WorldWideScience

Sample records for failure predictive models

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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

  7. 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).

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  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. 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.

  14. 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....

  15. 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.

  16. 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

  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. 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

  19. 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

  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. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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...

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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)

  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. 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.

  16. 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

  17. 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.

  18. 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

  19. 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

    Together with the need to use more appropriate mathematical expressions for describing hydro-mechanical soil processes, a challenge issue relates to the need of considering the effects induced by terrain heterogeneities on the physical mechanisms, taking into account the implications of the heterogeneities in affecting time-dependent hydro-mechanical variables, would improve the prediction capacities of models, such as the ones used in early warning systems. The presence of the heterogeneities in partially-saturated slopes results in irregular propagation of the moisture and suction front. To mathematically represent the "dual-implication" generally induced by the heterogeneities in describing the hydraulic terrain behavior, several bimodal hydraulic models have been presented in literature and replaced the conventional sigmoidal/unimodal functions; this presupposes that the scale of the macrostructure is comparable with the local scale (Darcy scale), thus the Richards' model can be assumed adequate to mathematically reproduce the processes. The purpose of this work is to focus on the differences in simulating flow infiltration processes and slope stability conditions originated from preliminary choices of hydraulic models and contextually between different approaches to evaluate the factor of safety (FoS). In particular, the results of two approaches are compared. The first one includes the conventional expression of the FoS under saturated conditions and the widespread used hydraulic model of van Genuchten-Mualem. The second approach includes a generalized FoS equation for infinite-slope model under variably saturated soil conditions (Lu and Godt, 2008) and the bimodal Romano et al.'s (2011) functions to describe the hydraulic response. The extension of the above mentioned approach to the bimodal context is based on an analytical method to assess the effects of the hydraulic properties on soil shear developed integrating a bimodal lognormal hydraulic function

  20. 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.

  1. 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.

  2. 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...

  3. 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...

  4. The failure of earthquake failure models

    Science.gov (United States)

    Gomberg, J.

    2001-01-01

    In this study I show that simple heuristic models and numerical calculations suggest that an entire class of commonly invoked models of earthquake failure processes cannot explain triggering of seismicity by transient or "dynamic" stress changes, such as stress changes associated with passing seismic waves. The models of this class have the common feature that the physical property characterizing failure increases at an accelerating rate when a fault is loaded (stressed) at a constant rate. Examples include models that invoke rate state friction or subcritical crack growth, in which the properties characterizing failure are slip or crack length, respectively. Failure occurs when the rate at which these grow accelerates to values exceeding some critical threshold. These accelerating failure models do not predict the finite durations of dynamically triggered earthquake sequences (e.g., at aftershock or remote distances). Some of the failure models belonging to this class have been used to explain static stress triggering of aftershocks. This may imply that the physical processes underlying dynamic triggering differs or that currently applied models of static triggering require modification. If the former is the case, we might appeal to physical mechanisms relying on oscillatory deformations such as compaction of saturated fault gouge leading to pore pressure increase, or cyclic fatigue. However, if dynamic and static triggering mechanisms differ, one still needs to ask why static triggering models that neglect these dynamic mechanisms appear to explain many observations. If the static and dynamic triggering mechanisms are the same, perhaps assumptions about accelerating failure and/or that triggering advances the failure times of a population of inevitable earthquakes are incorrect.

  5. 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…

  6. 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....

  7. 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.

  8. 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.

  9. 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.

  10. Prediction of the fuel failure following a large LOCA using modified gap heat transfer model

    International Nuclear Information System (INIS)

    Lee, K.M.; Lee, N.H.; Huh, J.Y.; Seo, S.K.; Choi, J.H.

    1995-01-01

    The modified Ross and Stoute gap heat transfer model in the ELOCA.Mk5 code for CANDU safety analysis is based on a simplified thermal deformation model. A review on a series of recent experiments reveals that fuel pellets crack, relocate, and are eccentrically positioned within the sheath rather than solid concentric cylinders. In this study, more realistic offset crap conductance model is implemented in the code to estimate the fuel failure thresholds usincr the transient conditions of a 100% Reactor Outlet Header (ROH) break LOCA. Based on the offset gap conductance model, the total release of I-131 from the failed fuel elements in the core is reduced from 3876 TBq to 3283 TBq to increase margin for dose limit. (author)

  11. 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

  12. 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 ...

  13. 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.

  14. Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

    Science.gov (United States)

    Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

    2016-04-01

    Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR

  15. 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)

  16. 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

  17. 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.

  18. Thermal comfort in residential buildings - Failure to predict by Standard model

    Energy Technology Data Exchange (ETDEWEB)

    Becker, R. [Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Rabin Building, Technion City, Haifa 32000 (Israel); Paciuk, M. [National Building Research Institute, Technion - IIT, Haifa 32000 (Israel)

    2009-05-15

    A field study, conducted in 189 dwellings in winter and 205 dwellings in summer, included measurement of hygro-thermal conditions and documentation of occupant responses and behavior patterns. Both samples included both passive and actively space-conditioned dwellings. Predicted mean votes (PMV) computed using Fanger's model yielded significantly lower-than-reported thermal sensation (TS) values, especially for the winter heated and summer air-conditioned groups. The basic model assumption of a proportional relationship between thermal response and thermal load proved to be inadequate, with actual thermal comfort achieved at substantially lower loads than predicted. Survey results also refuted the model's second assumption that symmetrical responses in the negative and positive directions of the scale represent similar comfort levels. Results showed that the model's curve of predicted percentage of dissatisfied (PPD) substantially overestimated the actual percentage of dissatisfied within the partial group of respondents who voted TS > 0 in winter as well as within the partial group of respondents who voted TS < 0 in summer. Analyses of sensitivity to possible survey-related inaccuracy factors (metabolic rate, clothing thermal resistance) did not explain the systematic discrepancies. These discrepancies highlight the role of contextual variables (local climate, expectations, available control) in thermal adaptation in actual settings. Collected data was analyzed statistically to establish baseline data for local standardized thermal and energy calculations. A 90% satisfaction criterion yielded 19.5 C and 26 C as limit values for passive winter and summer design conditions, respectively, while during active conditioning periods, set-point temperatures of 21.5 C and 23 C should be assumed for winter and summer, respectively. (author)

  19. Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores.

    Science.gov (United States)

    Houthooft, Rein; Ruyssinck, Joeri; van der Herten, Joachim; Stijven, Sean; Couckuyt, Ivo; Gadeyne, Bram; Ongenae, Femke; Colpaert, Kirsten; Decruyenaere, Johan; Dhaene, Tom; De Turck, Filip

    2015-03-01

    The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available. Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for ICU patient admission. Our goal is to develop and optimize models for patient survival and ICU length of stay (LOS) based on monitored ICU patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features. Different machine learning techniques are trained, using a 14,480 patient dataset, both on SOFA scores as well as their underlying raw data values from the first five days after admission, in order to predict (i) the patient LOS, and (ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models. For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with GA,D=65.9% (area under the curve (AUC) of 0.77) and GS,L=73.2% (AUC of 0.82). In terms of LOS regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay. Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support

  20. 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)

  1. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  2. 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

  3. Ductile failure modeling

    DEFF Research Database (Denmark)

    Benzerga, Ahmed Amine; Leblond, Jean Baptiste; Needleman, Alan

    2016-01-01

    Ductile fracture of structural metals occurs mainly by the nucleation, growth and coalescence of voids. Here an overview of continuum models for this type of failure is given. The most widely used current framework is described and its limitations discussed. Much work has focused on extending void...... growth models to account for non-spherical initial void shapes and for shape changes during growth. This includes cases of very low stress triaxiality, where the voids can close up to micro-cracks during the failure process. The void growth models have also been extended to consider the effect of plastic...... anisotropy, or the influence of nonlocal effects that bring a material size scale into the models. Often the voids are not present in the material from the beginning, and realistic nucleation models are important. The final failure process by coalescence of neighboring voids is an issue that has been given...

  4. Development of container failure models

    International Nuclear Information System (INIS)

    Garisto, N.C.

    1990-01-01

    In order to produce a complete performance assessment for a Canadian waste vault some prediction of container failure times is required. Data are limited; however, the effects of various possible failure scenarios on the rest of the vault model can be tested. For titanium and copper, the two materials considered in the Canadian program, data are available on the frequency of failures due to manufacturing defects; there is also an estimate on the expected size of such defects. It can be shown that the consequences of such small defects in terms of the dose to humans are acceptable. It is not clear, from a modelling point of view, whether titanium or copper are preferable

  5. 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...

  6. 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.

  7. Ductile shear failure or plug failure of spot welds modelled by modified Gurson model

    DEFF Research Database (Denmark)

    Nielsen, Kim Lau; Tvergaard, Viggo

    2010-01-01

    For resistance spot welded shear-lab specimens, interfacial failure under ductile shearing or ductile plug failure are analyzed numerically, using a shear modified Gurson model. The interfacial shear failure occurs under very low stress triaxiality, where the original Gurson model would predict...

  8. 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

  9. 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.

  10. 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

  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. Acute Pancreatitis as a Model to Predict Transition of Systemic Inflammation to Organ Failure in Trauma and Critical Illness

    Science.gov (United States)

    2017-10-01

    models ); • clinical interventions; • new business creation; and • other. Nothing to report. Nothing to report. Nothing to report. 17...AWARD NUMBER: W81XWH-14-1-0376 TITLE: Acute Pancreatitis as a Model to Predict Transition of Systemic Inflammation to Organ Failgure in Trauma...COVERED 22 Sep 2016 - 21 Sep 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Acute Pancreatitis as a Model to Predict Transition of Systemic

  13. 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

  14. 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.)

  15. Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

    Science.gov (United States)

    Revell, A D; Wang, D; Wood, R; Morrow, C; Tempelman, H; Hamers, R L; Alvarez-Uria, G; Streinu-Cercel, A; Ene, L; Wensing, A M J; DeWolf, F; Nelson, M; Montaner, J S; Lane, H C; Larder, B A

    2013-06-01

    Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure. The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania. We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

  16. Assessment of predictive models for the failure of titanium and ferrous alloys due to hydrogen effects. Report for the period of June 16 to September 15, 1981

    International Nuclear Information System (INIS)

    Archbold, T.F.; Bower, R.B.; Polonis, D.H.

    1982-04-01

    The 1977 version of the Simpson-Puls-Dutton model appears to be the most amenable with respect to utilizing known or readily estimated quantities. The Pardee-Paton model requires extensive calculations involving estimated quantities. Recent observations by Koike and Suzuki on vanadium support the general assumption that crack growth in hydride forming metals is determined by the rate of hydride formation, and their hydrogen atmosphere-displacive transformation model is of potential interest in explaining hydrogen embrittlement in ferrous alloys as well as hydride formers. The discontinuous nature of cracking due to hydrogen embrittlement appears to depend very strongly on localized stress intensities, thereby pointing to the role of microstructure in influencing crack initiation, fracture mode and crack path. The initiation of hydrogen induced failures over relatively short periods of time can be characterized with fair reliability using measurements of the threshold stress intensity. The experimental conditions for determining K/sub Th/ and ΔK/sub Th/ are designed to ensure plane strain conditions in most cases. Plane strain test conditions may be viewed as a conservative basis for predicting delayed failure. The physical configuration of nuclear waste canisters may involve elastic/plastic conditions rather than a state of plane strain, especially with thin-walled vessels. Under these conditions, alternative predictive tests may be considered, including COD and R-curve methods. The double cantilever beam technique employed by Boyer and Spurr on titanium alloys offers advantages for examining hydrogen induced delayed failure over long periods of time. 88 references

  17. 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.

  18. 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.

  19. Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach

    Directory of Open Access Journals (Sweden)

    Jiajie Fan

    2017-07-01

    Full Text Available With the expanding application of light-emitting diodes (LEDs, the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD, defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED’s optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1 the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2 the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs, and color rendering indexes (CRIs of phosphor-converted (pc-white LEDs, and also can estimate the residual color life.

  20. Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach.

    Science.gov (United States)

    Fan, Jiajie; Mohamed, Moumouni Guero; Qian, Cheng; Fan, Xuejun; Zhang, Guoqi; Pecht, Michael

    2017-07-18

    With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED's optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.

  1. Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

    Science.gov (United States)

    Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

    2015-08-01

    The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. © 2015 Society for Conservation Biology.

  2. 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.

  3. 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

  4. An analytical model for interactive failures

    International Nuclear Information System (INIS)

    Sun Yong; Ma Lin; Mathew, Joseph; Zhang Sheng

    2006-01-01

    In some systems, failures of certain components can interact with each other, and accelerate the failure rates of these components. These failures are defined as interactive failure. Interactive failure is a prevalent cause of failure associated with complex systems, particularly in mechanical systems. The failure risk of an asset will be underestimated if the interactive effect is ignored. When failure risk is assessed, interactive failures of an asset need to be considered. However, the literature is silent on previous research work in this field. This paper introduces the concepts of interactive failure, develops an analytical model to analyse this type of failure quantitatively, and verifies the model using case studies and experiments

  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. 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

  7. Failure probabilistic model of CNC lathes

    International Nuclear Information System (INIS)

    Wang Yiqiang; Jia Yazhou; Yu Junyi; Zheng Yuhua; Yi Shangfeng

    1999-01-01

    A field failure analysis of computerized numerical control (CNC) lathes is described. Field failure data was collected over a period of two years on approximately 80 CNC lathes. A coding system to code failure data was devised and a failure analysis data bank of CNC lathes was established. The failure position and subsystem, failure mode and cause were analyzed to indicate the weak subsystem of a CNC lathe. Also, failure probabilistic model of CNC lathes was analyzed by fuzzy multicriteria comprehensive evaluation

  8. 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.

