine the predictive value of ADAMTS-13. Results In total, 106 COPD and 106 non-COPD patients were finally recruited, and the incidences of concealed CRF were 19.81% and 7.55%, respectively. ADAMTS-13 (odds ratio [OR] =0.858, 95% CI =0.795–0.926), D-dimer (OR =1.095, 95% CI =1.027–1.169), and C-reactive protein (OR =1.252, 95% CI =1.058–1.480) were significantly associated with concealed CRF. Sensitivity and specificity at an ADAMTS-13 cutoff of 318.72 ng/mL were 100% and 81.2%, respectively. The area under the ROC curve was 0.959. Conclusion Prothrombotic state and systemic inflammation status might contribute to explaining the high incidence of concealed CRF in COPD, and plasma ADAMTS-13 levels may serve as a strong predictor. PMID:29255356

  4. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  5. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  6. Predictive factors for early failure of transarterial embolization in blunt hepatic injury patients

    International Nuclear Information System (INIS)

    Lee, Y.-H.; Wu, C.-H.; Wang, L.-J.; Wong, Y.-C.; Chen, H.-W.; Wang, C.-J.; Lin, B.-C.; Hsu, Y.-P.

    2014-01-01

    Aim: To evaluate the early success of transarterial embolization (TAE) in patients with traumatic liver haemorrhage and to determine independent factors for its failure. Materials and methods: From January 2009 to December 2012, TAE was performed in 48 patients for traumatic liver haemorrhage. Their medical charts were reviewed for demographic information, pre-TAE vital signs and laboratory data, injury grade, type of contrast medium extravasation (CME) at CT, angiography findings, and early failure. “Early failure” was defined as the need for repeated TAE or a laparotomy for hepatic haemorrhage within 4 days after TAE. Variables were compared between the early success and early failure groups. Variables with univariate significance were also analysed using multivariate logistic regression for predictors of early failure. Results: Among 48 liver TAE cases, nine (18.8%) were early failures due to liver haemorrhage. Early failure was associated with injury grade (p = 0.039), major liver injury (grades 4 and 5; p = 0.007), multiple CMEs at angiography (p = 0.031), incomplete TAE (p = 0.002), and elevated heart rate (p = 0.026). Incomplete embolization (OR = 8; p = 0.042), and heart rate >110 beats/min (bpm; OR = 8; p = 0.05) were independent factors for early failure of TAE in the group with major liver injuries. Conclusion: Major hepatic injury is an important factor in early failure. Patients with a heart rate >110 bpm and incomplete embolization in the major injury group have an increased rate of early failure. The success rate of proximal TAE was comparable to that of the more time-consuming, superselective, distal TAE. - Highlights: • Early failure of TAE is associated with a higher grade of liver injury. • Incomplete embolization is more likely to suffer early failure of TAE. • A heart rate greater than 110 bpm is more likely to suffer early failure of TAE. • We recommend proximal embolization to prevent early failure of TAE

  7. Predicting Collateral Status With Magnetic Resonance Perfusion Parameters: Probabilistic Approach With a Tmax-Derived Prediction Model.

    Science.gov (United States)

    Lee, Mi Ji; Son, Jeong Pyo; Kim, Suk Jae; Ryoo, Sookyung; Woo, Sook-Young; Cha, Jihoon; Kim, Gyeong-Moon; Chung, Chin-Sang; Lee, Kwang Ho; Bang, Oh Young

    2015-10-01

    Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth. © 2015 American Heart Association, Inc.

  8. Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

    Science.gov (United States)

    Hatfield, Glen S.; Hark, Frank; Stott, James

    2016-01-01

    Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account system integration risks such as those attributable to manufacturing and assembly. These sources often dominate component level risk. While consequence of failure is often understood, using predicted values in a risk model to estimate the probability of occurrence may underestimate the actual risk. Managers and decision makers use the probability of occurrence to influence the determination whether to accept the risk or require a design modification. The actual risk threshold for acceptance may not be fully understood due to the absence of system level test data or operational data. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

  9. Microstructure-based constitutive modeling of TRIP steel: Prediction of ductility and failure modes under different loading conditions

    International Nuclear Information System (INIS)

    Choi, K.S.; Liu, W.N.; Sun, X.; Khaleel, M.A.

    2009-01-01

    We study the ultimate ductility and failure modes of a commercial transformation-induced plasticity (TRIP) 800 steel under different loading conditions with an advanced microstructure-based finite-element analysis. The representative volume element (RVE) for the TRIP 800 under examination is developed based on an actual microstructure obtained from scanning electron microscopy. The ductile failure of the TRIP 800 under different loading conditions is predicted in the form of plastic strain localization without any prescribed failure criteria for the individual phases. This indicates that the microstructure-level inhomogeneity of the various constituent phases can be the key factor influencing the final ductility of the TRIP 800 steel under different loading conditions. Comparisons of the computational results with experimental measurements suggest that the microstructure-based modeling approach accurately captures the overall macroscopic behavior of the TRIP 800 steel under different loading and boundary conditions.

  10. The role of microstructure and phase distribution in the failure mechanisms and life prediction model for PSZ coatings

    Science.gov (United States)

    Sisson, R. D., Jr.; Sone, Ichiro; Biederman, R. R.

    1985-01-01

    Partially Stabilized Zirconia (PSZ) may become widely used for Thermal Barrier Coatings (TBC). Failure of these coatings can occur due to thermal fatigue in oxidizing atmospheres. The failure is due to the strains that develop due to thermal gradients, differences in thermal expansion coefficients, and oxidation of the bond coating. The role of microstructure and the cubic, tetragonal, and monoclinic phase distribution in the strain development and subsequent failure will be discussed. An X-ray diffraction technique for accurate determination of the fraction of each phase in PSZ will be applied to understanding the phase transformations and strain development. These results will be discussed in terms of developing a model for life prediction in PSZ coatings during thermal cycling.

  11. A risk score for predicting 30-day mortality in heart failure patients undergoing non-cardiac surgery

    DEFF Research Database (Denmark)

    Andersson, Charlotte; Gislason, Gunnar H; Hlatky, Mark A

    2014-01-01

    BACKGROUND: Heart failure is an established risk factor for poor outcomes in patients undergoing non-cardiac surgery, yet risk stratification remains a clinical challenge. We developed an index for 30-day mortality risk prediction in this particular group. METHODS AND RESULTS: All individuals...... with heart failure undergoing non-cardiac surgery between October 23 2004 and October 31 2011 were included from Danish administrative registers (n = 16 827). In total, 1787 (10.6%) died within 30 days. In a simple risk score based on the variables from the revised cardiac risk index, plus age, gender, acute...... by bootstrapping (1000 re-samples) provided c-statistic of 0.79. A more complex risk score based on stepwise logistic regression including 24 variables at P heart failure, this simple...

  12. Chaos emerging in soil failure patterns observed during tillage: Normalized deterministic nonlinear prediction (NDNP) and its application.

    Science.gov (United States)

    Sakai, Kenshi; Upadhyaya, Shrinivasa K; Andrade-Sanchez, Pedro; Sviridova, Nina V

    2017-03-01

    Real-world processes are often combinations of deterministic and stochastic processes. Soil failure observed during farm tillage is one example of this phenomenon. In this paper, we investigated the nonlinear features of soil failure patterns in a farm tillage process. We demonstrate emerging determinism in soil failure patterns from stochastic processes under specific soil conditions. We normalized the deterministic nonlinear prediction considering autocorrelation and propose it as a robust way of extracting a nonlinear dynamical system from noise contaminated motion. Soil is a typical granular material. The results obtained here are expected to be applicable to granular materials in general. From a global scale to nano scale, the granular material is featured in seismology, geotechnology, soil mechanics, and particle technology. The results and discussions presented here are applicable in these wide research areas. The proposed method and our findings are useful with respect to the application of nonlinear dynamics to investigate complex motions generated from granular materials.

  13. Prediction of thermoplastic failure of a reactor pressure vessel under a postulated core melt accident

    International Nuclear Information System (INIS)

    Duijvestijn, G.; Birchley, J.; Reichlin, K.

    1997-01-01

    This paper presents the lower head failure calculations performed for a postulated accident scenario in a commercial nuclear power plant. A postulated one inch break in the primary coolant circuit leads to dryout and subsequent meltdown of the core. The reference plant is a pressurized water reactor without penetrations in the reactor vessel lower head. The molten core material accumulates in the lower head, eventually causing failure of the vessel. The analysis investigates flow conditions in the melt pool, temperature evolution in the reactor vessel wall, and structure mechanical evaluation of the vessel under strong thermal loads and a range of internal pressures. The calculations were performed using the ADINA finite element codes. The analysis focusses on the failure processes, time and mode of failure. The most likely mode of failure at low pressure is global rupture due to gradual accumulation of creep strain over a large part of the heated area. In contrast, thermoplasticity becomes important at high pressure or following a pressure spike and can lead to earlier local failure. In situations in which part of the heat load is concentrated over a small area, resulting in a hot spot, local failure occurs, but not until the temperatures are close to the melting point. At low pressure, in particular, the hot spot area remains intact until the structure is molten across more than half of the thickness. (author) 14 figs., 16 refs

  14. Patency of Femoral Tunneled Hemodialysis Catheters and Factors Predictive of Patency Failure

    International Nuclear Information System (INIS)

    Burton, Kirsteen R.; Guo, Lancia L. Q.; Tan, Kong T.; Simons, Martin E.; Sniderman, Kenneth W.; Kachura, John R.; Beecroft, John R.; Rajan, Dheeraj K.