  9. 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.

  10. A simple approach to modeling ductile failure.

    Energy Technology Data Exchange (ETDEWEB)

    Wellman, Gerald William

    2012-06-01

    Sandia National Laboratories has the need to predict the behavior of structures after the occurrence of an initial failure. In some cases determining the extent of failure, beyond initiation, is required, while in a few cases the initial failure is a design feature used to tailor the subsequent load paths. In either case, the ability to numerically simulate the initiation and propagation of failures is a highly desired capability. This document describes one approach to the simulation of failure initiation and propagation.

  11. Reliability model for common mode failures in redundant safety systems

    International Nuclear Information System (INIS)

    Fleming, K.N.

    1974-12-01

    A method is presented for computing the reliability of redundant safety systems, considering both independent and common mode type failures. The model developed for the computation is a simple extension of classical reliability theory. The feasibility of the method is demonstrated with the use of an example. The probability of failure of a typical diesel-generator emergency power system is computed based on data obtained from U. S. diesel-generator operating experience. The results are compared with reliability predictions based on the assumption that all failures are independent. The comparison shows a significant increase in the probability of redundant system failure, when common failure modes are considered. (U.S.)

  12. [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.

  13. 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

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

    NARCIS (Netherlands)

    Revell, A. D.; Wang, D.; Wood, R.; Morrow, C.; Tempelman, H.; Hamers, R. L.; Alvarez-Uria, G.; Streinu-Cercel, A.; Ene, L.; Wensing, A. M. J.; DeWolf, F.; Nelson, M.; Montaner, J. S.; Lane, H. C.; Larder, B. A.

    2013-01-01

    Genotypic HIV drug-resistance testing is typically 6065 predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART

  19. 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

  20. Generic Sensor Failure Modeling for Cooperative Systems

    Science.gov (United States)

    Jäger, Georg; Zug, Sebastian

    2018-01-01

    The advent of cooperative systems entails a dynamic composition of their components. As this contrasts current, statically composed systems, new approaches for maintaining their safety are required. In that endeavor, we propose an integration step that evaluates the failure model of shared information in relation to an application’s fault tolerance and thereby promises maintainability of such system’s safety. However, it also poses new requirements on failure models, which are not fulfilled by state-of-the-art approaches. Consequently, this work presents a mathematically defined generic failure model as well as a processing chain for automatically extracting such failure models from empirical data. By examining data of an Sharp GP2D12 distance sensor, we show that the generic failure model not only fulfills the predefined requirements, but also models failure characteristics appropriately when compared to traditional techniques. PMID:29558435

  1. Computational Models of Rock Failure

    Science.gov (United States)

    May, Dave A.; Spiegelman, Marc

    2017-04-01

    Practitioners in computational geodynamics, as per many other branches of applied science, typically do not analyse the underlying PDE's being solved in order to establish the existence or uniqueness of solutions. Rather, such proofs are left to the mathematicians, and all too frequently these results lag far behind (in time) the applied research being conducted, are often unintelligible to the non-specialist, are buried in journals applied scientists simply do not read, or simply have not been proven. As practitioners, we are by definition pragmatic. Thus, rather than first analysing our PDE's, we first attempt to find approximate solutions by throwing all our computational methods and machinery at the given problem and hoping for the best. Typically this approach leads to a satisfactory outcome. Usually it is only if the numerical solutions "look odd" that we start delving deeper into the math. In this presentation I summarise our findings in relation to using pressure dependent (Drucker-Prager type) flow laws in a simplified model of continental extension in which the material is assumed to be an incompressible, highly viscous fluid. Such assumptions represent the current mainstream adopted in computational studies of mantle and lithosphere deformation within our community. In short, we conclude that for the parameter range of cohesion and friction angle relevant to studying rocks, the incompressibility constraint combined with a Drucker-Prager flow law can result in problems which have no solution. This is proven by a 1D analytic model and convincingly demonstrated by 2D numerical simulations. To date, we do not have a robust "fix" for this fundamental problem. The intent of this submission is to highlight the importance of simple analytic models, highlight some of the dangers / risks of interpreting numerical solutions without understanding the properties of the PDE we solved, and lastly to stimulate discussions to develop an improved computational model of

  2. 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

  3. 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.

  4. 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.

  5. Strong exploration of a cast iron pipe failure model

    International Nuclear Information System (INIS)

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

    2008-01-01

    A physical probabilistic failure model for buried cast iron pipes is described, which is based on the fracture mechanics of the pipe failure process. Such a model is useful in the asset management of buried pipelines. The model is then applied within a Monte-Carlo simulation framework after adding stochasticity to input variables. Historical failure rates are calculated based on a database of 81,595 pipes and their recorded failures, and model parameters are chosen to provide the best fit between historical and predicted failure rates. This provides an estimated corrosion rate distribution, which agrees well with experimental results. The first model design was chosen in a deliberate simplistic fashion in order to allow for further strong exploration of model assumptions. Therefore, first runs of the initial model resulted in a poor quantitative and qualitative fit in regards to failure rates. However, by exploring natural additional assumptions such as relating to stochastic loads, a number of assumptions were chosen which improved the model to a stage where an acceptable fit was achieved. The model bridges the gap between micro- and macro-level, and this is the novelty in the approach. In this model, data can be used both from the macro-level in terms of failure rates, as well as from the micro-level such as in terms of corrosion rates

  6. Validation of the Seattle Heart Failure Model (SHFM) in Heart Failure Population

    International Nuclear Information System (INIS)

    Hussain, S.; Kayani, A.M.; Munir, R.

    2014-01-01

    Objective: To determine the effectiveness of Seattle Heart Failure Model (SHFM) in a Pakistani systolic heart failure cohort in predicting mortality in this population. Study Design: Cohort study. Place and Duration of Study: The Armed Forces Institute of Cardiology - National Institute of Heart Diseases, Rawalpindi, from March 2011 to March 2012. Methodology: One hundred and eighteen patients with heart failure (HF) from the registry were followed for one year. Their 1-year mortality was calculated using the SHFM software on their enrollment into the registry. After 1-year predicted 1-year mortality was compared with the actual 1-year mortality of these patients. Results: The mean age was 41.6 +- 14.9 years (16 - 78 years). There were 73.7% males and 26.3% females. One hundred and fifteen patients were in NYHA class III or IV. Mean ejection fraction in these patients was 23 +- 9.3%. Mean brain natriuretic peptide levels were 1230 A+- 1214 pg/mL. Sensitivity of the model was 89.3% with 71.1% specificity, 49% positive predictive value and 95.5% negative predictive value. The accuracy of the model was 75.4%. In Roc analysis, AUC for the SHFM was 0.802 (p<0.001). conclusion: SHFM was found to be reliable in predicting one year mortality among patients with heart failure in the pakistan patients. (author)

  7. 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.

  8. 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.

  9. Predicting Failure Initiation in Structural Adhesive Joints

    Science.gov (United States)

    2012-08-15

    Elastoplástico de Adhesivos – Modeling, characterization and simulation of the elastoplastic behavior of adhesives. Maestría en Ciencia de Materiales...adhesive and a 1018 steel”. Maestría en Ciencia de Materiales. Centro de Investigación en Materiales Avanzados S.C. May 2012.  Abstract: In the

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  15. 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.

  16. 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.

  17. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  18. 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.

  19. Micromechanical Failure Analyses for Finite Element Polymer Modeling

    Energy Technology Data Exchange (ETDEWEB)

    CHAMBERS,ROBERT S.; REEDY JR.,EARL DAVID; LO,CHI S.; ADOLF,DOUGLAS B.; GUESS,TOMMY R.

    2000-11-01

    Polymer stresses around sharp corners and in constrained geometries of encapsulated components can generate cracks leading to system failures. Often, analysts use maximum stresses as a qualitative indicator for evaluating the strength of encapsulated component designs. Although this approach has been useful for making relative comparisons screening prospective design changes, it has not been tied quantitatively to failure. Accurate failure models are needed for analyses to predict whether encapsulated components meet life cycle requirements. With Sandia's recently developed nonlinear viscoelastic polymer models, it has been possible to examine more accurately the local stress-strain distributions in zones of likely failure initiation looking for physically based failure mechanisms and continuum metrics that correlate with the cohesive failure event. This study has identified significant differences between rubbery and glassy failure mechanisms that suggest reasonable alternatives for cohesive failure criteria and metrics. Rubbery failure seems best characterized by the mechanisms of finite extensibility and appears to correlate with maximum strain predictions. Glassy failure, however, seems driven by cavitation and correlates with the maximum hydrostatic tension. Using these metrics, two three-point bending geometries were tested and analyzed under variable loading rates, different temperatures and comparable mesh resolution (i.e., accuracy) to make quantitative failure predictions. The resulting predictions and observations agreed well suggesting the need for additional research. In a separate, additional study, the asymptotically singular stress state found at the tip of a rigid, square inclusion embedded within a thin, linear elastic disk was determined for uniform cooling. The singular stress field is characterized by a single stress intensity factor K{sub a} and the applicable K{sub a} calibration relationship has been determined for both fully bonded and

  20. Numerical investigations of rib fracture failure models in different dynamic loading conditions.

    Science.gov (United States)

    Wang, Fang; Yang, Jikuang; Miller, Karol; Li, Guibing; Joldes, Grand R; Doyle, Barry; Wittek, Adam

    2016-01-01

    Rib fracture is one of the most common thoracic injuries in vehicle traffic accidents that can result in fatalities associated with seriously injured internal organs. A failure model is critical when modelling rib fracture to predict such injuries. Different rib failure models have been proposed in prediction of thorax injuries. However, the biofidelity of the fracture failure models when varying the loading conditions and the effects of a rib fracture failure model on prediction of thoracic injuries have been studied only to a limited extent. Therefore, this study aimed to investigate the effects of three rib failure models on prediction of thoracic injuries using a previously validated finite element model of the human thorax. The performance and biofidelity of each rib failure model were first evaluated by modelling rib responses to different loading conditions in two experimental configurations: (1) the three-point bending on the specimen taken from rib and (2) the anterior-posterior dynamic loading to an entire bony part of the rib. Furthermore, the simulation of the rib failure behaviour in the frontal impact to an entire thorax was conducted at varying velocities and the effects of the failure models were analysed with respect to the severity of rib cage damages. Simulation results demonstrated that the responses of the thorax model are similar to the general trends of the rib fracture responses reported in the experimental literature. However, they also indicated that the accuracy of the rib fracture prediction using a given failure model varies for different loading conditions.

  1. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  2. 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.

  3. 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

  4. 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...

  5. 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.

  6. 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.

  7. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  8. 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

  9. 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.

  10. Failure diagnosis using discrete event models

    International Nuclear Information System (INIS)

    Sampath, M.; Sengupta, R.; Lafortune, S.; Teneketzis, D.; Sinnamohideen, K.

    1994-01-01

    We propose a Discrete Event Systems (DES) approach to the failure diagnosis problem. We present a methodology for modeling physical systems in a DES framework. We discuss the notion of diagnosability and present the construction procedure of the diagnoser. Finally, we illustrate our approach using a Heating, Ventilation and Air Conditioning (HVAC) system

  11. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  12. 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)

  13. On rate-state and Coulomb failure models

    Science.gov (United States)

    Gomberg, J.; Beeler, N.; Blanpied, M.

    2000-01-01

    We examine the predictions of Coulomb failure stress and rate-state frictional models. We study the change in failure time (clock advance) Δt due to stress step perturbations (i.e., coseismic static stress increases) added to "background" stressing at a constant rate (i.e., tectonic loading) at time t0. The predictability of Δt implies a predictable change in seismicity rate r(t)/r0, testable using earthquake catalogs, where r0 is the constant rate resulting from tectonic stressing. Models of r(t)/r0, consistent with general properties of aftershock sequences, must predict an Omori law seismicity decay rate, a sequence duration that is less than a few percent of the mainshock cycle time and a return directly to the background rate. A Coulomb model requires that a fault remains locked during loading, that failure occur instantaneously, and that Δt is independent of t0. These characteristics imply an instantaneous infinite seismicity rate increase of zero duration. Numerical calculations of r(t)/r0 for different state evolution laws show that aftershocks occur on faults extremely close to failure at the mainshock origin time, that these faults must be "Coulomb-like," and that the slip evolution law can be precluded. Real aftershock population characteristics also may constrain rate-state constitutive parameters; a may be lower than laboratory values, the stiffness may be high, and/or normal stress may be lower than lithostatic. We also compare Coulomb and rate-state models theoretically. Rate-state model fault behavior becomes more Coulomb-like as constitutive parameter a decreases relative to parameter b. This is because the slip initially decelerates, representing an initial healing of fault contacts. The deceleration is more pronounced for smaller a, more closely simulating a locked fault. Even when the rate-state Δt has Coulomb characteristics, its magnitude may differ by some constant dependent on b. In this case, a rate-state model behaves like a modified

  14. Modelling the failure modes in geobag revetments.

    Science.gov (United States)

    Akter, A; Crapper, M; Pender, G; Wright, G; Wong, W S

    2012-01-01

    In recent years, sand filled geotextile bags (geobags) have been used as a means of long-term riverbank revetment stabilization. However, despite their deployment in a significant number of locations, the failure modes of such structures are not well understood. Three interactions influence the geobag performance, i.e. geobag-geobag, geobag-water flow and geobag-water flow-river bank. The aim of the research reported here is to develop a detailed understanding of the failure mechanisms in a geobag revetment using a discrete element model (DEM) validated by laboratory data. The laboratory measured velocity data were used for preparing a mapped velocity field for a coupled DEM simulation of geobag revetment failure. The validated DEM model could identify well the critical bag location in varying water depths. Toe scour, one of the major instability factors in revetments, and its influence on the bottom-most layer of the bags were also reasonably represented in this DEM model. It is envisaged that the use of a DEM model will provide more details on geobag revetment performance in riverbanks.