    2012-01-01

    Purpose: To determine the patency rates of and factors associated with increased risk of patency failure in patients with femoral vein tunneled hemodialysis catheters. Methods: All femoral tunneled catheter insertions from 1996 to 2006 were reviewed, during which time 123 catheters were inserted. Of these, 66 were exchanges. Patients with femoral catheter failure versus those with femoral catheter patency were compared. Confounding factors, such as demographic and procedural factors, were incorporated and assessed using univariate and multivariable Cox proportional hazards regression analyses. Results: Mean catheter primary patency failure time was 96.3 days (SE 17.9 days). Primary patency at 30, 60, 90, and 180 days was 53.8%, 45.4%, 32.1%, and 27.1% respectively. Crude rates of risk of catheter failure did not suggest a benefit for patients receiving catheters introduced from one side versus the other, but more cephalad location of catheter tip was associated with improved patency. Multivariate analysis showed that patients whose catheters were on the left side (p = 0.009), were of increasing age at the time of insertion (p = 0.002) and that those who had diabetes (p = 0.001) were at significantly greater risk of catheter failure. The catheter infection rate was 1.4/1000 catheter days. Conclusion: Patients who were of a more advanced age and had diabetes were at greater risk of femoral catheter failure, whereas those who received femoral catheters from the right side were less at risk of catheter failure.

  15. Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty

    International Nuclear Information System (INIS)

    Zhu, Shun-Peng; Huang, Hong-Zhong; Peng, Weiwen; Wang, Hai-Kun; Mahadevan, Sankaran

    2016-01-01

    A probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs operating under uncertainty is developed. The framework incorporates the overall uncertainties appearing in a structural integrity assessment. A comprehensive uncertainty quantification (UQ) procedure is presented to quantify multiple types of uncertainty using multiplicative and additive UQ methods. In addition, the factors that contribute the most to the resulting output uncertainty are investigated and identified for uncertainty reduction in decision-making. A high prediction accuracy of the proposed framework is validated through a comparison of model predictions to the experimental results of GH4133 superalloy and full-scale tests of aero engine high-pressure turbine discs. - Highlights: • A probabilistic PoF-based framework for fatigue life prediction is proposed. • A comprehensive procedure forquantifyingmultiple types of uncertaintyis presented. • The factors that contribute most to the resulting output uncertainty are identified. • The proposed frameworkdemonstrates high prediction accuracybyfull-scale tests.

  16. New Combined Scoring System for Predicting Respiratory Failure in Iraqi Patients with Guillain-Barré Syndrome

    Directory of Open Access Journals (Sweden)

    Zaki Noah Hasan

    2010-09-01

    Full Text Available The Guillain-Barré syndrome (GBS is an acute post-infective autoimmune polyradiculoneuropathy, it is the commonest peripheral neuropathy causing respiratory failure. The aim of the study is to use the New Combined Scoring System in anticipating respiratory failure in order to perform elective measures without waiting for emergency situations to occur.
    Patients and methods: Fifty patients with GBS were studied. Eight clinical parameters (including progression of patients to maximum weakness, respiratory rate/minute, breath holding
    count (the number of digits the patient can count in holding his breath, presence of facial muscle weakness (unilateral or bilateral, presence of weakness of the bulbar muscle, weakness of the neck flexor muscle, and limbs weakness were assessed for each patient and a certain score was given to
    each parameter, a designed combined score being constructed by taking into consideration all the above mentioned clinical parameters. Results and discussion: Fifteen patients (30% that were enrolled in our study developed respiratory failure. There was a highly significant statistical association between the development of respiratory failure and the lower grades of (bulbar muscle weakness score, breath holding count scores, neck muscle weakness score, lower limbs and upper limbs weakness score , respiratory rate score and the total sum score above 16 out of 30 (p-value=0.000 . No significant statistical difference was found regarding the progression to maximum weakness (p-value=0.675 and facial muscle weakness (p-value=0.482.
    Conclusion: The patients who obtained a combined score (above 16’30 are at great risk of having respiratory failure.

  17. Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

    Science.gov (United States)

    Miksovsky, J.; Raidl, A.

    Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

  18. Parameters Online Detection and Model Predictive Control during the Grain Drying Process

    Directory of Open Access Journals (Sweden)

    Lihui Zhang

    2013-01-01

    Full Text Available In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

  19. Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

    DEFF Research Database (Denmark)

    Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen

    Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

  20. Health Literacy and Global Cognitive Function Predict E-Mail but Not Internet Use in Heart Failure Patients

    Directory of Open Access Journals (Sweden)

    Jared P. Schprechman

    2013-01-01

    Full Text Available Background. The internet offers a potential for improving patient knowledge, and e-mail may be used in patient communication with providers. However, barriers to internet and e-mail use, such as low health literacy and cognitive impairment, may prevent patients from using technological resources. Purpose. We investigated whether health literacy, heart failure knowledge, and cognitive function were related to internet and e-mail use in older adults with heart failure (HF. Methods. Older adults (N=119 with heart failure (69.84±9.09 years completed measures of health literacy, heart failure knowledge, cognitive functioning, and internet use in a cross-sectional study. Results. Internet and e-mail use were reported in 78.2% and 71.4% of this sample of patients with HF, respectively. Controlling for age and education, logistic regression analyses indicated that higher health literacy predicted e-mail (P<.05 but not internet use. Global cognitive function predicted e-mail (P<.05 but not internet use. Only 45% used the Internet to obtain information on HF and internet use was not associated with greater HF knowledge. Conclusions. The majority of HF patients use the internet and e-mail, but poor health literacy and cognitive impairment may prevent some patients from accessing these resources. Future studies that examine specific internet and email interventions to increase HF knowledge are needed.

  1. Prediction of Cascading Collapse Occurrence due to the Effect of Hidden Failure of a Protection System using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Nor Hazwani Idris

    2017-06-01

    Full Text Available Transmission line act as a medium of transportation for electrical energy from a power station to the consumer. There are many factors that could cause the cascading collapse such as instability of voltage and frequency, the change of environment and weather, the software and operator error and also the failure in protection system. Protection system plays an important function in maintaining the stability and reliability of the power grid. Hidden failures in relay protection systems are the primary factors for triggering the cascading collapse. This paper presents an Artificial Neural Network (ANN model for prediction of cascading collapse occurrence due to the effect of hidden failure of protection system. The ANN model has been developed through the normalized training and testing data process with optimum number of hidden layer, the momentum rate and the learning rate. The ANN model employs probability of hidden failure, random number of line limit power flow and exposed line as its input while trip index of cascading collapse occurrence as its output. IEEE 14 bus system is used in this study to illustrate the proposed approach. The performance of the results is analysed in terms of its Mean Square Error (MSE and Correlation Coefficient (R. The results show the ANN model produce reliable prediction of cascading collapse occurrence.

  2. Is there a way to predict failure after direct vision internal urethrotomy for single and short bulbar urethral strictures?

    Science.gov (United States)

    Harraz, Ahmed M; El-Assmy, Ahmed; Mahmoud, Osama; Elbakry, Amr A; Tharwat, Mohamed; Omar, Helmy; Farg, Hashim; Laymon, Mahmoud; Mosbah, Ahmed

    2015-12-01

    To identify patient and stricture characteristics predicting failure after direct vision internal urethrotomy (DVIU) for single and short (urethroplasty. Predictors of failure were analysed. In all, 430 adult patients with a mean (SD) age of 50 (15) years were included. The main causes of stricture were idiopathic followed by iatrogenic in 51.6% and 26.3% of patients, respectively. Most patients presented with obstructive lower urinary tract symptoms (68.9%) and strictures were proximal bulbar, i.e. just close to the external urethral sphincter, in 35.3%. The median (range) follow-up duration was 29 (3-132) months. In all, 250 (58.1%) patients did not require any further instrumentation, while RSD was maintained in 116 (27%) patients, including 28 (6.5%) who required a redo DVIU or urethroplasty. In 64 (6.5%) patients, a redo DVIU or urethroplasty was performed. On multivariate analysis, older age at presentation [odds ratio (OR) 1.017; P = 0.03], obesity (OR 1.664; P = 0.015), and idiopathic strictures (OR 3.107; P = 0.035) were independent predictors of failure after DVIU. The failure rate after DVIU accounted for 41.8% of our present cohort with older age at presentation, obesity, and idiopathic strictures independent predictors of failure after DVIU. This information is important in counselling patients before surgery.

  3. Clinical Significance of Hemostatic Parameters in the Prediction for Type 2 Diabetes Mellitus and Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Lianlian Pan

    2018-01-01

    Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.

  4. Safety analysis methodology with assessment of the impact of the prediction errors of relevant parameters

    International Nuclear Information System (INIS)

    Galia, A.V.

    2011-01-01

    The best estimate plus uncertainty approach (BEAU) requires the use of extensive resources and therefore it is usually applied for cases in which the available safety margin obtained with a conservative methodology can be questioned. Outside the BEAU methodology, there is not a clear approach on how to deal with the issue of considering the uncertainties resulting from prediction errors in the safety analyses performed for licensing submissions. However, the regulatory document RD-310 mentions that the analysis method shall account for uncertainties in the analysis data and models. A possible approach is presented, that is simple and reasonable, representing just the author's views, to take into account the impact of prediction errors and other uncertainties when performing safety analysis in line with regulatory requirements. The approach proposes taking into account the prediction error of relevant parameters. Relevant parameters would be those plant parameters that are surveyed and are used to initiate the action of a mitigating system or those that are representative of the most challenging phenomena for the integrity of a fission barrier. Examples of the application of the methodology are presented involving a comparison between the results with the new approach and a best estimate calculation during the blowdown phase for two small breaks in a generic CANDU 6 station. The calculations are performed with the CATHENA computer code. (author)

  5. Doppler Ultrasonographic Parameters for Predicting Cerebral Vascular Reserve in Patients with Acute Ischemic Stroke

    International Nuclear Information System (INIS)

    Jung, Han Young; Lee, Hui Joong; Kim, Hye Jung; Kim, Yong Sun; Kang, Duk Sik

    2006-01-01

    We investigated Doppler ultrasonographic (US) parameters of patients with acute stroke to predict the cerebral vascular reserve (CVR) measured by SPECT. We reviewed the flow velocity and cross-sectional area of the circular vessel at the common, external, and internal carotid arteries (ICA) and the vertebral arteries (VA) in 109 acute stroke patients who underwent SPECT. Flow volume (FV) of each artery was calculated as the product of the angle-corrected time averaged flow velocity and cross-sectional area of the circular vessel. Total cerebral FV (TCBFV) was determined as the sum of the FVs of the right and left ICA and VA. We compared the Doppler US parameters between 44 cases of preserved and 65 cases of impaired CVR. In the preserved CVR group, ICA FV, anterior circulating FV (ACFV) and TCBFV were higher than in the impaired CVR group (p < 0.05, independent t-test). In the impaired CVR group, the ROC curves showed ACFV and TCBFV were suitable parameters to predict CVR (p < 0.05). Doppler US was helpful for understanding the hemodynamic state of acute stroke. FV measurement by Doppler US was useful for predicting CVR

  6. Predicting hepatic steatosis and liver fat content in obese children based on biochemical parameters and anthropometry.

    Science.gov (United States)

    Zhang, H-X; Xu, X-Q; Fu, J-F; Lai, C; Chen, X-F

    2015-04-01

    Predictors of quantitative evaluation of hepatic steatosis and liver fat content (LFC) using clinical and laboratory variables available in the general practice in the obese children are poorly identified. To build predictive models of hepatic steatosis and LFC in obese children based on biochemical parameters and anthropometry. Hepatic steatosis and LFC were determined using proton magnetic resonance spectroscopy in 171 obese children aged 5.5-18.0 years. Routine clinical and laboratory parameters were also measured in all subjects. Group analysis, univariable correlation analysis, and multivariate logistic and linear regression analysis were used to develop a liver fat score to identify hepatic steatosis and a liver fat equation to predict LFC in each subject. The predictive model of hepatic steatosis in our participants based on waist circumference and alanine aminotransferase had an area under the receiver operating characteristic curve of 0.959 (95% confidence interval: 0.927-0.990). The optimal cut-off value of 0.525 for determining hepatic steatosis had sensitivity of 93% and specificity of 90%. A liver fat equation was also developed based on the same parameters of hepatic steatosis liver fat score, which would be used to calculate the LFC in each individual. The liver fat score and liver fat equation, consisting of routinely available variables, may help paediatricians to accurately determine hepatic steatosis and LFC in clinical practice, but external validation is needed before it can be employed for this purpose. © 2014 The Authors. Pediatric Obesity © 2014 World Obesity.

  7. An improved method for predicting the evolution of the characteristic parameters of an information system

    Science.gov (United States)

    Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

    2018-03-01

    The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

  8. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

    NARCIS (Netherlands)

    Tangri, N.; Grams, M.E.; Levey, A.S.; Coresh, J.; Appel, L.J.; Astor, B.C.; Chodick, G.; Collins, A.J.; Djurdjev, O.; Elley, C.R.; Evans, M.; Garg, A.X.; Hallan, S.I.; Inker, L.A.; Ito, S.; Jee, S.H.; Kovesdy, C.P.; Kronenberg, F.; Heerspink, H.J.; Marks, A.; Nadkarni, G.N.; Navaneethan, S.D.; Nelson, R.G.; Titze, S.; Sarnak, M.J.; Stengel, B.; Woodward, M.; Iseki, K.; Wetzels, J.F.M.; et al.,

    2016-01-01

    IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously

  9. A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events

    Data.gov (United States)

    National Aeronautics and Space Administration — Particle filters (PF) have been established as the de facto state of the art in failure prognosis, and particularly in the representation and management of...

  10. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    Science.gov (United States)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  11. Failure prediction for the optimization of stretch forming aluminium-polymer laminate foils used for pharmaceutical packaging

    Science.gov (United States)

    Müller, Simon; Weygand, Sabine M.

    2018-05-01

    Axisymmetric stretch forming processes of aluminium-polymer laminate foils (e.g. consisting of PA-Al-PVC layers) are analyzed numerically by finite element modeling of the multi-layer material as well as experimentally in order to identify a suitable damage initiation criterion. A simple ductile fracture criterion is proposed to predict the forming limits. The corresponding material constants are determined from tensile tests and then applied in forming simulations with different punch geometries. A comparison between the simulations and the experimental results shows that the determined failure constants are not applicable. Therefore, one forming experiment was selected and in the corresponding simulation the failure constant was fitted to its measured maximum stretch. With this approach it is possible to predict the forming limit of the laminate foil with satisfying accuracy for different punch geometries.

  12. Development of Health Parameter Model for Risk Prediction of CVD Using SVM

    Directory of Open Access Journals (Sweden)

    P. Unnikrishnan

    2016-01-01

    Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

  13. Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

    Science.gov (United States)

    Kaklamanos, James; Baise, Laurie G.; Boore, David M.

    2011-01-01

    The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

  14. Lower head failure analysis

    International Nuclear Information System (INIS)

    Rempe, J.L.; Thinnes, G.L.; Allison, C.M.; Cronenberg, A.W.

    1991-01-01

    The US Nuclear Regulatory Commission is sponsoring a lower vessel head research program to investigate plausible modes of reactor vessel failure in order to determine (a) which modes have the greatest likelihood of occurrence during a severe accident and (b) the range of core debris and accident conditions that lead to these failures. This paper presents the methodology and preliminary results of an investigation of reactor designs and thermodynamic conditions using analytic closed-form approximations to assess the important governing parameters in non-dimensional form. Preliminary results illustrate the importance of vessel and tube geometrical parameters, material properties, and external boundary conditions on predicting vessel failure. Thermal analyses indicate that steady-state temperature distributions will occur in the vessel within several hours, although the exact time is dependent upon vessel thickness. In-vessel tube failure is governed by the tube-to-debris mass ratio within the lower head, where most penetrations are predicted to fail if surrounded by molten debris. Melt penetration distance is dependent upon the effective flow diameter of the tube. Molten debris is predicted to penetrate through tubes with a larger effective flow diameter, such as a boiling water reactor (BWR) drain nozzle. Ex-vessel tube failure for depressurized reactor vessels is predicted to be more likely for a BWR drain nozzle penetration because of its larger effective diameter. At high pressures (between ∼0.1 MPa and ∼12 MPa) ex-vessel tube rupture becomes a dominant failure mechanism, although tube ejection dominates control rod guide tube failure at lower temperatures. However, tube ejection and tube rupture predictions are sensitive to the vessel and tube radial gap size and material coefficients of thermal expansion

  15. An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car

    International Nuclear Information System (INIS)

    Ho, Tien; Karri, Vishy; Lim, Daniel; Barret, Danny

    2008-01-01

    With the depletion of fossil fuel resources and the potential consequences of climate change due to fossil fuel use, much effort has been put into the search for alternative fuels for transportation. Although there are several potential alternative fuels, which have low impact on the environment, none of these fuels have the ability to be used as the sole 'fuel of the future'. One fuel which is likely to become a part of the over all solution to the transportation fuel dilemma is hydrogen. In this paper, The Toyota Corolla four cylinder, 1.8 l engine running on petrol is systematically converted to run on hydrogen. Several ancillary instruments for measuring various engine operating parameters and emissions are fitted to appraise the performance of the hydrogen car. The effect of hydrogen as a fuel compares with gasoline on engine operating parameters and effect of engine operating parameters on emission characteristics is discussed. Based on the experimental setup, a suite of neural network models were tested to accurately predict the effect of major engine operating conditions on the hydrogen car emissions. Predictions were found to be ±4% to the experimental values. This work provided better understanding of the effect of engine process parameters on emissions. (author)

  16. Prediction of polycystic ovarian syndrome based on ultrasound findings and clinical parameters.

    Science.gov (United States)

    Moschos, Elysia; Twickler, Diane M

    2015-03-01

    To determine the accuracy of sonographic-diagnosed polycystic ovaries and clinical parameters in predicting polycystic ovarian syndrome. Medical records and ultrasounds of 151 women with sonographically diagnosed polycystic ovaries were reviewed. Sonographic criteria for polycystic ovaries were based on 2003 Rotterdam European Society of Human Reproduction and Embryology/American Society for Reproductive Medicine guidelines: at least one ovary with 12 or more follicles measuring 2-9 mm and/or increased ovarian volume >10 cm(3) . Clinical variables of age, gravidity, ethnicity, body mass index, and sonographic indication were collected. One hundred thirty-five patients had final outcomes (presence/absence of polycystic ovarian syndrome). Polycystic ovarian syndrome was diagnosed if a patient had at least one other of the following two criteria: oligo/chronic anovulation and/or clinical/biochemical hyperandrogenism. A logistic regression model was constructed using stepwise selection to identify variables significantly associated with polycystic ovarian syndrome (p polycystic ovaries and 115 (89.8%) had polycystic ovarian syndrome (p = .009). Lower gravidity, abnormal bleeding, and body mass index >33 were significant in predicting polycystic ovarian syndrome (receiver operating characteristics curve, c = 0.86). Pain decreased the likelihood of polycystic ovarian syndrome. Polycystic ovaries on ultrasound were sensitive in predicting polycystic ovarian syndrome. Ultrasound, combined with clinical parameters, can be used to generate a predictive index for polycystic ovarian syndrome. © 2014 Wiley Periodicals, Inc.