  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. 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.

  18. 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.

  19. Application of a model of plastic porous materials including void shape effects to the prediction of ductile failure under shear-dominated loadings

    DEFF Research Database (Denmark)

    Morin, Léo; Leblond, Jean Baptiste; Tvergaard, Viggo

    2016-01-01

    , a numerical implementation of the model is proposed and incorporated into the SYSTUS® and ABAQUS® finite element programmes (through some freely available UMAT (Leblond, 2015) in the second case). Second, the implementation in SYSTUS® is used to simulate previous "numerical experiments" of Tvergaard...... and coworkers (Tvergaard, 2008, 2009, 2012, 2015a; Dahl et al., 2012; Nielsen et al., 2012) involving the shear loading of elementary porous cells, where softening due to changes of the void shape and orientation was very apparent. It is found that with a simple, heuristic modelling of the phenomenon...

  20. 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)

  1. 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

  2. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  3. 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.

  4. Implementation of a PETN failure model using ARIA's general chemistry framework

    Energy Technology Data Exchange (ETDEWEB)

    Hobbs, Michael L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-01-01

    We previously developed a PETN thermal decomposition model that accurately predicts thermal ignition and detonator failure [1]. This model was originally developed for CALORE [2] and required several complex user subroutines. Recently, a simplified version of the PETN decomposition model was implemented into ARIA [3] using a general chemistry framework without need for user subroutines. Detonator failure was also predicted with this new model using ENCORE. The model was simplified by 1) basing the model on moles rather than mass, 2) simplifying the thermal conductivity model, and 3) implementing ARIA’s new phase change model. This memo briefly describes the model, implementation, and validation.

  5. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  6. Pin failure modeling of the A series CABRI tests

    International Nuclear Information System (INIS)

    Young, M.F.; Portugal, J.L.

    1978-01-01

    The EXPAND pin fialure model, a research tool designed to model pin failure under prompt burst conditions, has been used to predict failure conditions for several of the A series CABRI tests as part of the United States participation in the CABRI Joint Project. The Project is an international program involving France, Germany, England, Japan, and the United States and has the goal of obtaining experimental data relating to the safety of LMFBR's. The A series, designed to simulate high ramp rate TOP conditions, initially utilizes single, fresh UO 2 pins of the PHENIX type in a flowing sodium loop. The pins are preheated at constant power in the CABRI reactor to establish steady state conditions (480 w/cm at the axial peak) and then subjected to a power pulse of 14 ms to 24 ms duration

  7. 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

  8. Enhancement of Physics-of-Failure Prognostic Models with System Level Features

    National Research Council Canada - National Science Library

    Kacprzynski, Gregory

    2002-01-01

    .... The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring...

  9. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  10. Multi-scale modeling of ductile failure in metallic alloys

    International Nuclear Information System (INIS)

    Pardoen, Th.; Scheyvaerts, F.; Simar, A.; Tekoglu, C.; Onck, P.R.

    2010-01-01

    Micro-mechanical models for ductile failure have been developed in the seventies and eighties essentially to address cracking in structural applications and complement the fracture mechanics approach. Later, this approach has become attractive for physical metallurgists interested by the prediction of failure during forming operations and as a guide for the design of more ductile and/or high-toughness microstructures. Nowadays, a realistic treatment of damage evolution in complex metallic microstructures is becoming feasible when sufficiently sophisticated constitutive laws are used within the context of a multilevel modelling strategy. The current understanding and the state of the art models for the nucleation, growth and coalescence of voids are reviewed with a focus on the underlying physics. Considerations are made about the introduction of the different length scales associated with the microstructure and damage process. Two applications of the methodology are then described to illustrate the potential of the current models. The first application concerns the competition between intergranular and transgranular ductile fracture in aluminum alloys involving soft precipitate free zones along the grain boundaries. The second application concerns the modeling of ductile failure in friction stir welded joints, a problem which also involves soft and hard zones, albeit at a larger scale. (authors)

  11. Multiscale modeling of ductile failure in metallic alloys

    Science.gov (United States)

    Pardoen, Thomas; Scheyvaerts, Florence; Simar, Aude; Tekoğlu, Cihan; Onck, Patrick R.

    2010-04-01

    Micromechanical models for ductile failure have been developed in the 1970s and 1980s essentially to address cracking in structural applications and complement the fracture mechanics approach. Later, this approach has become attractive for physical metallurgists interested by the prediction of failure during forming operations and as a guide for the design of more ductile and/or high-toughness microstructures. Nowadays, a realistic treatment of damage evolution in complex metallic microstructures is becoming feasible when sufficiently sophisticated constitutive laws are used within the context of a multilevel modelling strategy. The current understanding and the state of the art models for the nucleation, growth and coalescence of voids are reviewed with a focus on the underlying physics. Considerations are made about the introduction of the different length scales associated with the microstructure and damage process. Two applications of the methodology are then described to illustrate the potential of the current models. The first application concerns the competition between intergranular and transgranular ductile fracture in aluminum alloys involving soft precipitate free zones along the grain boundaries. The second application concerns the modeling of ductile failure in friction stir welded joints, a problem which also involves soft and hard zones, albeit at a larger scale.

  12. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  13. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  14. 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.

  15. 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.)

  16. 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

  17. 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’.

  18. 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...

  19. MODELS OF INSULIN RESISTANCE AND HEART FAILURE

    Science.gov (United States)

    Velez, Mauricio; Kohli, Smita; Sabbah, Hani N.

    2013-01-01

    The incidence of heart failure (HF) and diabetes mellitus is rapidly increasing and is associated with poor prognosis. In spite of the advances in therapy, HF remains a major health problem with high morbidity and mortality. When HF and diabetes coexist, clinical outcomes are significantly worse. The relationship between these two conditions has been studied in various experimental models. However, the mechanisms for this interrelationship are complex, incompletely understood, and have become a matter of considerable clinical and research interest. There are only few animal models that manifest both HF and diabetes. However, the translation of results from these models to human disease is limited and new models are needed to expand our current understanding of this clinical interaction. In this review, we discuss mechanisms of insulin signaling and insulin resistance, the clinical association between insulin resistance and HF and its proposed pathophysiologic mechanisms. Finally, we discuss available animal models of insulin resistance and HF and propose requirements for future new models. PMID:23456447

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. A zipper network model of the failure mechanics of extracellular matrices.

    Science.gov (United States)

    Ritter, Michael C; Jesudason, Rajiv; Majumdar, Arnab; Stamenovic, Dimitrije; Buczek-Thomas, Jo Ann; Stone, Phillip J; Nugent, Matthew A; Suki, Béla

    2009-01-27

    Mechanical failure of soft tissues is characteristic of life-threatening diseases, including capillary stress failure, pulmonary emphysema, and vessel wall aneurysms. Failure occurs when mechanical forces are sufficiently high to rupture the enzymatically weakened extracellular matrix (ECM). Elastin, an important structural ECM protein, is known to stretch beyond 200% strain before failing. However, ECM constructs and native vessel walls composed primarily of elastin and proteoglycans (PGs) have been found to fail at much lower strains. In this study, we hypothesized that PGs significantly contribute to tissue failure. To test this, we developed a zipper network model (ZNM), in which springs representing elastin are organized into long wavy fibers in a zipper-like formation and placed within a network of springs mimicking PGs. Elastin and PG springs possessed distinct mechanical and failure properties. Simulations using the ZNM showed that the failure of PGs alone reduces the global failure strain of the ECM well below that of elastin, and hence, digestion of elastin does not influence the failure strain. Network analysis suggested that whereas PGs drive the failure process and define the failure strain, elastin determines the peak and failure stresses. Predictions of the ZNM were experimentally confirmed by measuring the failure properties of engineered elastin-rich ECM constructs before and after digestion with trypsin, which cleaves the core protein of PGs without affecting elastin. This study reveals a role for PGs in the failure properties of engineered and native ECM with implications for the design of engineered tissues.

  5. 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.

  6. 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.

  7. The multi-class binomial failure rate model for the treatment of common-cause failures

    International Nuclear Information System (INIS)

    Hauptmanns, U.

    1995-01-01

    The impact of common cause failures (CCF) on PSA results for NPPs is in sharp contrast with the limited quality which can be achieved in their assessment. This is due to the dearth of observations and cannot be remedied in the short run. Therefore the methods employed for calculating failure rates should be devised such as to make the best use of the few available observations on CCF. The Multi-Class Binomial Failure Rate (MCBFR) Model achieves this by assigning observed failures to different classes according to their technical characteristics and applying the BFR formalism to each of these. The results are hence determined by a superposition of BFR type expressions for each class, each of them with its own coupling factor. The model thus obtained flexibly reproduces the dependence of CCF rates on failure multiplicity insinuated by the observed failure multiplicities. This is demonstrated by evaluating CCFs observed for combined impulse pilot valves in German NPPs. (orig.) [de

  8. [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.

  9. Review of constitutive models and failure criteria for concrete

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Jeong Moon; Choun, Young Sun [Korea Atomic Energy Research Institute, Taejeon (Korea)

    2000-03-01

    The general behavior, constitutive models, and failure criteria of concrete are reviewed. The current constitutive models for concrete cannot satisfy all of mechanical behavior of concrete. Among several constitutive models, damage models are recommended to describe properly the structural behavior of concrete containment buildings, because failure modes and post-failure behavior are important in containment buildings. A constitutive model which can describe the concrete behavior in tension is required because the containment buildings will reach failure state due to ultimate internal pressure. Therefore, a thorough study on the behavior and models under tension stress state in concrete and reinforced concrete has to be performed. There are two types of failure criteria in containment buildings: structural failure criteria and leakage failure criteria. For reinforced or prestressed concrete containment buildings, concrete cracking does not mean the structural failure of containment building because the reinforcement or post-tensioning system is able to resist tensile stress up to yield stress. Therefore leakage failure criteria will be prior to structural failure criteria, and a strain failure criterion for concrete has to be established. 120 refs., 59 figs., 1 tabs. (Author)

  10. Omnibus risk assessment via accelerated failure time kernel machine modeling.

    Science.gov (United States)

    Sinnott, Jennifer A; Cai, Tianxi

    2013-12-01

    Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

  11. 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

  12. 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.

  13. 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.

  14. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  15. Centrifuge model test of rock slope failure caused by seismic excitation. Plane failure of dip slope

    International Nuclear Information System (INIS)

    Ishimaru, Makoto; Kawai, Tadashi

    2008-01-01

    Recently, it is necessary to assess quantitatively seismic safety of critical facilities against the earthquake induced rock slope failure from the viewpoint of seismic PSA. Under these circumstances, it is essential to evaluate more accurately the possibilities of rock slope failure and the potential failure boundary, which are triggered by earthquake ground motions. The purpose of this study is to analyze dynamic failure characteristics of rock slopes by centrifuge model tests for verification and improvement of the analytical methods. We conducted a centrifuge model test using a dip slope model with discontinuities limitated by Teflon sheets. The centrifugal acceleration was 50G, and the acceleration amplitude of input sin waves increased gradually at every step. The test results were compared with safety factors of the stability analysis based on the limit equilibrium concept. Resultant conclusions are mainly as follows: (1) The slope model collapsed when it was excited by the sine wave of 400gal, which was converted to real field scale, (2) Artificial discontinuities were considerably concerned in the collapse, and the type of collapse was plane failure, (3) From response acceleration records observed at the slope model, we can say that tension cracks were generated near the top of the slope model during excitation, and that might be cause of the collapse, (4) By considering generation of the tension cracks in the stability analysis, correspondence of the analytical results and the experimental results improved. From the obtained results, we need to consider progressive failure in evaluating earthquake induced rock slope failure. (author)

  16. 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.

  17. 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.

  18. 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.

  19. Utility of the Seattle Heart Failure Model in patients with advanced heart failure.

    Science.gov (United States)

    Kalogeropoulos, Andreas P; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Smith, Andrew L; Agha, Syed A; Waheed, Sana; Laskar, Sonjoy; Puskas, John; Dunbar, Sandra; Vega, David; Levy, Wayne C; Butler, Javed

    2009-01-27

    The aim of this study was to validate the Seattle Heart Failure Model (SHFM) in patients with advanced heart failure (HF). The SHFM was developed primarily from clinical trial databases and extrapolated the benefit of interventions from published data. We evaluated the discrimination and calibration of SHFM in 445 advanced HF patients (age 52 +/- 12 years, 68.5% male, 52.4% white, ejection fraction 18 +/- 8%) referred for cardiac transplantation. The primary end point was death (n = 92), urgent transplantation (n = 14), or left ventricular assist device (LVAD) implantation (n = 3); a secondary analysis was performed on mortality alone. Patients were receiving optimal therapy (angiotensin-II modulation 92.8%, beta-blockers 91.5%, aldosterone antagonists 46.3%), and 71.0% had an implantable device (defibrillator 30.4%, biventricular pacemaker 3.4%, combined 37.3%). During a median follow-up of 21 months, 109 patients (24.5%) had an event. Although discrimination was adequate (c-statistic >0.7), the SHFM overall underestimated absolute risk (observed vs. predicted event rate: 11.0% vs. 9.2%, 21.0% vs. 16.6%, and 27.9% vs. 22.8% at 1, 2, and 3 years, respectively). Risk underprediction was more prominent in patients with an implantable device. The SHFM had different calibration properties in white versus black patients, leading to net underestimation of absolute risk in blacks. Race-specific recalibration improved the accuracy of predictions. When analysis was restricted to mortality, the SHFM exhibited better performance. In patients with advanced HF, the SHFM offers adequate discrimination, but absolute risk is underestimated, especially in blacks and in patients with devices. This is more prominent when including transplantation and LVAD implantation as an end point.