  17. Improved time series prediction with a new method for selection of model parameters

    International Nuclear Information System (INIS)

    Jade, A M; Jayaraman, V K; Kulkarni, B D

    2006-01-01

    A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

  18. A single-item self-report medication adherence question predicts hospitalisation and death in patients with heart failure.

    Science.gov (United States)

    Wu, Jia-Rong; DeWalt, Darren A; Baker, David W; Schillinger, Dean; Ruo, Bernice; Bibbins-Domingo, Kristen; Macabasco-O'Connell, Aurelia; Holmes, George M; Broucksou, Kimberly A; Erman, Brian; Hawk, Victoria; Cene, Crystal W; Jones, Christine DeLong; Pignone, Michael

    2014-09-01

    To determine whether a single-item self-report medication adherence question predicts hospitalisation and death in patients with heart failure. Poor medication adherence is associated with increased morbidity and mortality. Having a simple means of identifying suboptimal medication adherence could help identify at-risk patients for interventions. We performed a prospective cohort study in 592 participants with heart failure within a four-site randomised trial. Self-report medication adherence was assessed at baseline using a single-item question: 'Over the past seven days, how many times did you miss a dose of any of your heart medication?' Participants who reported no missing doses were defined as fully adherent, and those missing more than one dose were considered less than fully adherent. The primary outcome was combined all-cause hospitalisation or death over one year and the secondary endpoint was heart failure hospitalisation. Outcomes were assessed with blinded chart reviews, and heart failure outcomes were determined by a blinded adjudication committee. We used negative binomial regression to examine the relationship between medication adherence and outcomes. Fifty-two percent of participants were 52% male, mean age was 61 years, and 31% were of New York Heart Association class III/IV at enrolment; 72% of participants reported full adherence to their heart medicine at baseline. Participants with full medication adherence had a lower rate of all-cause hospitalisation and death (0·71 events/year) compared with those with any nonadherence (0·86 events/year): adjusted-for-site incidence rate ratio was 0·83, fully adjusted incidence rate ratio 0·68. Incidence rate ratios were similar for heart failure hospitalisations. A single medication adherence question at baseline predicts hospitalisation and death over one year in heart failure patients. Medication adherence is associated with all-cause and heart failure-related hospitalisation and death in heart

  19. Long-term success and failure with SG is predictable by 3 months: a multivariate model using simple office markers.

    Science.gov (United States)

    Cottam, Austin; Billing, Josiah; Cottam, Daniel; Billing, Peter; Cottam, Samuel; Zaveri, Hinali; Surve, Amit

    2017-08-01

    Despite being the most common surgery in the United States, little is known about predicting weight loss success and failure with sleeve gastrectomy (SG). Papers that have been published are inconclusive. We decided to use multivariate analysis from 2 practices to design a model to predict weight loss outcomes using data widely available to any surgical practice at 3 months to determine weight loss outcomes at 1 year. Two private practices in the United States. A retrospective review of 613 patients from 2 bariatric institutions were included in this study. Co-morbidities and other preoperative characteristics were gathered, and %EWL was calculated for 1, 3, and 12 months. Excess weight loss (%EWL)failure. Multiple variate analysis was used to find factors that affect %EWL at 12 months. Preoperative sleep apnea, preoperative diabetes, %EWL at 1 month, and %EWL at 3 months all affect %EWL at 1 year. The positive predictive value and negative predictive value of our model was 72% and 91%, respectively. Sensitivity and specificity were 71% and 91%, respectively. One-year results of the SG can be predicted by diabetes, sleep apnea, and weight loss velocity at 3 months postoperatively. This can help surgeons direct surgical or medical interventions for patients at 3 months rather than at 1 year or beyond. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

  20. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

    Science.gov (United States)

    Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

    2013-04-01

    Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

  1. Systematic review of dose-volume parameters in the prediction of esophagitis in thoracic radiotherapy

    International Nuclear Information System (INIS)

    Rose, Jim; Rodrigues, George; Yaremko, Brian; Lock, Michael; D'Souza, David

    2009-01-01

    Purpose: With dose escalation and increasing use of concurrent chemoradiotherapy, radiation esophagitis (RE) remains a common treatment-limiting acute side effect in the treatment of thoracic malignancies. The advent of 3DCT planning has enabled investigators to study esophageal dose-volume histogram (DVH) parameters as predictors of RE. The purpose of this study was to assess published dosimetric parameters and toxicity data systematically in order to define reproducible predictors of RE, both for potential clinical use, and to provide recommendations for future research in the field. Materials and methods: We performed a systematic literature review of published studies addressing RE in the treatment of lung cancer and thymoma. Our search strategy included a variety of electronic medical databases, textbooks and bibliographies. Both prospective and retrospective clinical studies were included. Information relating to the relationship among measured dosimetric parameters, patient demographics, tumor characteristics, chemotherapy and RE was extracted and analyzed. Results: Eighteen published studies were suitable for analysis. Eleven of these assessed acute RE, while the remainder assessed both acute and chronic RE together. Heterogeneity of esophageal contouring practices, individual differences in information reporting and variability of RE outcome definitions were assessed. Well-described clinical and logistic modeling directly related V 35Gy , V 60Gy and SA 55Gy to clinically significant RE. Conclusions: Several reproducible dosimetric parameters exist in the literature, and these may be potentially relevant in the prediction of RE in the radiotherapy of thoracic malignancies. Further clarification of the predictive relationship between such standardized dosimetric parameters and observed RE outcomes is essential to develop efficient radiation treatment planning in locally advanced NSCLC in the modern concurrent chemotherapy and image-guided IMRT era.

  2. Variables predictive of outcome in patients with acute hypercapneic respiratory failure treated with noninvasive ventilation

    International Nuclear Information System (INIS)

    Salahuddin, N.; Irfan, M.; Khan, S.; Naeem, M.; Haque, A.S.

    2010-01-01

    To assess results with NIV in acute hypercapneic respiratory failure and to identify outcome predictors. This was a retrospective observational study on consecutive patients presenting with acute type II respiratory failure and meeting criteria for NIV use over a 5 year period. Patients presenting with haemodynamic instability, inability to protect their airway, malignant arrhythmias and recent oesophageal surgery were excluded. Univariate and Multivariate regression analysis was used to determine the impact on survival. A p value of 35 Meq/L (adjusted Odds ratio 0.9; 95% CI 0.83, 0.98, p < 0.015) identified those less at risk for intubation. NIV was found to be both safe and effective in the management of acute hypercapneic respiratory failure. Sepsis and serum HCO/sub 3/ at admission identified patients having poor outcomes (JPMA 60:13; 2010). (author)

  3. Predictive factors for renal failure and a control and treatment algorithm

    Directory of Open Access Journals (Sweden)

    Denise de Paula Cerqueira

    2014-04-01

    Full Text Available OBJECTIVES: to evaluate the renal function of patients in an intensive care unit, to identify the predisposing factors for the development of renal failure, and to develop an algorithm to help in the control of the disease.METHOD: exploratory, descriptive, prospective study with a quantitative approach.RESULTS: a total of 30 patients (75.0% were diagnosed with kidney failure and the main factors associated with this disease were: advanced age, systemic arterial hypertension, diabetes mellitus, lung diseases, and antibiotic use. Of these, 23 patients (76.6% showed a reduction in creatinine clearance in the first 24 hours of hospitalization.CONCLUSION: a decline in renal function was observed in a significant number of subjects, therefore, an algorithm was developed with the aim of helping in the control of renal failure in a practical and functional way.

  4. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jin Won [Graduate School of Catholic University of Pusan, Busan (Korea, Republic of); Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo [Dept. Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan (Korea, Republic of)

    2016-12-15

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening.

  5. A Bayesian approach for parameter estimation and prediction using a computationally intensive model

    International Nuclear Information System (INIS)

    Higdon, Dave; McDonnell, Jordan D; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

    2015-01-01

    Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

  6. Guidelineness of the parameters using integrated test in down syndrome risk prediction

    International Nuclear Information System (INIS)

    Lee, Jin Won; Go, Sung Jin; Kang, Se Sik; Kim, Chang Soo

    2016-01-01

    This study was an evaluation of the significance of each parameter through aimed at pregnant women subjected to screening test(integrated test) in predicting risk of Down syndrome. We retrospectively analysed the correlation of risk of Down's syndrome with Nuchal Translucency(NT) images measured by ultrasound, Pregnancy Associated Plasma Protein A(PAPP-A), alpha-fetoprotein(AFP), unconjugated estriol(uE3), human chorionic gonadotrophin(hCG) and Inhibin A by maternal serum. As a result, a significant correlation with NT, uE3, hCG, Inhibin A is revealed with Down's syndrome risk(P<.001). In ROC analysis, AUC of Inhibin A is analysed as the biggest predictor of Down's syndrome(0.859). And the criterion for cut-off was inhibin A 1.4 MoM(sensitivity 81.8%, specificity 75.9%). In conclusion, Inhibin A was the most useful in parameters to predict Down's syndrome in the integrated test. If we make up for the weakness based on the cut-off value of parameters they will be able to be used as an independent indicator in the risk of Down's syndrome screening

  7. A predictive thermal dynamic model for parameter generation in the laser assisted direct write process

    International Nuclear Information System (INIS)

    Shang Shuo; Fearon, Eamonn; Wellburn, Dan; Sato, Taku; Edwardson, Stuart; Dearden, G; Watkins, K G

    2011-01-01

    The laser assisted direct write (LADW) method can be used to generate electrical circuitry on a substrate by depositing metallic ink and curing the ink thermally by a laser. Laser curing has emerged over recent years as a novel yet efficient alternative to oven curing. This method can be used in situ, over complicated 3D contours of large parts (e.g. aircraft wings) and selectively cure over heat sensitive substrates, with little or no thermal damage. In previous studies, empirical methods have been used to generate processing windows for this technique, relating to the several interdependent processing parameters on which the curing quality and efficiency strongly depend. Incorrect parameters can result in a track that is cured in some areas and uncured in others, or in damaged substrates. This paper addresses the strong need for a quantitative model which can systematically output the processing conditions for a given combination of ink, substrate and laser source; transforming the LADW technique from a purely empirical approach, to a simple, repeatable, mathematically sound, efficient and predictable process. The method comprises a novel and generic finite element model (FEM) that for the first time predicts the evolution of the thermal profile of the ink track during laser curing and thus generates a parametric map which indicates the most suitable combination of parameters for process optimization. Experimental data are compared with simulation results to verify the accuracy of the model.

  8. Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

    Science.gov (United States)

    Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.

    2013-06-01

    Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that

  9. Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Vikas, E-mail: vikas.kumar@urv.cat [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Barros, Felipe P.J. de [Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles 90089, CA (United States); Schuhmacher, Marta [Department of Chemical Engineering, Rovira i Virgili University, Tarragona 43007 (Spain); Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier [Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, University Politècnica de Catalunya-BarcelonaTech, Barcelona 08034 (Spain)

    2013-12-15

    Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

  10. Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Sunil Khuntia

    2014-09-01

    Full Text Available This study presents the application of artificial neural networks (ANN and least square support vector machine (LS-SVM for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

  11. Strength Prediction and Failure Modes of Concrete Specimens Subjected to the Split Test

    DEFF Research Database (Denmark)

    Hoang, Linh Cao; Andersen, M.E.; Hansen, N.T.

    2014-01-01

    This paper deals with modelling and test of concrete specimens subjected to the Brazilian split test. Based on the fictitious crack concept, a simple model for the crack propagation process in the splitting plane is developed. From the model, it is possible to determine the distribution of residual...... tensile strength as crack propagation take place. The residual tensile strength is thereafter used in a rigid plastic analysis of the splitting failure. Based on this combined approach, the ultimate load may either be governed by crack propagation or by a plastic failure, which then terminates the crack...

  12. On the development of life prediction methodologies for the failure of human teeth

    Energy Technology Data Exchange (ETDEWEB)

    Nalla, R.K.; Imbeni, V.; Kinney, J.H.; Marshall, S.J.; Ritchie, R.O.

    2002-09-18

    Human dentin is known to be susceptible to failure under cyclic loading. Surprisingly, there are few reports that quantify the effect of such loading, considering the fact that a typical tooth experiences a million or so loading cycles annually. In the present study, a systematic investigation is described of the effects of prolonged cyclic loading on human dentin in a simulated physiological environment. In vitro stress-life (S/N) data are discussed in the context of possible mechanisms of fatigue damage and failure.

  13. Predictive parameters of infectiologic complications in patients after TIPSS; Praediktive Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage

    Energy Technology Data Exchange (ETDEWEB)

    Cohnen, M.; Saleh, A.; Moedder, U. [Institut fuer Diagnostische Radiologie, Universitaetsklinikum Duesseldorf (Germany); Luethen, R.; Bode, J.; Haeussinger, D. [Klinik fuer Gastroenterologie, Hepatologie und Infektiologie, Universitaetsklinikum Duesseldorf (Germany); Daeubener, W. [Institut fuer Mikrobiologie und Virologie, Universitaetsklinikum Duesseldorf (Germany)

    2003-02-01

    Aim To define predictive parameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/{mu}l) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8{+-}1.0; d1: 3.2{+-}1.5; d7: 4.3{+-}3.2), and white blood cell count (d0: 7700{+-}2600; d1: 10800{+-}2800; d7: 7500{+-}1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6{+-}0.6; d1: 1.8{+-}1.0; d7: 1.9{+-}0.6. WBC: d0: 6900{+-}1500; d1: 8000{+-}1600; d7: 7600{+-}1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [German] Fragestellung Definition praediktiver Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage.Methodik Bei 41 Patienten wurden Blutproben prospektiv vor intrahepatischer Stentanlage zentralvenoes und portalvenoes sowie 20 min postinterventionell erneut zentralvenoes entnommen und mikrobiologisch analysiert. C-reaktives Protein (CRP) (mg/dl) und Leukozytenzahl (/{mu}l) wurden am Interventionstag (d0), am 1. (d1) sowie 7 Tage (d7) postinterventionell bestimmt. Patienten mit kompliziertem Verlauf (spontane bakterielle Peritonitis,Pneumonie, Sepsis; Gruppe 1) wurden von Patienten ohne klinische Komplikationen (Gruppe 2) unterschieden.Ergebnisse Gruppe 1 wies einen signifikanten Anstieg des CRP (d0: 1,8{+-}1,0; d1: 3,2{+-}1,5; d7: 4,3{+-}3,2) und

  14. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure : A Meta-analysis

    NARCIS (Netherlands)

    Tangri, Navdeep; Grams, Morgan E.; Levey, Andrew S.; Coresh, Josef; Appel, Lawrence J.; Astor, Brad C.; Chodick, Gabriel; Collins, Allan J.; Djurdjev, Ognjenka; Elley, Raina; Evans, Marie; Garg, Amit X.; Hallan, Stein I.; Nicer, Lesley A.; Ito, Sadayoshi; Jee, Sun Ha; Kovesdy, Csaba P.; Kronenberg, Florian; Heerspink, Hiddo J. Lambers; Marks, Angharad; Nadkarni, Girish N.; Navaneethan, Sankar D.; Nelson, Robert G.; Titze, Stephanie; Sarnak, Mark J.; Stengel, Benedicte; Woodward, Mark; Iseki, Kunitoshi

    2016-01-01

    IMPORTANCE Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a

  15. Acute renal failure in the medical ICU still predictive of high mortality ...

    African Journals Online (AJOL)

    Background. We aimed to determine the outcome and certain predictors of outcome for acute renal failure (ARF) in the medical intensive care unit (ICU) at Tygerberg Hospital. Method. We conducted a retrospective, single-centre cohort study over 12 months comprising all patients admitted to the medical ICU with all causes ...

  16. An Evolutionary Algorithm for Feature Subset Selection in Hard Disk Drive Failure Prediction

    Science.gov (United States)

    Bhasin, Harpreet

    2011-01-01

    Hard disk drives are used in everyday life to store critical data. Although they are reliable, failure of a hard disk drive can be catastrophic, especially in applications like medicine, banking, air traffic control systems, missile guidance systems, computer numerical controlled machines, and more. The use of Self-Monitoring, Analysis and…

  17. Early Treatment Outcome in Failure to Thrive: Predictions from a Transactional Model.

    Science.gov (United States)

    Drotar, Dennis

    Children diagnosed with environmentally based failure to thrive early during their first year of life were seen at 12 and 18 months for assessment of psychological development (cognition, language, symbolic play, and behavior during testing). Based on a transactional model of outcome, factors reflecting biological vulnerability (wasting and…

  18. A comparison of predictive algorithms for failure prevention in smart environment applications

    NARCIS (Netherlands)

    Warriach, E.U.; Ozcelebi, T.; Lukkien, J.J.

    2015-01-01

    The functional correctness and the performance of smart environment applications can be hampered by faults. Fault tolerance solutions aim to achieve graceful performance degradation in the presence of faults, ideally without leading to application failures. This is a reactive approach and, by

  19. Predicting kidney graft failure using time-dependent renal function covariates

    NARCIS (Netherlands)

    de Bruijne, Mattheus H. J.; Sijpkens, Yvo W. J.; Paul, Leendert C.; Westendorp, Rudi G. J.; van Houwelingen, Hans C.; Zwinderman, Aeilko H.