  20. A new method for explicit modelling of single failure event within different common cause failure groups

    International Nuclear Information System (INIS)

    Kančev, Duško; Čepin, Marko

    2012-01-01

    Redundancy and diversity are the main principles of the safety systems in the nuclear industry. Implementation of safety components redundancy has been acknowledged as an effective approach for assuring high levels of system reliability. The existence of redundant components, identical in most of the cases, implicates a probability of their simultaneous failure due to a shared cause—a common cause failure. This paper presents a new method for explicit modelling of single component failure event within multiple common cause failure groups simultaneously. The method is based on a modification of the frequently utilised Beta Factor parametric model. The motivation for development of this method lays in the fact that one of the most widespread softwares for fault tree and event tree modelling as part of the probabilistic safety assessment does not comprise the option for simultaneous assignment of single failure event to multiple common cause failure groups. In that sense, the proposed method can be seen as an advantage of the explicit modelling of common cause failures. A standard standby safety system is selected as a case study for application and study of the proposed methodology. The results and insights implicate improved, more transparent and more comprehensive models within probabilistic safety assessment.

  1. 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.

  2. Data analysis using the Binomial Failure Rate common cause model

    International Nuclear Information System (INIS)

    Atwood, C.L.

    1983-09-01

    This report explains how to use the Binomial Failure Rate (BFR) method to estimate common cause failure rates. The entire method is described, beginning with the conceptual model, and covering practical issues of data preparation, treatment of variation in the failure rates, Bayesian estimation of the quantities of interest, checking the model assumptions for lack of fit to the data, and the ultimate application of the answers

  3. 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)

  4. 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

  5. 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)

  6. Prediction by numerical modeling of the risk zone along a riverside exposed to a dam failure; Prevision par modelisation numerique de la zone de risque bordant un troncon de riviere subissant une rupture de barrage

    Energy Technology Data Exchange (ETDEWEB)

    Mahdi, T.F.

    2004-07-01

    The risk zone associated with the surge wave following dam failure is defined in this study using a newly developed methodology that incorporates the conventionally used maximum water levels, the sediment movement in the river bed and the possibility of bank failure. Although the risk zone is typically defined as the inundated area, extensive lateral erosion that causes landslides could also accompany the inundation. This study demonstrates that the stability of the riverbank (in terms of geotechnical considerations) can influence the delineation of the risk area. The combined disciplines of geotechnics and hydraulics can be used to follow the evolution of the riverbed and riverbanks during a flood event. This study also presents a structured methodology for the St. Venant shallow water wave equations which were used to determine the area likely to be flooded without taking into account sediment transport. It also discusses aspects of the sediment transport theory. Fluvial erosion and lateral bank failure are the basic physical processes responsible for bank retreat. The minimum energy dissipation rate theory is applied for fluvial erosion, while Bishop's modified method is used to analyze slope stability when evaluating lateral bank failures. A numerical modeling of flows over movable beds is presented along with a review of some of the available numerical models. A diagnostic phase that provides information needed to qualify the extent of the damage after a flood is also presented. Some of the numerical models to evaluate risk area were validated on a portion of the HaHa River which was affected in the 1996 Saguenay flood. The new methodology, applied to the Outaouais River at Notre Dame du Nord in Quebec, produces a risk area much greater than that obtained when only the inundated area is considered.

  7. 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

  8. Comparison of US/FRG accident condition models for HTGR fuel failure and radionuclide release

    International Nuclear Information System (INIS)

    Verfondern, K.

    1991-03-01

    The objective was to compare calculation models used in safety analyses in the US and FRG which describe fission product release behavior from TRISO coated fuel particles under core heatup accident conditions. The frist step performed is the qualitative comparison of both sides' fuel failure and release models in order to identify differences and similarities in modeling assumptions and inputs. Assumptions of possible particle failure mechanisms under accident conditions (SiC degradation, pressure vessel) are principally the same on both sides though they are used in different modeling approaches. The characterization of a standard (= intact) coated particle to be of non-releasing (GA) or possibly releasing (KFA/ISF) type is one of the major qualitative differences. Similar models are used regarding radionuclide release from exposed particle kernels. In a second step, a quantitative comparison of the calculation models was made by assessing a benchmark problem predicting particle failure and radionuclide release under MHTGR conduction cooldown accident conditions. Calculations with each side's reference method have come to almost the same failure fractions after 250 hours for the core region with maximum core heatup temperature despite the different modeling approaches of SORS and PANAMA-I. The comparison of the results of particle failure obtained with the Integrated Failure and Release Model for Standard Particles and its revision provides a 'verification' of these models in this sense that the codes (SORS and PANAMA-II, and -III, respectively) which were independently developed lead to very good agreement in the predictions. (orig./HP) [de

  9. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    African Journals Online (AJOL)

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  10. 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.

  11. Comparing risk of failure models in water supply networks using ROC curves

    International Nuclear Information System (INIS)

    Debon, A.; Carrion, A.; Cabrera, E.; Solano, H.

    2010-01-01

    The problem of predicting the failure of water mains has been considered from different perspectives and using several methodologies in engineering literature. Nowadays, it is important to be able to accurately calculate the failure probabilities of pipes over time, since water company profits and service quality for citizens depend on pipe survival; forecasting pipe failures could have important economic and social implications. Quantitative tools (such as managerial or statistical indicators and reliable databases) are required in order to assess the current and future state of networks. Companies managing these networks are trying to establish models for evaluating the risk of failure in order to develop a proactive approach to the renewal process, instead of using traditional reactive pipe substitution schemes. The main objective of this paper is to compare models for evaluating the risk of failure in water supply networks. Using real data from a water supply company, this study has identified which network characteristics affect the risk of failure and which models better fit data to predict service breakdown. The comparison using the receiver operating characteristics (ROC) graph leads us to the conclusion that the best model is a generalized linear model. Also, we propose a procedure that can be applied to a pipe failure database, allowing the most appropriate decision rule to be chosen.

  12. Comparing risk of failure models in water supply networks using ROC curves

    Energy Technology Data Exchange (ETDEWEB)

    Debon, A., E-mail: andeau@eio.upv.e [Centro de Gestion de la Calidad y del Cambio, Dpt. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica de Valencia, E-46022 Valencia (Spain); Carrion, A. [Centro de Gestion de la Calidad y del Cambio, Dpt. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica de Valencia, E-46022 Valencia (Spain); Cabrera, E. [Dpto. De Ingenieria Hidraulica Y Medio Ambiente, Instituto Tecnologico del Agua, Universidad Politecnica de Valencia, E-46022 Valencia (Spain); Solano, H. [Universidad Diego Portales, Santiago (Chile)

    2010-01-15

    The problem of predicting the failure of water mains has been considered from different perspectives and using several methodologies in engineering literature. Nowadays, it is important to be able to accurately calculate the failure probabilities of pipes over time, since water company profits and service quality for citizens depend on pipe survival; forecasting pipe failures could have important economic and social implications. Quantitative tools (such as managerial or statistical indicators and reliable databases) are required in order to assess the current and future state of networks. Companies managing these networks are trying to establish models for evaluating the risk of failure in order to develop a proactive approach to the renewal process, instead of using traditional reactive pipe substitution schemes. The main objective of this paper is to compare models for evaluating the risk of failure in water supply networks. Using real data from a water supply company, this study has identified which network characteristics affect the risk of failure and which models better fit data to predict service breakdown. The comparison using the receiver operating characteristics (ROC) graph leads us to the conclusion that the best model is a generalized linear model. Also, we propose a procedure that can be applied to a pipe failure database, allowing the most appropriate decision rule to be chosen.

  13. 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.

  14. 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

  15. Pig models for the human heart failure syndrome

    DEFF Research Database (Denmark)

    Hunter, Ingrid; Terzic, Dijana; Zois, Nora Elisabeth

    2014-01-01

    Human heart failure remains a challenging illness despite advances in the diagnosis and treatment of heart failure patients. There is a need for further improvement of our understanding of the failing myocardium and its molecular deterioration. Porcine models provide an important research tool...... in this respect as molecular changes can be examined in detail, which is simply not feasible in human patients. However, the human heart failure syndrome is based on symptoms and signs, where pig models mostly mimic the myocardial damage, but without decisive data on clinical presentation and, therefore, a heart...... to elucidate the human heart failure syndrome....

  16. 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

  17. 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 ...

  18. Enhancement of weld failure and tube ejection model in PENTAP program

    International Nuclear Information System (INIS)

    Jung, Jaehoon; An, Sang Mo; Ha, Kwang Soon; Kim, Hwan Yeol

    2014-01-01

    The reactor vessel pressure, the debris mass, the debris temperature, and the component of material can have an effect on the penetration tube failure modes. Furthermore, these parameters are interrelated. There are some representative severe accident codes such as MELCOR, MAAP, and PENTAP program. MELCOR decides on a penetration tube failure by its failure temperature such as 1273K simply. MAAP considers all penetration failure modes and has the most advanced model for a penetration tube failure model. However, the validation work against the experimental data is very limited. PENTAP program which evaluates the possible penetration tube failure modes such as creep failure, weld failure, tube ejection, and a long term tube failure under given accident condition was developed by KAERI. The experiment for the tube ejection is being performed by KAERI. The temperature distribution and the ablation rate of both weld and lower vessel wall can be obtained through the experiment. This paper includes the updated calculation steps for the weld failure and the tube ejection modes of the PENTAP program to apply the experimental results. PENTAP program can evaluate the possible penetration tube failure modes. It still requires a large amount of efforts to increase the prediction of failure modes. Some calculation steps are necessary for applying the experimental and the numerical data in the PENTAP program. In this study, new calculation steps are added to PENTAP program to enhance the weld failure and tube ejection models using KAERI's experimental data which are the ablation rate and temperature distribution of weld and lower vessel wall

  19. ARRA: Reconfiguring Power Systems to Minimize Cascading Failures - Models and Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Dobson, Ian [Iowa State University; Hiskens, Ian [Unversity of Michigan; Linderoth, Jeffrey [University of Wisconsin-Madison; Wright, Stephen [University of Wisconsin-Madison

    2013-12-16

    Building on models of electrical power systems, and on powerful mathematical techniques including optimization, model predictive control, and simluation, this project investigated important issues related to the stable operation of power grids. A topic of particular focus was cascading failures of the power grid: simulation, quantification, mitigation, and control. We also analyzed the vulnerability of networks to component failures, and the design of networks that are responsive to and robust to such failures. Numerous other related topics were investigated, including energy hubs and cascading stall of induction machines

  20. 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.

  1. Comparison of a fuel sheath failure model with published experimental data

    International Nuclear Information System (INIS)

    Varty, R.L.; Rosinger, H.E.

    1982-01-01

    A fuel sheath failure model has been compared with the published results of experiments in which a Zircaloy-4 fuel sheath was subjected to a temperature ramp and a differential pressure until failure occurred. The model assumes that the deformation of the sheath is controlled by steady-state creep and that there is a relationship between tangential stress and temperature at the instant of failure. The sheath failure model predictions agree reasonably well with the experimental data. The burst temperature is slightly overpredicted by the model. The burst strain is overpredicted for small experimental burst strains but is underpredicted otherwise. The reasons for these trends are discussed and the extremely wide variation in burst strain reported in the literature is explained using the model

  2. Predicción de fracaso en empresas latinoamericanas utilizando el método del vecino más cercano para predecir efectos aleatorios en modelos mixtos || Prediction of Failure in Latin-American Companies Using the Nearest-Neighbor Method to Predict Random Effects in Mixed Models

    Directory of Open Access Journals (Sweden)

    Caro, Norma Patricia

    2017-12-01

    Full Text Available En la presente década, en economías emergentes como las latinoamericanas, se han comenzado a aplicar modelos logísticos mixtos para predecir el fracaso financiero de las empresas. No obstante, existen limitaciones subyacentes a la metodología, vinculadas a la factibilidad de predicción del estado de nuevas empresas que no han formado parte de la muestra de entrenamiento con la que se estimó el modelo. En la literatura se han propuesto diversos métodos de predicción para los efectos aleatorios que forman parte de los modelos mixtos, entre ellos, el del vecino más cercano. Este método es aplicado en una segunda etapa, luego de la estimación de un modelo que explica la situación financiera (en crisis o sana de las empresas mediante la consideración del comportamiento de sus ratios contables. En el presente trabajo, se consideraron empresas de Argentina, Chile y Perú, estimando los efectos aleatorios que resultaron significativos en la estimación del modelo mixto. De este modo, se concluye que la aplicación de este método permite identificar empresas con problemas financieros con una tasa de clasificación correcta superior a 80%, lo cual cobra relevancia en la modelación y predicción de este tipo de riesgo. || In the present decade, in emerging economies such as those in Latin-America, mixed logistic models have been started applying to predict the financial failure of companies. However, there are limitations for the methodology linked to the feasibility of predicting the state of new companies that have not been part of the training sample which was used to estimate the model. In the literature, several methods have been proposed for predicting random effects in the mixed models such as, for example, the nearest neighbor. This method is applied in a second step, after estimating a model that explains the financial situation (in crisis or healthy of companies by considering the behavior of its financial ratios. In this study

  3. Modeling the failure data of a repairable equipment with bathtub type failure intensity

    International Nuclear Information System (INIS)

    Pulcini, G.