    2003-01-01

    Chronic rejection and recurrent disease are the major causes of late graft failure in renal transplantation. To assess outcome, most researchers use Cox proportional hazard analysis with time-fixed covariates. We developed a model adding time-dependent renal function covariates to improve the

  20. Application of a simple parameter estimation method to predict effluent transport in the Savannah River

    International Nuclear Information System (INIS)

    Hensel, S.J.; Hayes, D.W.

    1993-01-01

    A simple parameter estimation method has been developed to determine the dispersion and velocity parameters associated with stream/river transport. The unsteady one dimensional Burgers' equation was chosen as the model equation, and the method has been applied to recent Savannah River dye tracer studies. The computed Savannah River transport coefficients compare favorably with documented values, and the time/concentration curves calculated from these coefficients compare well with the actual tracer data. The coefficients were used as a predictive capability and applied to Savannah River tritium concentration data obtained during the December 1991 accidental tritium discharge from the Savannah River Site. The peak tritium concentration at the intersection of Highway 301 and the Savannah River was underpredicted by only 5% using the coefficients computed from the dye data

  1. Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units. Using Multiclassifiers and Feature Selection Methods.

    Science.gov (United States)

    Martín-González, Félix; González-Robledo, Javier; Sánchez-Hernández, Fernando; Moreno-García, María N

    2016-05-17

    This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventilation (NIMV) in intensive care units. Data mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas. Feature selection methods provided the most influential variables in the success/failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method. Data mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.

  2. [The role of epicardial fat and obesity parameters in the prediction of coronary heart disease].

    Science.gov (United States)

    Prídavková, Dana; Kantárová, Daniela; Lišková, Renáta; Červeň, Peter; Kovář, František; Mokáň, Marián

    2016-04-01

    To assess the relationship of parameters of obesity in relationship to coronary angiography findings with correlation of epicardial fat (EF) thickness in uppermentioned context. There were 80 patients examined (43 males, 37 postmenopausal females) undergoing elective coronary angiography. We examined the regular obesity parameters - BMI, waist circumference (WC), neck circumference (NC), total body fat (TBF), and visceral fat (VF) using bioimpedance. We assessed the echocardiographically measured EF thickness. We added examination of lipidogram, glycaemia, HOMA-IR (insulin resistance index) and AIP (aterogenic index of plasma). The set was divided into group with coronarographically proved stenosis or stenoses (withCS), and a group without finding of quantifiable stenosis or stenoses (withoutCS). The average thickness of EF in withCS group was 6.3 vs 5.6 mm in group withoutCS (p obesity parameters in assessment of pre-clinical stages of coronary atherosclerosis and prediction of risk of coronary heart disease. In adipose parameters, EF thickness was correlated the most by WC. Risk stratification of coronary artery disease is supplemented by increased HOMA-IR and AIP.

  3. Cervical Vertebral Body’s Volume as a New Parameter for Predicting the Skeletal Maturation Stages

    Directory of Open Access Journals (Sweden)

    Youn-Kyung Choi

    2016-01-01

    Full Text Available This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age. We performed Pearson’s correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P<0.05. The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

  4. Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages.

    Science.gov (United States)

    Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

    2016-01-01

    This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

  5. MODEL JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI PARAMETER KUALITAS TOMAT BERDASARKAN PARAMETER WARNA RGB (An artificial neural network model for predicting tomato quality parameters based on color

    Directory of Open Access Journals (Sweden)

    Rudiati Evi Masithoh

    2013-03-01

    Full Text Available Artificial neural networks (ANN was used to predict the quality parameters of tomato, i.e. Brix, citric acid, total carotene, and vitamin C. ANN was developed from Red Green Blue (RGB image data of tomatoes measured using a developed computer vision system (CVS. Qualitative analysis of tomato compositions were obtained from laboratory experiments. ANN model was based on a feedforward backpropagation network with different training functions, namely gradient descent (traingd, gradient descent with the resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab and Shanno (BFGS quasi-Newton (trainbfg, as well as Levenberg Marquardt (trainlm.  The network structure using logsig and linear (purelin activation function at the hidden and output layer, respectively, and using  the trainlm as a training function resulted in the best performance. Correlation coefficient (r of training and validation process were 0.97 - 0.99 and 0.92 - 0.99, whereas the MAE values ​​ranged from 0.01 to 0.23 and 0.03 to 0.59, respectively. Keywords: Artificial neural network, trainlm, tomato, RGB   Jaringan syaraf tiruan (JST digunakan untuk memprediksi parameter kualitas tomat, yaitu Brix, asam sitrat, karoten total, dan vitamin C. JST dikembangkan dari data Red Green Blue (RGB  citra tomat yang diukur menggunakan computer vision system. Data kualitas tomat diperoleh dari analisis di laboratorium. Struktur model JST didasarkan pada jaringan feedforward backpropagation dengan berbagai fungsi pelatihan, yaitu gradient descent (traingd, gradient descent dengan resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab dan Shanno (BFGS quasi-Newton (trainbfg, serta Levenberg Marquardt (trainlm. Fungsi pelatihan yang terbaik adalah menggunakan trainlm, serta pada struktur jaringan digunakan fungsi aktivasi logsig pada lapisan tersembunyi dan linier (purelin pada lapisan keluaran. dengan 1000 epoch. Nilai koefisien korelasi (r pada tahap pelatihan dan validasi

  6. Evolving chemometric models for predicting dynamic process parameters in viscose production

    Energy Technology Data Exchange (ETDEWEB)

    Cernuda, Carlos [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Lughofer, Edwin, E-mail: edwin.lughofer@jku.at [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Suppan, Lisbeth [Kompetenzzentrum Holz GmbH, St. Peter-Str. 25, 4021 Linz (Austria); Roeder, Thomas; Schmuck, Roman [Lenzing AG, 4860 Lenzing (Austria); Hintenaus, Peter [Software Research Center, Paris Lodron University Salzburg (Austria); Maerzinger, Wolfgang [i-RED Infrarot Systeme GmbH, Linz (Austria); Kasberger, Juergen [Recendt GmbH, Linz (Austria)

    2012-05-06

    Highlights: Black-Right-Pointing-Pointer Quality assurance of process parameters in viscose production. Black-Right-Pointing-Pointer Automatic prediction of spin-bath concentrations based on FTNIR spectra. Black-Right-Pointing-Pointer Evolving chemometric models for efficiently handling changing system dynamics over time (no time-intensive re-calibration needed). Black-Right-Pointing-Pointer Significant reduction of huge errors produced by statistical state-of-the-art calibration methods. Black-Right-Pointing-Pointer Sufficient flexibility achieved by gradual forgetting mechanisms. - Abstract: In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H{sub 2}SO{sub 4}, Na{sub 2}SO{sub 4} and Z{sub n}SO{sub 4}. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual

  7. Evaluation of selected predictive models and parameters for the environmental transport and dosimetry of radionuclides

    International Nuclear Information System (INIS)

    Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.

    1979-07-01

    Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of 131 I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for 232 Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models

  8. A nomogram for predicting distant brain failure in patients treated with gamma knife stereotactic radiosurgery without whole brain radiotherapy

    Science.gov (United States)

    Ayala-Peacock, Diandra N.; Peiffer, Ann M.; Lucas, John T.; Isom, Scott; Kuremsky, J. Griff; Urbanic, James J.; Bourland, J. Daniel; Laxton, Adrian W.; Tatter, Stephen B.; Shaw, Edward G.; Chan, Michael D.

    2014-01-01

    Background We review our single institution experience to determine predictive factors for early and delayed distant brain failure (DBF) after radiosurgery without whole brain radiotherapy (WBRT) for brain metastases. Materials and methods Between January 2000 and December 2010, a total of 464 patients were treated with Gamma Knife stereotactic radiosurgery (SRS) without WBRT for primary management of newly diagnosed brain metastases. Histology, systemic disease, RPA class, and number of metastases were evaluated as possible predictors of DBF rate. DBF rates were determined by serial MRI. Kaplan–Meier method was used to estimate rate of DBF. Multivariate analysis was performed using Cox Proportional Hazard regression. Results Median number of lesions treated was 1 (range 1–13). Median time to DBF was 4.9 months. Twenty-seven percent of patients ultimately required WBRT with median time to WBRT of 5.6 months. Progressive systemic disease (χ2= 16.748, P < .001), number of metastases at SRS (χ2 = 27.216, P < .001), discovery of new metastases at time of SRS (χ2 = 9.197, P < .01), and histology (χ2 = 12.819, P < .07) were factors that predicted for earlier time to distant failure. High risk histologic subtypes (melanoma, her2 negative breast, χ2 = 11.020, P < .001) and low risk subtypes (her2 + breast, χ2 = 11.343, P < .001) were identified. Progressive systemic disease (χ2 = 9.549, P < .01), number of brain metastases (χ2 = 16.953, P < .001), minimum SRS dose (χ2 = 21.609, P < .001), and widespread metastatic disease (χ2 = 29.396, P < .001) were predictive of shorter time to WBRT. Conclusion Systemic disease, number of metastases, and histology are factors that predict distant failure rate after primary radiosurgical management of brain metastases. PMID:24558022

  9. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

    Directory of Open Access Journals (Sweden)

    Chunxiao Zhang

    2012-01-01

    Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

  10. Predicting hospital-acquired infections by scoring system with simple parameters.

    Directory of Open Access Journals (Sweden)

    Ying-Jui Chang

    Full Text Available BACKGROUND: Hospital-acquired infections (HAI are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR and validated by Artificial Neural Networks (ANN simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507 to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447. The scoring system also performed extremely well in the internal (AUC: 0.965 and external (AUC: 0.871 validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction

  11. Predicting the success of noninvasive positive pressure ventilation in emergency room for patients with acute heart failure.