    2001-01-01

    The paper deals with the reliability modeling of the failure process of large and complex repairable equipment whose failure intensity shows a bathtub type non-monotonic behavior. A non-homogeneous Poisson process arising from the superposition of two power law processes is proposed, and the characteristics and mathematical details of the proposed model are illustrated. A graphical approach is also presented, which allows to determine whether the proposed model can adequately describe a given failure data. A graphical method for obtaining crude but easy estimates of the model parameters is then illustrated, as well as more accurate estimates based on the maximum likelihood method are provided. Finally, two numerical applications are given to illustrate the proposed model and the estimation procedures

  4. A probability model for the failure of pressure containing parts

    International Nuclear Information System (INIS)

    Thomas, H.M.

    1978-01-01

    The model provides a method of estimating the order of magnitude of the leakage failure probability of pressure containing parts. It is a fatigue based model which makes use of the statistics available for both specimens and vessels. Some novel concepts are introduced but essentially the model simply quantifies the obvious i.e. that failure probability increases with increases in stress levels, number of cycles, volume of material and volume of weld metal. A further model based on fracture mechanics estimates the catastrophic fraction of leakage failures. (author)

  5. 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...

  6. Continuum Damage Mechanics Models for the Analysis of Progressive Failure in Open-Hole Tension Laminates

    Science.gov (United States)

    Song, Kyonchan; Li, Yingyong; Rose, Cheryl A.

    2011-01-01

    The performance of a state-of-the-art continuum damage mechanics model for interlaminar damage, coupled with a cohesive zone model for delamination is examined for failure prediction of quasi-isotropic open-hole tension laminates. Limitations of continuum representations of intra-ply damage and the effect of mesh orientation on the analysis predictions are discussed. It is shown that accurate prediction of matrix crack paths and stress redistribution after cracking requires a mesh aligned with the fiber orientation. Based on these results, an aligned mesh is proposed for analysis of the open-hole tension specimens consisting of different meshes within the individual plies, such that the element edges are aligned with the ply fiber direction. The modeling approach is assessed by comparison of analysis predictions to experimental data for specimen configurations in which failure is dominated by complex interactions between matrix cracks and delaminations. It is shown that the different failure mechanisms observed in the tests are well predicted. In addition, the modeling approach is demonstrated to predict proper trends in the effect of scaling on strength and failure mechanisms of quasi-isotropic open-hole tension laminates.

  7. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  8. 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

  9. 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

  10. 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.

  11. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  12. 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.

  13. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  14. A Zebrafish Heart Failure Model for Assessing Therapeutic Agents.

    Science.gov (United States)

    Zhu, Xiao-Yu; Wu, Si-Qi; Guo, Sheng-Ya; Yang, Hua; Xia, Bo; Li, Ping; Li, Chun-Qi

    2018-03-20

    Heart failure is a leading cause of death and the development of effective and safe therapeutic agents for heart failure has been proven challenging. In this study, taking advantage of larval zebrafish, we developed a zebrafish heart failure model for drug screening and efficacy assessment. Zebrafish at 2 dpf (days postfertilization) were treated with verapamil at a concentration of 200 μM for 30 min, which were determined as optimum conditions for model development. Tested drugs were administered into zebrafish either by direct soaking or circulation microinjection. After treatment, zebrafish were randomly selected and subjected to either visual observation and image acquisition or record videos under a Zebralab Blood Flow System. The therapeutic effects of drugs on zebrafish heart failure were quantified by calculating the efficiency of heart dilatation, venous congestion, cardiac output, and blood flow dynamics. All 8 human heart failure therapeutic drugs (LCZ696, digoxin, irbesartan, metoprolol, qiliqiangxin capsule, enalapril, shenmai injection, and hydrochlorothiazide) showed significant preventive and therapeutic effects on zebrafish heart failure (p failure model developed and validated in this study could be used for in vivo heart failure studies and for rapid screening and efficacy assessment of preventive and therapeutic drugs.

  15. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. 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.

  17. 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.

  18. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  19. 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

  20. 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…

  1. A Thermal Runaway Failure Model for Low-Voltage BME Ceramic Capacitors with Defects

    Science.gov (United States)

    Teverovsky, Alexander

    2017-01-01

    Reliability of base metal electrode (BME) multilayer ceramic capacitors (MLCCs) that until recently were used mostly in commercial applications, have been improved substantially by using new materials and processes. Currently, the inception of intrinsic wear-out failures in high quality capacitors became much greater than the mission duration in most high-reliability applications. However, in capacitors with defects degradation processes might accelerate substantially and cause infant mortality failures. In this work, a physical model that relates the presence of defects to reduction of breakdown voltages and decreasing times to failure has been suggested. The effect of the defect size has been analyzed using a thermal runaway model of failures. Adequacy of highly accelerated life testing (HALT) to predict reliability at normal operating conditions and limitations of voltage acceleration are considered. The applicability of the model to BME capacitors with cracks is discussed and validated experimentally.

  2. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  3. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  4. 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.

  5. Software reliability growth models with normal failure time distributions

    International Nuclear Information System (INIS)

    Okamura, Hiroyuki; Dohi, Tadashi; Osaki, Shunji

    2013-01-01

    This paper proposes software reliability growth models (SRGM) where the software failure time follows a normal distribution. The proposed model is mathematically tractable and has sufficient ability of fitting to the software failure data. In particular, we consider the parameter estimation algorithm for the SRGM with normal distribution. The developed algorithm is based on an EM (expectation-maximization) algorithm and is quite simple for implementation as software application. Numerical experiment is devoted to investigating the fitting ability of the SRGMs with normal distribution through 16 types of failure time data collected in real software projects

  6. On a Stochastic Failure Model under Random Shocks

    Science.gov (United States)

    Cha, Ji Hwan

    2013-02-01

    In most conventional settings, the events caused by an external shock are initiated at the moments of its occurrence. In this paper, we study a new classes of shock model, where each shock from a nonhomogeneous Poisson processes can trigger a failure of a system not immediately, as in classical extreme shock models, but with delay of some random time. We derive the corresponding survival and failure rate functions. Furthermore, we study the limiting behaviour of the failure rate function where it is applicable.

  7. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

  8. A Bayesian network approach for modeling local failure in lung cancer

    International Nuclear Information System (INIS)

    Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam

    2011-01-01

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

  9. 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.

  10. 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...

  11. NEESROCK: A Physical and Numerical Modeling Investigation of Seismically Induced Rock-Slope Failure

    Science.gov (United States)

    Applegate, K. N.; Wartman, J.; Keefer, D. K.; Maclaughlin, M.; Adams, S.; Arnold, L.; Gibson, M.; Smith, S.

    2013-12-01

    Worldwide, seismically induced rock-slope failures have been responsible for approximately 30% of the most significant landslide catastrophes of the past century. They are among the most common, dangerous, and still today, least understood of all seismic hazards. Seismically Induced Rock-Slope Failure: Mechanisms and Prediction (NEESROCK) is a major research initiative that fully integrates physical modeling (geotechnical centrifuge) and advanced numerical simulations (discrete element modeling) to investigate the fundamental mechanisms governing the stability of rock slopes during earthquakes. The research is part of the National Science Foundation-supported Network for Earthquake Engineering Simulation Research (NEES) program. With its focus on fractures and rock materials, the project represents a significant departure from the traditional use of the geotechnical centrifuge for studying soil, and pushes the boundaries of physical modeling in new directions. In addition to advancing the fundamental understanding of the rock-slope failure process under seismic conditions, the project is developing improved rock-slope failure assessment guidelines, analysis procedures, and predictive tools. Here, we provide an overview of the project, present experimental and numerical modeling results, discuss special considerations for the use of synthetic rock materials in physical modeling, and address the suitability of discrete element modeling for simulating the dynamic rock-slope failure process.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Developing a Model for Identifying Students at Risk of Failure in a First Year Accounting Unit

    Science.gov (United States)

    Smith, Malcolm; Therry, Len; Whale, Jacqui

    2012-01-01

    This paper reports on the process involved in attempting to build a predictive model capable of identifying students at risk of failure in a first year accounting unit in an Australian university. Identifying attributes that contribute to students being at risk can lead to the development of appropriate intervention strategies and support…

  18. 3D constitutive model of anisotropic damage for unidirectional ply based on physical failure mechanisms

    DEFF Research Database (Denmark)

    Qing, Hai; Mishnaevsky, Leon

    2010-01-01

    in a computational finite element framework, which is capable of predicting initial failure, subsequent progressive damage up to final collapse. Crack band model and viscous regularization are applied to depress the convergence difficulties associated with strain softening behaviours. To verify the accuracy...

  19. 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

  20. 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

  1. Failure/leakage predictions of concrete structures containing cracks

    International Nuclear Information System (INIS)

    Pan, Y.C.; Marchertas, A.H.; Kennedy, J.M.

    1984-06-01

    An approach is presented for studying the cracking and radioactive release of a reactor containment during severe accidents and extreme environments. The cracking of concrete is modeled as the blunt crack. The initiation and propagation of a crack are determined by using the maximum strength and the J-integral criteria. Furthermore, the extent of cracking is related to the leakage calculation by using a model developed by Rizkalla, Lau and Simmonds. Numerical examples are given for a three-point bending problem and a hypothetical case of a concrete containment structure subjected to high internal pressure during an accident

  2. A Markov Model for Commen-Cause Failures

    DEFF Research Database (Denmark)

    Platz, Ole

    1984-01-01

    A continuous time four-state Markov chain is shown to cover several of the models that have been used for describing dependencies between failures of components in redundant systems. Among these are the models derived by Marshall and Olkin and by Freund and models for one-out-of-three and two...

  3. 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

  4. An interval-valued reliability model with bounded failure rates

    DEFF Research Database (Denmark)

    Kozine, Igor; Krymsky, Victor

    2012-01-01

    The approach to deriving interval-valued reliability measures described in this paper is distinctive from other imprecise reliability models in that it overcomes the issue of having to impose an upper bound on time to failure. It rests on the presupposition that a constant interval-valued failure...... rate is known possibly along with other reliability measures, precise or imprecise. The Lagrange method is used to solve the constrained optimization problem to derive new reliability measures of interest. The obtained results call for an exponential-wise approximation of failure probability density...

  5. Experimental models of hepatotoxicity related to acute liver failure

    Energy Technology Data Exchange (ETDEWEB)

    Maes, Michaël [Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels (Belgium); Vinken, Mathieu, E-mail: mvinken@vub.ac.be [Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels (Belgium); Jaeschke, Hartmut [Department of Pharmacology, Toxicology and Therapeutics, University of Kansas Medical Center, Kansas City (United States)

    2016-01-01

    Acute liver failure can be the consequence of various etiologies, with most cases arising from drug-induced hepatotoxicity in Western countries. Despite advances in this field, the management of acute liver failure continues to be one of the most challenging problems in clinical medicine. The availability of adequate experimental models is of crucial importance to provide a better understanding of this condition and to allow identification of novel drug targets, testing the efficacy of new therapeutic interventions and acting as models for assessing mechanisms of toxicity. Experimental models of hepatotoxicity related to acute liver failure rely on surgical procedures, chemical exposure or viral infection. Each of these models has a number of strengths and weaknesses. This paper specifically reviews commonly used chemical in vivo and in vitro models of hepatotoxicity associated with acute liver failure. - Highlights: • The murine APAP model is very close to what is observed in patients. • The Gal/ET model is useful to study TNFα-mediated apoptotic signaling mechanisms. • Fas receptor activation is an effective model of apoptosis and secondary necrosis. • The ConA model is a relevant model of auto-immune hepatitis and viral hepatitis. • Multiple time point evaluation needed in experimental models of acute liver injury.

  6. Financial performance evaluation and bankruptcy prediction (failure1

    Directory of Open Access Journals (Sweden)

    Talal A. Al-Kassar, Dr.

    2014-10-01

    The research also demonstrates the need to include measures of both financial and non-financial performance in the evaluation as they complement each other. Without both financial and non-financial, the evaluation process is incomplete and does not provide desired results or the correct image of the process. The research suggests including comprehensive measures of performance evaluation of projects by using indicators of adopted criteria. Thus, the application of both models leads to better results and assists users in maintaining greater objectivity while obtaining more accurate results than from analysis based on personal evaluation alone.

  7. Weibull Model Allowing Nearly Instantaneous Failures

    Directory of Open Access Journals (Sweden)

    C. D. Lai

    2007-01-01

    expressed as a mixture of the uniform distribution and the Weibull distribution. Properties of the resulting distribution are derived; in particular, the probability density function, survival function, and the hazard rate function are obtained. Some selected plots of these functions are also presented. An R script was written to fit the model parameters. An application of the modified model is illustrated.

  8. 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.

  9. 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.

  10. Matching the results of a theoretical model with failure rates obtained from a population of non-nuclear pressure vessels

    International Nuclear Information System (INIS)

    Harrop, L.P.

    1982-02-01

    Failure rates for non-nuclear pressure vessel populations are often regarded as showing a decrease with time. Empirical evidence can be cited which supports this view. On the other hand theoretical predictions of PWR type reactor pressure vessel failure rates have shown an increasing failure rate with time. It is shown that these two situations are not necessarily incompatible. If adjustments are made to the input data of the theoretical model to treat a non-nuclear pressure vessel population, the model can produce a failure rate which decreases with time. These adjustments are explained and the results obtained are shown. (author)

  11. 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.

  12. 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

  13. Cladding failure probability modeling for risk evaluations of fast reactors

    International Nuclear Information System (INIS)

    Mueller, C.J.; Kramer, J.M.

    1987-01-01

    This paper develops the methodology to incorporate cladding failure data and associated modeling into risk evaluations of liquid metal-cooled fast reactors (LMRs). Current US innovative designs for metal-fueled pool-type LMRs take advantage of inherent reactivity feedback mechanisms to limit reactor temperature increases in response to classic anticipated-transient-without-scram (ATWS) initiators. Final shutdown without reliance on engineered safety features can then be accomplished if sufficient time is available for operator intervention to terminate fission power production and/or provide auxiliary cooling prior to significant core disruption. Coherent cladding failure under the sustained elevated temperatures of ATWS events serves as one indicator of core disruption. In this paper we combine uncertainties in cladding failure data with uncertainties in calculations of ATWS cladding temperature conditions to calculate probabilities of cladding failure as a function of the time for accident recovery

  14. Cladding failure probability modeling for risk evaluations of fast reactors

    International Nuclear Information System (INIS)

    Mueller, C.J.; Kramer, J.M.

    1987-01-01

    This paper develops the methodology to incorporate cladding failure data and associated modeling into risk evaluations of liquid metal-cooled fast reactors (LMRs). Current U.S. innovative designs for metal-fueled pool-type LMRs take advantage of inherent reactivity feedback mechanisms to limit reactor temperature increases in response to classic anticipated-transient-without-scram (ATWS) initiators. Final shutdown without reliance on engineered safety features can then be accomplished if sufficient time is available for operator intervention to terminate fission power production and/or provide auxiliary cooling prior to significant core disruption. Coherent cladding failure under the sustained elevated temperatures of ATWS events serves as one indicator of core disruption. In this paper we combine uncertainties in cladding failure data with uncertainties in calculations of ATWS cladding temperature conditions to calculate probabilities of cladding failure as a function of the time for accident recovery. (orig.)

  15. Wood-adhesive bonding failure : modeling and simulation

    Science.gov (United States)

    Zhiyong Cai

    2010-01-01

    The mechanism of wood bonding failure when exposed to wet conditions or wet/dry cycles is not fully understood and the role of the resulting internal stresses exerted upon the wood-adhesive bondline has yet to be quantitatively determined. Unlike previous modeling this study has developed a new two-dimensional internal-stress model on the basis of the mechanics of...

  16. 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).

  17. 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.

  18. Failure Diameter of PBX 9502: Simulations with the SURFplus model

    Energy Technology Data Exchange (ETDEWEB)

    Menikoff, Ralph [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-07-03

    SURFplus is a reactive burn model for high explosives aimed at modelling shock initiation and propagation of detonation waves. It utilizes the SURF model for the fast hot-spot reaction plus a slow reaction for the energy released by carbon clustering. A feature of the SURF model is that there is a partially decoupling between burn rate parameters and detonation wave properties. Previously, parameters for PBX 9502 that control shock ini- tiation had been calibrated to Pop plot data (distance-of-run to detonation as a function of shock pressure initiating the detonation). Here burn rate parameters for the high pres- sure regime are adjusted to t the failure diameter and the limiting detonation speed just above the failure diameter. Simulated results are shown for an uncon ned rate stick when the 9502 diameter is slightly above and slightly below the failure diameter. Just above the failure diameter, in the rest frame of the detonation wave, the front is sonic at the PBX/air interface. As a consequence, the lead shock in the neighborhood of the interface is supported by the detonation pressure in the interior of the explosive rather than the reaction immediately behind the front. In the interior, the sonic point occurs near the end of the fast hot-spot reaction. Consequently, the slow carbon clustering reaction can not a ect the failure diameter. Below the failure diameter, the radial extent of the detonation front decreases starting from the PBX/air interface. That is, the failure starts at the PBX boundary and propagates inward to the axis of the rate stick.

  19. Failure Propagation Modeling and Analysis via System Interfaces

    Directory of Open Access Journals (Sweden)

    Lin Zhao

    2016-01-01

    Full Text Available Safety-critical systems must be shown to be acceptably safe to deploy and use in their operational environment. One of the key concerns of developing safety-critical systems is to understand how the system behaves in the presence of failures, regardless of whether that failure is triggered by the external environment or caused by internal errors. Safety assessment at the early stages of system development involves analysis of potential failures and their consequences. Increasingly, for complex systems, model-based safety assessment is becoming more widely used. In this paper we propose an approach for safety analysis based on system interface models. By extending interaction models on the system interface level with failure modes as well as relevant portions of the physical system to be controlled, automated support could be provided for much of the failure analysis. We focus on fault modeling and on how to compute minimal cut sets. Particularly, we explore state space reconstruction strategy and bounded searching technique to reduce the number of states that need to be analyzed, which remarkably improves the efficiency of cut sets searching algorithm.

  20. Computational modeling for hexcan failure under core distruptive accidental conditions

    Energy Technology Data Exchange (ETDEWEB)

    Sawada, T.; Ninokata, H.; Shimizu, A. [Tokyo Institute of Technology (Japan)

    1995-09-01

    This paper describes the development of computational modeling for hexcan wall failures under core disruptive accident conditions of fast breeder reactors. A series of out-of-pile experiments named SIMBATH has been analyzed by using the SIMMER-II code. The SIMBATH experiments were performed at KfK in Germany. The experiments used a thermite mixture to simulate fuel. The test geometry of SIMBATH ranged from single pin to 37-pin bundles. In this study, phenomena of hexcan wall failure found in a SIMBATH test were analyzed by SIMMER-II. Although the original model of SIMMER-II did not calculate any hexcan failure, several simple modifications made it possible to reproduce the hexcan wall melt-through observed in the experiment. In this paper the modifications and their significance are discussed for further modeling improvements.

  1. A quasi-independence model to estimate failure rates

    International Nuclear Information System (INIS)

    Colombo, A.G.

    1988-01-01

    The use of a quasi-independence model to estimate failure rates is investigated. Gate valves of nuclear plants are considered, and two qualitative covariates are taken into account: plant location and reactor system. Independence between the two covariates and an exponential failure model are assumed. The failure rate of the components of a given system and plant is assumed to be a constant, but it may vary from one system to another and from one plant to another. This leads to the analysis of a contingency table. A particular feature of the model is the different operating time of the components in the various cells which can also be equal to zero. The concept of independence of the covariates is then replaced by that of quasi-independence. The latter definition, however, is used in a broader sense than usual. Suitable statistical tests are discussed and a numerical example illustrates the use of the method. (author)

  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. 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)

  4. 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.

  5. A cascading failure model for analyzing railway accident causation

    Science.gov (United States)

    Liu, Jin-Tao; Li, Ke-Ping

    2018-01-01

    In this paper, a new cascading failure model is proposed for quantitatively analyzing the railway accident causation. In the model, the loads of nodes are redistributed according to the strength of the causal relationships between the nodes. By analyzing the actual situation of the existing prevention measures, a critical threshold of the load parameter in the model is obtained. To verify the effectiveness of the proposed cascading model, simulation experiments of a train collision accident are performed. The results show that the cascading failure model can describe the cascading process of the railway accident more accurately than the previous models, and can quantitatively analyze the sensitivities and the influence of the causes. In conclusion, this model can assist us to reveal the latent rules of accident causation to reduce the occurrence of railway accidents.

  6. Fission product release modelling for application of fuel-failure monitoring and detection - An overview

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, B.J., E-mail: lewibre@gmail.com [Department of Chemistry and Chemical Engineering, Royal Military College of Canada, Kingston, Ontario, K7K 7B4 (Canada); Chan, P.K.; El-Jaby, A. [Department of Chemistry and Chemical Engineering, Royal Military College of Canada, Kingston, Ontario, K7K 7B4 (Canada); Iglesias, F.C.; Fitchett, A. [Candesco Division of Kinectrics Inc., 26 Wellington Street East, 3rd Floor, Toronto, Ontario M5E 1S2 (Canada)

    2017-06-15

    A review of fission product release theory is presented in support of fuel-failure monitoring analysis for the characterization and location of defective fuel. This work is used to describe: (i) the development of the steady-state Visual-DETECT code for coolant activity analysis to characterize failures in the core and the amount of tramp uranium; (ii) a generalization of this model in the STAR code for prediction of the time-dependent release of iodine and noble gas fission products to the coolant during reactor start-up, steady-state, shutdown, and bundle-shifting manoeuvres; (iii) an extension of the model to account for the release of fission products that are delayed-neutron precursors for assessment of fuel-failure location; and (iv) a simplification of the steady-state model to assess the methodology proposed by WANO for a fuel reliability indicator for water-cooled reactors.

  7. Mathematical models for prediction of safety factors for a simply ...

    African Journals Online (AJOL)

    From the results obtained, mathematical prediction models were developed using a least square regression analysis for bending, shear and deflection modes of failure considered in the study. The results showed that the safety factors for material, dead and live load are not unique, but they are influenced by safety index ...

  8. A Markov deterioration model for predicting recurrent maintenance ...

    African Journals Online (AJOL)

    The parameters of the Markov chain model for predicting the condition of the road at a design · period for· the flexible pavement failures of wheel track rutting, cracks and pot holes were developed for the Niger State· road network . in Nigeria. Twelve sampled candidate roads were each subjected to standard inventory, traffic ...

  9. Modelling and Verifying Communication Failure of Hybrid Systems in HCSP

    DEFF Research Database (Denmark)

    Wang, Shuling; Nielson, Flemming; Nielson, Hanne Riis

    2016-01-01

    Hybrid systems are dynamic systems with interacting discrete computation and continuous physical processes. They have become ubiquitous in our daily life, e.g. automotive, aerospace and medical systems, and in particular, many of them are safety-critical. For a safety-critical hybrid system......, in the presence of communication failure, the expected control from the controller will get lost and as a consequence the physical process cannot behave as expected. In this paper, we mainly consider the communication failure caused by the non-engagement of one party in communication action, i.......e. the communication itself fails to occur. To address this issue, this paper proposes a formal framework by extending HCSP, a formal modeling language for hybrid systems, for modeling and verifying hybrid systems in the absence of receiving messages due to communication failure. We present two inference systems...

  10. 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.

  11. 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.

  12. MATHEMATICAL MODEL OF WEAR CHARACTER FAILURE IN AIRCRAFT OPERATION

    OpenAIRE

    Радько, Олег Віталійович; Молдован, Володимир Дмитрович

    2016-01-01

    In this paper the mathematical model of failures associated with wear during aircraft exploitationis developed. Тhe calculations of the distribution function, distribution density and failurerate gamma distribution at low coefficients of variation and the relatively low value of averagewear rate for the current time, which varies quite widely. The results coincide well with thephysical concepts and can be used to build different models of aircraft. Gamma distribution is apretty good model for...

  13. Fuzzy modeling of analytical redundancy for sensor failure detection

    International Nuclear Information System (INIS)

    Tsai, T.M.; Chou, H.P.

    1991-01-01

    Failure detection and isolation (FDI) in dynamic systems may be accomplished by testing the consistency of the system via analytically redundant relations. The redundant relation is basically a mathematical model relating system inputs and dissimilar sensor outputs from which information is extracted and subsequently examined for the presence of failure signatures. Performance of the approach is often jeopardized by inherent modeling error and noise interference. To mitigate such effects, techniques such as Kalman filtering, auto-regression-moving-average (ARMA) modeling in conjunction with probability tests are often employed. These conventional techniques treat the stochastic nature of uncertainties in a deterministic manner to generate best-estimated model and sensor outputs by minimizing uncertainties. In this paper, the authors present a different approach by treating the effect of uncertainties with fuzzy numbers. Coefficients in redundant relations derived from first-principle physical models are considered as fuzzy parameters and on-line updated according to system behaviors. Failure detection is accomplished by examining the possibility that a sensor signal occurred in an estimated fuzzy domain. To facilitate failure isolation, individual FDI monitors are designed for each interested sensor

  14. 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.

  15. 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.

  16. Application of different failure criteria in fuel pin modelling and consequences for overpower transients in LMFBRs

    International Nuclear Information System (INIS)

    Kuczera, B.; Royl, P.

    1975-01-01

    The CAPRI-2 code system for analysis of hypothetical core disruptive accidents in LMFBRs has recently been coupled with the transient deformation model BREDA-2. The new code system determines thermal and mechanical loads under transient conditions for both, fresh and irradiated fuel and cladding, taking into account fuel restructuring as well as effects from fission gas and fuel and clad swelling. The system has been used for analysis of mild uncontrolled overpower transients in the SNR-300 to predict failure, and to initialize and calculate subsequent fuel coolant interaction (FCI). Thirteen channels have been coupled by point kinetics for the whole core analysis. Three different failure mechanisms and their influence on accident sequence have been investigated: clad melt-through; clad burst caused by internal pressure build-up; clad straining due to differential thermal expansion between fuel and clad cylinders. The results of these analyses show that each failure mechanism will lead to rather different failure and accident sequences. There is still a lack of experimental data from which failure thresholds can be derived. To get better predictions from the applied models an improved understanding of fission release and its relation to fuel porosity also some better experimental data on fluence and temperature dependent rupture strains of the cladding material should be available

  17. 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

  18. 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.