    Science.gov (United States)

    Shirakabe, Akihiro; Hata, Noritake; Yokoyama, Shinya; Shinada, Takuro; Kobayashi, Nobuaki; Tomita, Kazunori; Kitamura, Mitsunobu; Nozaki, Ayaka; Tokuyama, Hideo; Asai, Kuniya; Mizuno, Kyoichi

    2011-01-01

    Non-invasive positive pressure ventilation (NPPV) for acute heart failure (AHF) is increasingly used to avoid endotracheal intubation (ETI). We therefore reviewed our experience using respirator management in the emergency room for AHF, and evaluated the predictive factors in the success of NPPV in the emergency room. Three-hundred forty-three patients with AHF were analyzed. The AHF patients were assigned to either BiPAP-Synchrony (B-S; Respironics, Merrysville, PA, USA) period (2005-2007, n = 176) or BiPAP-Vision (B-V; Respironics) period (2008-2010, n = 167). The rate of carperitide use was significantly increased and dopamine use was significantly decreased in the B-V period. The total length of hospital stay was significantly shorter in the B-V period. AHF patients were also assigned to a failed trial of NPPV followed by ETI (NPPV failure group) or an NPPV success group in the emergency room for each period. NPPV was successfully used in 48 cases in the B-S period, and in 111 cases in the B-V period. Fifty-seven ETI patients included 45 direct ETI and 11 NPPV failure cases in the B-S period, and 16 ETI patients included 10 direct ETI and 6 NPPV failure cases in the B-V period. The pH values were significantly lower in the NPPV failure than in the NPPV success for both periods (7.19 ± 0.10 vs. 7.28 ± 0.11, B-S period, p successful estimates of NPPV with a high sensitivity and specificity, and the aortic blood gas level was above 7.03 pH when using the B-V system. Copyright © 2011 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

  12. Difficult to predict early failure after major lower-extremity amputations

    DEFF Research Database (Denmark)

    Kristensen, Morten Tange; Holm, Gitte; Gebuhr, Peter

    2015-01-01

    INTRODUCTION: The successful outcome of a major amputation depends on several factors, including stump wound healing. The purpose of this study was to examine the criteria upon which the index amputation was based and to identify factors associated with early amputation failure after major non......-traumatic lower-extremity amputation. METHODS: We studied a consecutive one-year series of 36 men and 34 women with a median (25-75% quartiles) age of 72 (63-83) years who were treated in an acute orthopaedic ward; 44 below-knee and 26 above-knee amputees of whom 47 had an American Society of Anesthesiologists...... rating above two. Patient characteristics and other factors potentially influencing early amputation failure within 30 days were evaluated. RESULTS: Eleven patients died (16%) and 11 (16%) had a re-amputation at a higher level, whereas four (6%) had a major revision at the same level within 30 days...

  13. Prediction of Radionuclide transfer based on soil parameters: application to vulnerability studies

    International Nuclear Information System (INIS)

    Roig, M.; Vidal, M.; Rauret, G.

    1998-01-01

    The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH 4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)

  14. Application of data mining in a maintenance system for failure prediction

    OpenAIRE

    Bastos, Pedro; Lopes, Isabel; Pires, L.C.M.

    2014-01-01

    In industrial environment, data generated during equipment maintenance and monitoring activities has become increasingly overwhelming. Data mining presents an opportunity to increase significantly the rate at which the volume of data can be turned into useful information. This paper presents an architecture designed to gather data generated in industrial units on their maintenance activities, and to forecast future failures based on data analysis. Rapid Miner is used to apply diff...

  15. Predictability of steel containment response near failure track 3 - structural integrity, dynamic behavior, and seismic design

    International Nuclear Information System (INIS)

    Costello, J.F.; Ludwigsen, J.S.; Luk, V.K.; Hessheimer, M.F.

    2000-01-01

    The Nuclear Power Engineering Corporation of Japan and the US Nuclear Regulatory Commission Office of Nuclear Regulatory Research, are co-sponsoring and jointly funding a Cooperative Containment Research Program at Sandia National Laboratories, Albuquerque, New Mexico, USA. As a part of this program, a steel containment vessel model and contact structure assembly was tested to failure with over pressurization at Sandia on December 11--12, 1996. The steel containment vessel model was a mixed-scale model (1:10 in geometry and 1:4 in shell thickness) of a steel containment for an improved Mark-II Boiling Water Reactor plant in Japan. The contact structure, which is a thick, bell-shaped steel shell separated at a nominally uniform distance from the model, provides a simplified representation of features of the concrete reactor shield building in the actual plant. The objective of the internal pressurization test was to provide measurement data of the structural response of the model up to its failure in order to validate analytical modeling, to find its pressure capacity, and to observe the failure model and mechanisms

  16. Single living predicts a higher mortality in both women and men with chronic heart failure

    DEFF Research Database (Denmark)

    Mard, Shan; Nielsen, Finn Erland

    2016-01-01

    INTRODUCTION: We examined the impact of single living on all-cause mortality in patients with chronic heart failure and determined if this association was modified by gender. METHODS: This historical cohort study included 637 patients who were admitted to the Department of Cardiology, Herlev Hosp......, the risk of death did not differ among single-living women and men. CONCLUSION: Single living is a prognostic determinant of all-cause mortality in men and women with chronic heart failure. FUNDING: none. TRIAL REGISTRATION: not relevant.......INTRODUCTION: We examined the impact of single living on all-cause mortality in patients with chronic heart failure and determined if this association was modified by gender. METHODS: This historical cohort study included 637 patients who were admitted to the Department of Cardiology, Herlev...... for confounding factors. RESULTS: The median follow-up time was 2.8 years. A total of 323 (50.7%) patients died during the follow-up period. After adjustment for confounding factors, risk of death was associated with being single (HR = 1.53 (95% confidence interval: 1.19-1.96)). In a gender-stratified analysis...

  17. Which nerve conduction parameters can predict spontaneous electromyographic activity in carpal tunnel syndrome?

    Science.gov (United States)

    Chang, Chia-Wei; Lee, Wei-Ju; Liao, Yi-Chu; Chang, Ming-Hong

    2013-11-01

    We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP amplitude are less associated with spontaneous activity, implying that needle EMG examination should be recommended for the detection of

  18. Assessment of blood gas parameters and the degree of inflammation in noninvasive positive pressure ventilation combined with aminophylline treatment of COPD complicated with type II respiratory failure

    Directory of Open Access Journals (Sweden)

    Jin-Ru Zhang

    2016-10-01

    Full Text Available Objective: To analyze the effect of noninvasive positive pressure ventilation combined with aminophylline therapy on blood gas parameters and the degree of inflammation in patients with COPD and type II respiratory failure. Methods: A total of 80 patients with COPD and type Ⅱ respiratory failure were randomly divided into observation group and control group (n=40, control group received symptomatic treatment + aminophylline treatment, observation group received symptomatic treatment + aminophylline + noninvasive positive pressure ventilation treatment, and then differences in blood gas parameters, pulmonary function parameters, hemorheology parameters and inflammatory factor levels were compared between two groups of patients after treatment. Results: Radial artery pH and PO2 values of observation group after treatment were higher than those of control group while PCO2, Cl- and CO2CP values were lower than those of control group; pulmonary function parameters FVC, FEV1, FEF25-75, MMF, PEF and FRC values of observation group after treatment were higher than those of control group; whole blood viscosity (150 s- and 10 s-, plasma viscosity, fibrinogen, erythrocyte aggregation index and erythrocyte rigidity index values in peripheral venous blood of observation group after treatment were lower than those of control group; serum IL-17, IL-33, TREM-1, sICAM-1 and PGE2 levels of observation group after treatment were lower than those of control group. Conclusion: Noninvasive positive pressure ventilation combined with aminophylline can optimize the respiratory function of patients with COPD and type II respiratory failure and improve blood gas parameters and the degree of inflammation.

  19. Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

    Science.gov (United States)

    Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

    2016-03-01

    This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

  20. Prediction and optimization of friction welding parameters for super duplex stainless steel (UNS S32760) joints

    International Nuclear Information System (INIS)

    Udayakumar, T.; Raja, K.; Afsal Husain, T.M.; Sathiya, P.