  19. 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.

  20. Reliability physics and engineering time-to-failure modeling

    CERN Document Server

    McPherson, J W

    2013-01-01

    Reliability Physics and Engineering provides critically important information that is needed for designing and building reliable cost-effective products. Key features include:  ·       Materials/Device Degradation ·       Degradation Kinetics ·       Time-To-Failure Modeling ·       Statistical Tools ·       Failure-Rate Modeling ·       Accelerated Testing ·       Ramp-To-Failure Testing ·       Important Failure Mechanisms for Integrated Circuits ·       Important Failure Mechanisms for  Mechanical Components ·       Conversion of Dynamic  Stresses into Static Equivalents ·       Small Design Changes Producing Major Reliability Improvements ·       Screening Methods ·       Heat Generation and Dissipation ·       Sampling Plans and Confidence Intervals This textbook includes numerous example problems with solutions. Also, exercise problems along with the answers are included at the end of each chapter. Relia...

  1. 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.

  2. A bivariate model for analyzing recurrent multi-type automobile failures

    Science.gov (United States)

    Sunethra, A. A.; Sooriyarachchi, M. R.

    2017-09-01

    The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by

  3. Assessment of compressive failure process of cortical bone materials using damage-based model.

    Science.gov (United States)

    Ng, Theng Pin; R Koloor, S S; Djuansjah, J R P; Abdul Kadir, M R

    2017-02-01

    The main failure factors of cortical bone are aging or osteoporosis, accident and high energy trauma or physiological activities. However, the mechanism of damage evolution coupled with yield criterion is considered as one of the unclear subjects in failure analysis of cortical bone materials. Therefore, this study attempts to assess the structural response and progressive failure process of cortical bone using a brittle damaged plasticity model. For this reason, several compressive tests are performed on cortical bone specimens made of bovine femur, in order to obtain the structural response and mechanical properties of the material. Complementary finite element (FE) model of the sample and test is prepared to simulate the elastic-to-damage behavior of the cortical bone using the brittle damaged plasticity model. The FE model is validated in a comparative method using the predicted and measured structural response as load-compressive displacement through simulation and experiment. FE results indicated that the compressive damage initiated and propagated at central region where maximum equivalent plastic strain is computed, which coincided with the degradation of structural compressive stiffness followed by a vast amount of strain energy dissipation. The parameter of compressive damage rate, which is a function dependent on damage parameter and the plastic strain is examined for different rates. Results show that considering a similar rate to the initial slope of the damage parameter in the experiment would give a better sense for prediction of compressive failure. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. 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

  5. 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...

  6. Convex models and probabilistic approach of nonlinear fatigue failure

    International Nuclear Information System (INIS)

    Qiu Zhiping; Lin Qiang; Wang Xiaojun

    2008-01-01

    This paper is concerned with the nonlinear fatigue failure problem with uncertainties in the structural systems. In the present study, in order to solve the nonlinear problem by convex models, the theory of ellipsoidal algebra with the help of the thought of interval analysis is applied. In terms of the inclusion monotonic property of ellipsoidal functions, the nonlinear fatigue failure problem with uncertainties can be solved. A numerical example of 25-bar truss structures is given to illustrate the efficiency of the presented method in comparison with the probabilistic approach

  7. Correlation model to analyze dependent failures for probabilistic risk assessment

    International Nuclear Information System (INIS)

    Dezfuli, H.

    1985-01-01

    A methodology is formulated to study the dependent (correlated) failures of various abnormal events in nuclear power plants. This methodology uses correlation analysis is a means for predicting and quantifying the dependent failures. Appropriate techniques are also developed to incorporate the dependent failure in quantifying fault trees and accident sequences. The uncertainty associated with each estimation in all of the developed techniques is addressed and quantified. To identify the relative importance of the degree of dependency (correlation) among events and to incorporate these dependencies in the quantification phase of PRA, the interdependency between a pair of events in expressed with the aid of the correlation coefficient. For the purpose of demonstrating the methodology, the data base used in the Accident Sequence Precursor Study (ASP) was adopted and simulated to obtain distributions for the correlation coefficients. A computer program entitled Correlation Coefficient Generator (CCG) was developed to generate a distribution for each correlation coefficient. The method of bootstrap technique was employed in the CCG computer code to determine confidence limits of the estimated correlation coefficients. A second computer program designated CORRELATE was also developed to obtain probability intervals for both fault trees and accident sequences with statistically correlated failure data

  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. Discrete competing risk model with application to modeling bus-motor failure data

    International Nuclear Information System (INIS)

    Jiang, R.

    2010-01-01

    Failure data are often modeled using continuous distributions. However, a discrete distribution can be appropriate for modeling interval or grouped data. When failure data come from a complex system, a simple discrete model can be inappropriate for modeling such data. This paper presents two types of discrete distributions. One is formed by exponentiating an underlying distribution, and the other is a two-fold competing risk model. The paper focuses on two special distributions: (a) exponentiated Poisson distribution and (b) competing risk model involving a geometric distribution and an exponentiated Poisson distribution. The competing risk model has a decreasing-followed-by-unimodal mass function and a bathtub-shaped failure rate. Five classical data sets on bus-motor failures can be simultaneously and appropriately fitted by a general 5-parameter competing risk model with the parameters being functions of the number of successive failures. The lifetime and aging characteristics of the fitted distribution are analyzed.

  11. 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

  12. 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

  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. 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.

  15. Diseño de un modelo específico para la predicción de la quiebra de micro-entities // Design of a Specific Model for Predicting Micro-Entities Failure

    Directory of Open Access Journals (Sweden)

    Antonio J. Blanco Oliver

    2016-12-01

    Full Text Available La importancia de las micro-entities como generadoras de empleo y propulsoras de la actividad económica conlleva, unida a sus mayores tasas de quiebra y a su dificultad para acceder a las fuentes de financiación, la necesidad de diseñar métodos apropiados que anticipen sus quiebras. Con este fin, en este trabajo se desarrolla un modelo híbrido mediante la combinación de enfoques paramétricos y no paramétricos para la detección de sus quiebras. Para ello, se seleccionan las variables con mayor poder predictivo para detectar la quiebra mediante un modelo híbrido de regresión logística (LR y árboles de regresión y clasificación (CART. Nuestros resultados muestran que este modelo híbrido obtiene una mejor performance que aquellos modelos implementados de forma aislada, además de tener una más fácil interpretación y una convergencia más rápida. Por otra parte, se constata la conveniencia de la introducción de variables no financieras y macroeconómicas que complementen a la información proporcionada por los ratios financieros para la predicción de la quiebra de las micro-entities, lo cual está en línea con las características propias e idiosincrasia de este tamaño empresarial recientemente definido por la Comisión Europea. ------------------------------------ The importance of micro-entities due to their generation of employment and propelling economic activity, together with the fact of their particularities, implies the need to design appropriate methods that anticipate their bankruptcies. For that purpose, a hybrid model by combining parametric and nonparametric approaches is developed in this paper. First, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR. Subsequently, a non-parametric method, namely regression trees and classification (CART, is then applied to companies classified as "bankruptcy" or "non-bankruptcy". Our results show that this model

  16. 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.

  17. 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.

  18. Conduit Stability and Collapse in Explosive Volcanic Eruptions: Coupling Conduit Flow and Failure Models

    Science.gov (United States)

    Mullet, B.; Segall, P.

    2017-12-01

    Explosive volcanic eruptions can exhibit abrupt changes in physical behavior. In the most extreme cases, high rates of mass discharge are interspaced by dramatic drops in activity and periods of quiescence. Simple models predict exponential decay in magma chamber pressure, leading to a gradual tapering of eruptive flux. Abrupt changes in eruptive flux therefore indicate that relief of chamber pressure cannot be the only control of the evolution of such eruptions. We present a simplified physics-based model of conduit flow during an explosive volcanic eruption that attempts to predict stress-induced conduit collapse linked to co-eruptive pressure loss. The model couples a simple two phase (gas-melt) 1-D conduit solution of the continuity and momentum equations with a Mohr-Coulomb failure condition for the conduit wall rock. First order models of volatile exsolution (i.e. phase mass transfer) and fragmentation are incorporated. The interphase interaction force changes dramatically between flow regimes, so smoothing of this force is critical for realistic results. Reductions in the interphase force lead to significant relative phase velocities, highlighting the deficiency of homogenous flow models. Lateral gas loss through conduit walls is incorporated using a membrane-diffusion model with depth dependent wall rock permeability. Rapid eruptive flux results in a decrease of chamber and conduit pressure, which leads to a critical deviatoric stress condition at the conduit wall. Analogous stress distributions have been analyzed for wellbores, where much work has been directed at determining conditions that lead to wellbore failure using Mohr-Coulomb failure theory. We extend this framework to cylindrical volcanic conduits, where large deviatoric stresses can develop co-eruptively leading to multiple distinct failure regimes depending on principal stress orientations. These failure regimes are categorized and possible implications for conduit flow are discussed, including

  19. 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

  20. Dam failure analysis/calibration using NWS models on dam failure in Alton, New Hampshire

    International Nuclear Information System (INIS)

    Capone, E.J.

    1998-01-01

    The State of New Hampshire Water Resources Board, the United States Geological Service, and private concerns have compiled data on the cause of a catastrophic failure of the Bergeron Dam in Alton, New Hampshire in March of 1996. Data collected related to the cause of the breach, the breach parameters, the soil characteristics of the failed section, and the limits of downstream flooding. Dam break modeling software was used to calibrate and verify the simulated flood-wave caused by the Bergeron Dam breach. Several scenarios were modeled, using different degrees of detail concerning the topography/channel-geometry of the affected areas. A sensitivity analysis of the important output parameters was completed. The relative importance of model parameters on the results was assessed against the background of observed historical events

  1. Modeling Dynamic Anisotropic Behaviour and Spall Failure in Commercial Aluminium Alloys AA7010

    Science.gov (United States)

    Mohd Nor, M. K.; Ma'at, N.; Ho, C. S.

    2018-04-01

    This paper presents a finite strain constitutive model to predict a complex elastoplastic deformation behaviour involves very high pressures and shockwaves in orthotropic materials of aluminium alloys. The previous published constitutive model is used as a reference to start the development in this work. The proposed formulation that used a new definition of Mandel stress tensor to define Hill's yield criterion and a new shock equation of state (EOS) of the generalised orthotropic pressure is further enhanced with Grady spall failure model to closely predict shockwave propagation and spall failure in the chosen commercial aluminium alloy. This hyperelastic-plastic constitutive model is implemented as a new material model in the Lawrence Livermore National Laboratory (LLNL)-DYNA3D code of UTHM's version, named Material Type 92 (Mat92). The implementations of a new EOS of the generalised orthotropic pressure including the spall failure are also discussed in this paper. The capability of the proposed constitutive model to capture the complex behaviour of the selected material is validated against range of Plate Impact Test data at 234, 450 and 895 ms-1 impact velocities.

  2. Prediction of Basic Math Course Failure Rate in the Physics, Meteorology, Mathematics, Actuarial Sciences and Pharmacy Degree Programs

    Directory of Open Access Journals (Sweden)

    Luis Rojas-Torres

    2014-09-01

    Full Text Available This paper summarizes a study conducted in 2013 with the purpose of predicting the failure rate of math courses taken by Pharmacy, Mathematics, Actuarial Science, Physics and Meteorology students at Universidad de Costa Rica (UCR. Using the Logistics Regression statistical techniques applied to the 2010 cohort, failure rates were predicted of students in the aforementioned programs in one of their Math introductory courses (Calculus 101 for Physics and Meteorology, Math Principles for Mathematics and Actuarial Science and Applied Differential Equations for Pharmacy. For these models, the UCR admission average, the student’s genre, and the average correct answers in the Quantitative Skills Test were used as predictor variables. The most important variable for all models was the Quantitative Skills Test, and the model with the highest correct classification rate was the Logistics Regression. For the estimated Physics-Meteorology, Pharmacy and Mathematics-Actuarial Science models, correct classifications were 89.8%, 73.6%, and 93.9%, respectively.

  3. 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.

  4. Graft intolerance syndrome requiring graft nephrectomy after late kidney graft failure: can it be predicted? A retrospective cohort study.

    Science.gov (United States)

    Bunthof, Kim L W; Verhoeks, Carmen M; van den Brand, Jan A J G; Hilbrands, Luuk B

    2018-02-01

    Graft nephrectomy is recommended in case of early graft failure. When the graft fails more than 3-6 months after transplantation, it is current practice to follow a wait-and-see policy. A common indication for graft removal is the graft intolerance syndrome. We aimed to create a risk prediction model for the occurrence of graft intolerance resulting in graft nephrectomy. We collected data of kidney transplantations performed in our center between 1980 and 2010 that failed at least 6 months after transplantation. We evaluated the association between baseline characteristics and the occurrence of graft nephrectomy because of graft intolerance using a competing risk regression model. Prognostic factors were included in a multivariate prediction model. In- and exclusion criteria were met in 288 cases. In 48 patients, the graft was removed because of graft intolerance. Donor age, the number of rejections, and shorter graft survival were predictive factors for graft nephrectomy because of the graft intolerance syndrome. These factors were included in a prediction rule. Using donor age, graft survival, and the number of rejections, clinicians can predict the need for graft nephrectomy with a reasonable accuracy. © 2017 Steunstichting ESOT.