    2014-01-01

    Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

  1. Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    Mata-Cabrera Francisco

    2013-10-01

    Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

  2. Genetic Algorithms for Estimating Effective Parameters in a Lumped Reactor Model for Reactivity Predictions

    International Nuclear Information System (INIS)

    Marseguerra, Marzio; Zio, Enrico

    2001-01-01

    The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account

  3. An Adaptive Medium Access Parameter Prediction Scheme for IEEE 802.11 Real-Time Applications

    Directory of Open Access Journals (Sweden)

    Estefanía Coronado

    2017-01-01

    Full Text Available Multimedia communications have experienced an unprecedented growth due mainly to the increase in the content quality and the emergence of smart devices. The demand for these contents is tending towards wireless technologies. However, these transmissions are quite sensitive to network delays. Therefore, ensuring an optimum QoS level becomes of great importance. The IEEE 802.11e amendment was released to address the lack of QoS capabilities in the original IEEE 802.11 standard. Accordingly, the Enhanced Distributed Channel Access (EDCA function was introduced, allowing it to differentiate traffic streams through a group of Medium Access Control (MAC parameters. Although EDCA recommends a default configuration for these parameters, it has been proved that it is not optimum in many scenarios. In this work a dynamic prediction scheme for these parameters is presented. This approach ensures an appropriate traffic differentiation while maintaining compatibility with the stations without QoS support. As the APs are the only devices that use this algorithm, no changes are required to current network cards. The results show improvements in both voice and video transmissions, as well as in the QoS level of the network that the proposal achieves with regard to EDCA.

  4. Preoperative Biometric Parameters Predict the Vault after ICL Implantation: A Retrospective Clinical Study.

    Science.gov (United States)

    Zheng, Qian-Yin; Xu, Wen; Liang, Guan-Lu; Wu, Jing; Shi, Jun-Ting

    2016-01-01

    To investigate the correlation between the preoperative biometric parameters of the anterior segment and the vault after implantable Collamer lens (ICL) implantation via this retrospective study. Retrospective clinical study. A total of 78 eyes from 41 patients who underwent ICL implantation surgery were included in this study. Preoperative biometric parameters, including white-to-white (WTW) diameter, central corneal thickness, keratometer, pupil diameter, anterior chamber depth, sulcus-to-sulcus diameter, anterior chamber area (ACA) and central curvature radius of the anterior surface of the lens (Lenscur), were measured. Lenscur and ACA were measured with Rhinoceros 5.0 software on the image scanned with ultrasound biomicroscopy (UBM). The vault was assessed by UBM 3 months after surgery. Multiple stepwise regression analysis was employed to identify the variables that were correlated with the vault. The results showed that the vault was correlated with 3 variables: ACA (22.4 ± 4.25 mm2), WTW (11.36 ± 0.29 mm) and Lenscur (9.15 ± 1.21 mm). The regressive equation was: vault (mm) = 1.785 + 0.017 × ACA + 0.051 × Lenscur - 0.203 × WTW. Biometric parameters of the anterior segment (ACA, WTW and Lenscur) can predict the vault after ICL implantation using a new regression equation. © 2016 The Author(s) Published by S. Karger AG, Basel.

  5. The prediction of failure situations using the CTOD concept based on the engineering treatment model (ETM)

    International Nuclear Information System (INIS)

    Schwalbe, K.H.

    1985-01-01

    Under the assumption of plane stress conditions, the CTOD concept is extended such that a full R-curve methodology arises consisting of an experimental procedure for the determination of the R-curve and of the driving force prediction. Predictions of initiation and maximum loads are in reasonable agreement with experimental results. (orig./HP)

  6. The Effect of Process and Model Parameters in Temperature Prediction for Hot Stamping of Boron Steel

    Directory of Open Access Journals (Sweden)

    Chaoyang Sun

    2013-01-01

    Full Text Available Finite element models of the hot stamping and cold die quenching process for boron steel sheet were developed using either rigid or elastic tools. The effect of tool elasticity and process parameters on workpiece temperature was investigated. Heat transfer coefficient between blank and tools was modelled as a function of gap and contact pressure. Temperature distribution and thermal history in the blank were predicted, and thickness distribution of the blank was obtained. Tests were carried out and the test results are used for the validation of numerical predictions. The effect of holding load and the size of cooling ducts on temperature distribution during the forming and the cool die quenching process was also studied by using two models. The results show that higher accuracy predictions of blank thickness and temperature distribution during deformation were obtained using the elastic tool model. However, temperature results obtained using the rigid tool model were close to those using the elastic tool model for a range of holding load.

  7. A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

    International Nuclear Information System (INIS)

    Baumann, Brian C.; Guzzo, Thomas J.; He Jiwei; Keefe, Stephen M.; Tucker, Kai; Bekelman, Justin E.; Hwang, Wei-Ting; Vaughn, David J.; Malkowicz, S. Bruce; Christodouleas, John P.

    2013-01-01

    Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed after RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage ≥pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage ≥pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk (≤pT2), intermediate-risk (≥pT3 and ≥10 nodes removed), and high-risk (≥pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively common in

  8. A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

    Energy Technology Data Exchange (ETDEWEB)

    Baumann, Brian C. [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Guzzo, Thomas J. [Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); He Jiwei [Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Keefe, Stephen M. [Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Tucker, Kai; Bekelman, Justin E. [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Hwang, Wei-Ting [Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Vaughn, David J. [Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Malkowicz, S. Bruce [Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Christodouleas, John P., E-mail: christojo@uphs.upenn.edu [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States)

    2013-01-01

    Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed after RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage {>=}pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage {>=}pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk ({<=}pT2), intermediate-risk ({>=}pT3 and {>=}10 nodes removed), and high-risk ({>=}pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively common

  9. Positive predictive value and impact of misdiagnosis of a heart failure diagnosis in administrative registers among patients admitted to a University Hospital cardiac care unit

    DEFF Research Database (Denmark)

    Mard, Shan; Nielsen, Finn Erland

    2010-01-01

    To evaluate the positive predictive value (PPV) of a diagnosis of heart failure (HF) in the Danish National Registry of Patients (NRP) among patients admitted to a University Hospital cardiac care unit, and to evaluate the impact of misdiagnosing HF.......To evaluate the positive predictive value (PPV) of a diagnosis of heart failure (HF) in the Danish National Registry of Patients (NRP) among patients admitted to a University Hospital cardiac care unit, and to evaluate the impact of misdiagnosing HF....

  10. Search for HRV-parameters that detect a sympathetic shift in heart failure patients on beta-blocker treatment

    NARCIS (Netherlands)

    Zhang, Yanru; de Peuter, Olav R.; Kamphuisen, Pieter W.; Karemaker, John M.

    2013-01-01

    Background: A sympathetic shift in heart rate variability (HRV) from high to lower frequencies may be an early signal of deterioration in a monitored patient. Most chronic heart failure (CHF) patients receive (3-blockers. This tends to obscure HRV observation by increasing the fast variations. We

  11. Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

    Directory of Open Access Journals (Sweden)

    Simon van Mourik

    2014-06-01

    Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

  12. Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

    Science.gov (United States)

    Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

    2010-01-01

    Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in

  13. Dynamic Failure Properties of the Porcine Medial Collateral Ligament-Bone Complex for Predicting Injury in Automotive Collisions

    Science.gov (United States)

    Peck, Louis; Billiar, Kristen; Ray, Malcolm

    2010-01-01

    The goal of this study was to model the dynamic failure properties of ligaments and their attachment sites to facilitate the development of more realistic dynamic finite element models of the human lower extremities for use in automotive collision simulations. Porcine medial collateral ligaments were chosen as a test model due to their similarities in size and geometry with human ligaments. Each porcine medial collateral ligament-bone complex (n = 12) was held in a custom test fixture placed in a drop tower to apply an axial impulsive impact load, applying strain rates ranging from 0.005 s-1 to 145 s-1. The data from the impact tests were analyzed using nonlinear regression to construct model equations for predicting the failure load of ligament-bone complexes subjected to specific strain rates as calculated from finite element knee, thigh, and hip impact simulations. The majority of the ligaments tested failed by tibial avulsion (75%) while the remaining ligaments failed via mid-substance tearing. The failure load ranged from 384 N to 1184 N and was found to increase with the applied strain rate and the product of ligament length and cross-sectional area. The findings of this study indicate the force required to rupture the porcine MCL increases with the applied bone-to-bone strain rate in the range expected from high speed frontal automotive collisions. PMID:20461229

  14. Predictive factors for functional outcome and failure in angular stable osteosynthesis of the proximal humerus.

    LENUS (Irish Health Repository)

    Hardeman, Francois

    2012-02-01

    Angular stable osteosynthesis has become the gold standard in the operative treatment of proximal humeral fractures. The aim of this article is to determine the indications for osteosynthesis versus primary arthroplasty based on clinical and radiological parameters.

  15. Toe Pressures are Superior to Duplex Parameters in Predicting Wound Healing following Toe and Foot Amputations.

    Science.gov (United States)

    Stone, Patrick A; Glomski, Alexis; Thompson, Stephanie N; Adams, Elliott

    2018-01-01

    No criteria, including preamputation vascular diagnostic thresholds, have been established to reliably predict healing versus nonhealing following minor lower extremity amputations. Thus, the goal of our study was to identify clinical factors, including noninvasive vascular laboratory measures, associated with wound healing following toe, forefoot, and midfoot amputations. We retrospectively examined records of patients receiving elective toe, forefoot, or midfoot amputation at our institution over a 5-year span (2010-2015). A total of 333 amputations received noninvasive vascular assessment of the lower extremity preamputation and follow-up at 90 days postamputation. Multivariate binomial logistic regression was used to identify variables predicting wound healing as defined as the absence of reamputation due to wound breakdown. Wound healing occurred in 81% of amputations. A total of 23 (7%) patients required revisions of the foot while 39 (12%) patients required major amputations by 9