  5. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  6. Cascading failures in interdependent systems under a flow redistribution model

    Science.gov (United States)

    Zhang, Yingrui; Arenas, Alex; Yaǧan, Osman

    2018-02-01

    Robustness and cascading failures in interdependent systems has been an active research field in the past decade. However, most existing works use percolation-based models where only the largest component of each network remains functional throughout the cascade. Although suitable for communication networks, this assumption fails to capture the dependencies in systems carrying a flow (e.g., power systems, road transportation networks), where cascading failures are often triggered by redistribution of flows leading to overloading of lines. Here, we consider a model consisting of systems A and B with initial line loads and capacities given by {LA,i,CA ,i} i =1 n and {LB,i,CB ,i} i =1 n, respectively. When a line fails in system A , a fraction of its load is redistributed to alive lines in B , while remaining (1 -a ) fraction is redistributed equally among all functional lines in A ; a line failure in B is treated similarly with b giving the fraction to be redistributed to A . We give a thorough analysis of cascading failures of this model initiated by a random attack targeting p1 fraction of lines in A and p2 fraction in B . We show that (i) the model captures the real-world phenomenon of unexpected large scale cascades and exhibits interesting transition behavior: the final collapse is always first order, but it can be preceded by a sequence of first- and second-order transitions; (ii) network robustness tightly depends on the coupling coefficients a and b , and robustness is maximized at non-trivial a ,b values in general; (iii) unlike most existing models, interdependence has a multifaceted impact on system robustness in that interdependency can lead to an improved robustness for each individual network.

  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. 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.

  9. Cap plasticity models and compactive and dilatant pre-failure deformation

    International Nuclear Information System (INIS)

    Fossum, Arlo F.; Fredrich, Joanne T.

    2000-01-01

    At low mean stresses, porous geomaterials fail by shear localization, and at higher mean stresses, they undergo strain-hardening behavior. Cap plasticity models attempt to model this behavior using a pressure-dependent shear yield and/or shear limit-state envelope with a hardening or hardening/softening elliptical end cap to define pore collapse. While these traditional models describe compactive yield and ultimate shear failure, difficulties arise when the behavior involves a transition from compactive to dilatant deformation that occurs before the shear failure or limit-state shear stress is reached. In this work, a continuous surface cap plasticity model is used to predict compactive and dilatant pre-failure deformation. During loading the stress point can pass freely through the critical state point separating compactive from dilatant deformation. The predicted volumetric strain goes from compactive to dilatant without the use of a non-associated flow rule. The new model is stable in that Drucker's stability postulates are satisfied. The study has applications to several geosystems of current engineering interest (oil and gas reservoirs, nuclear waste repositories, buried targets, and depleted reservoirs for possible use for subsurface sequestration of greenhouse gases)

  10. Fold catastrophe model of dynamic pillar failure in asymmetric mining

    Energy Technology Data Exchange (ETDEWEB)

    Yue Pan; Ai-wu Li; Yun-song Qi [Qingdao Technological University, Qingdao (China). College of Civil Engineering

    2009-01-15

    A rock burst disaster not only destroys the pit facilities and results in economic loss but it also threatens the life of the miners. Pillar rock burst has a higher frequency of occurrence in the pit compared to other kinds of rock burst. Understanding the cause, magnitude and prevention of pillar rock burst is a significant undertaking. Equations describing the bending moment and displacement of the rock beam in asymmetric mining have been deduced for simplified asymmetric beam-pillar systems. Using the symbolic operation software MAPLE 9.5 a catastrophe model of the dynamic failure of an asymmetric rock-beam pillar system has been established. The differential form of the total potential function deduced from the law of conservation of energy was used for this deduction. The critical conditions and the initial and final positions of the pillar during failure have been given in analytical form. The amount of elastic energy released by the rock beam at the instant of failure is determined as well. A diagrammatic form showing the pillar failure was plotted using MATLAB software. This graph contains a wealth of information and is important for understanding the behavior during each deformation phase of the rock-beam pillar system. The graphic also aids in distinguishing the equivalent stiffness of the rock beam in different directions. 11 refs., 8 figs.

  11. 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.

  12. Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study

    International Nuclear Information System (INIS)

    Hall, P.L.; Strutt, J.E.

    2003-01-01

    In reliability engineering, component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or 'wear out' failures. When the time-distribution of failures of a population of components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values 1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes. We describe a methodology for the implementation of physics-of-failure models of component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1-2% can be obtained using typically 1000 simulations. The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η. Examples are given of the results obtained using three different models: (1) the Eyring-Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β, from β>1, characteristic of wear-out behaviour, to β<1, characteristic of early-life failure, depending on the degree of

  13. 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

  14. A personalized BEST: characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol.

    Directory of Open Access Journals (Sweden)

    David P Kao

    Full Text Available Heart failure patients with reduced ejection fraction (HFREF are heterogenous, and our ability to identify patients likely to respond to therapy is limited. We present a method of identifying disease subtypes using high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizing prognosis and treatment in HFREF.A total of 1121 patients with nonischemic HFREF from the β-blocker Evaluation of Survival Trial were categorized according to 27 clinical features. Latent class analysis was used to generate two latent class models, LCM A and B, to identify HFREF subtypes. LCM A consisted of features associated with HF pathogenesis, whereas LCM B consisted of markers of HF progression and severity. The Seattle Heart Failure Model (SHFM Score was also calculated for all patients. Mortality, improvement in left ventricular ejection fraction (LVEF defined as an increase in LVEF ≥5% and a final LVEF of 35% after 12 months, and effect of bucindolol on both outcomes were compared across HFREF subtypes. Performance of models that included a combination of LCM subtypes and SHFM scores towards predicting mortality and LVEF response was estimated and subsequently validated using leave-one-out cross-validation and data from the Multicenter Oral Carvedilol Heart Failure Assessment Trial.A total of 6 subtypes were identified using LCM A and 5 subtypes using LCM B. Several subtypes resembled familiar clinical phenotypes. Prognosis, improvement in LVEF, and the effect of bucindolol treatment differed significantly between subtypes. Prediction improved with addition of both latent class models to SHFM for both 1-year mortality and LVEF response outcomes.The combination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with implications for prognosis and response to specific therapies that may provide insight into mechanisms of disease. These subtypes may facilitate development of personalized

  15. 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.

  16. 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

  17. A competing risk model of first failure site after definitive (chemo) radiation therapy for locally advanced non-small cell lung cancer

    DEFF Research Database (Denmark)

    Nygård, Lotte; Vogelius, Ivan R; Fischer, Barbara M

    2018-01-01

    INTRODUCTION: The aim of the study was to build a model of first failure site and lesion specific failure probability after definitive chemo-radiotherapy for inoperable non-small cell lung cancer (NSCLC). METHODS: We retrospectively analyzed 251 patients receiving definitive chemo......-regional failure, multivariable logistic regression was applied to assess risk of each lesion being first site of failure. The two models were used in combination to predict lesion failure probability accounting for competing events. RESULTS: Adenocarcinoma had a lower hazard ratio (HR) of loco-regional (LR...

  18. 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.

  19. Upgrade of Common Cause Failure Modelling of NPP Krsko PSA

    International Nuclear Information System (INIS)

    Vukovic, I.; Mikulicic, V.; Vrbanic, I.

    2006-01-01

    Over the last thirty years the probabilistic safety assessments (PSA) have been increasingly applied in technical engineering practice. Various failure modes of system of concern are mathematically and explicitly modelled by means of fault tree structure. Statistical independence of basic events from which the fault tree is built is not acceptable for an event category referred to as common cause failures (CCF). Based on overview of current international status of modelling of common cause failures in PSA several steps were made related to primary technical basis for methodology and data used for CCF model upgrade project in NPP Krsko (NEK) PSA. As a primary technical basis for methodological aspects of CCF modelling in Krsko PSA the following documents were considered: NUREG/CR-5485, NUREG/CR-4780, and Westinghouse Owners Group documents (WOG) WCAP-15674 and WCAP-15167. Use of these documents is supported by the most relevant guidelines and standards in the field, such as ASME PRA Standard and NRC Regulatory Guide 1.200. WCAP documents are in compliance with NUREG/CR-5485 and NUREG/CR-4780. Additionally, they provide WOG perspective on CCF modelling, which is important to consider since NEK follows WOG practice in resolving many generic and regulatory issues. It is, therefore, desirable that NEK CCF methodology and modelling is in general accordance with recommended WOG approaches. As a primary basis for CCF data needed to estimate CCF model parameters and their uncertainty, the main used documents were: NUREG/CR-5497, NUREG/CR-6268, WCAP-15167, and WCAP-16187. Use of NUREG/CR-5497 and NUREG/CR-6268 as a source of data for CCF parameter estimating is supported by the most relevant industry and regulatory PSA guides and standards currently existing in the field, including WOG. However, the WCAP document WCAP-16187 has provided a basis for CCF parameter values specific to Westinghouse PWR plants. Many of events from NRC / INEEL database were re-classified in WCAP

  20. Murine Models of Heart Failure With Preserved Ejection Fraction

    Directory of Open Access Journals (Sweden)

    Maria Valero-Muñoz, PhD

    2017-12-01

    Full Text Available Heart failure with preserved ejection fraction (HFpEF is characterized by signs and symptoms of heart failure in the presence of a normal left ventricular ejection fraction. Despite accounting for up to 50% of all clinical presentations of heart failure, the mechanisms implicated in HFpEF are poorly understood, thus precluding effective therapy. The pathophysiological heterogeneity in the HFpEF phenotype also contributes to this disease and likely to the absence of evidence-based therapies. Limited access to human samples and imperfect animal models that completely recapitulate the human HFpEF phenotype have impeded our understanding of the mechanistic underpinnings that exist in this disease. Aging and comorbidities such as atrial fibrillation, hypertension, diabetes and obesity, pulmonary hypertension, and renal dysfunction are highly associated with HFpEF, yet the relationship and contribution between them remains ill-defined. This review discusses some of the distinctive clinical features of HFpEF in association with these comorbidities and highlights the advantages and disadvantage of commonly used murine models used to study the HFpEF phenotype.

  1. Cardioprotective Effect of Resveratrol in a Postinfarction Heart Failure Model

    Directory of Open Access Journals (Sweden)

    Adam Riba

    2017-01-01

    Full Text Available Despite great advances in therapies observed during the last decades, heart failure (HF remained a major health problem in western countries. In order to further improve symptoms and survival in patients with heart failure, novel therapeutic strategies are needed. In some animal models of HF resveratrol (RES, it was able to prevent cardiac hypertrophy, contractile dysfunction, and remodeling. Several molecular mechanisms are thought to be involved in its protective effects, such as inhibition of prohypertrophic signaling molecules, improvement of myocardial Ca2+ handling, regulation of autophagy, and the reduction of oxidative stress and inflammation. In our present study, we wished to further examine the effects of RES on prosurvival (Akt-1, GSK-3β and stress signaling (p38-MAPK, ERK 1/2, and MKP-1 pathways, on oxidative stress (iNOS, COX-2 activity, and ROS formation, and ultimately on left ventricular function, hypertrophy and fibrosis in a murine, and isoproterenol- (ISO- induced postinfarction heart failure model. RES treatment improved left ventricle function, decreased interstitial fibrosis, cardiac hypertrophy, and the level of plasma BNP induced by ISO treatment. ISO also increased the activation of P38-MAPK, ERK1/2Thr183-Tyr185, COX-2, iNOS, and ROS formation and decreased the phosphorylation of Akt-1, GSK-3β, and MKP-1, which were favorably influenced by RES. According to our results, regulation of these pathways may also contribute to the beneficial effects of RES in HF.

  2. Promoting success or preventing failure: cultural differences in motivation by positive and negative role models.

    Science.gov (United States)

    Lockwood, Penelope; Marshall, Tara C; Sadler, Pamela

    2005-03-01

    In two studies, cross-cultural differences in reactions to positive and negative role models were examined. The authors predicted that individuals from collectivistic cultures, who have a stronger prevention orientation, would be most motivated by negative role models, who highlight a strategy of avoiding failure; individuals from individualistic cultures, who have a stronger promotion focus, would be most motivated by positive role models, who highlight a strategy of pursuing success. In Study 1, the authors examined participants' reported preferences for positive and negative role models. Asian Canadian participants reported finding negative models more motivating than did European Canadians; self-construals and regulatory focus mediated cultural differences in reactions to role models. In Study 2, the authors examined the impact of role models on the academic motivation of Asian Canadian and European Canadian participants. Asian Canadians were motivated only by a negative model, and European Canadians were motivated only by a positive model.

  3. FEM simulation of TBC failure in a model system

    Energy Technology Data Exchange (ETDEWEB)

    Seiler, P; Baeker, M; Roesier, J [Institut fuer Werkstoffe (IfW), Technische Universitaet Braunschweig (Germany); Beck, T; Schweda, M, E-mail: p.seiler@tu-bs.d [Institut fuer Energieforschung/ Werkstoffstruktur und -Eigenschaften (IEF 2), Forschungszentrum Juelich (Germany)

    2010-07-01

    In order to study the behavior of the complex failure mechanisms in thermal barrier coatings on turbine blades, a simplified model system is used to reduce the number of system parameters. The artificial system consists of a bond-coat material (fast creeping Fecralloy or slow creeping MA956) as the substrate with a Y{sub 2}O{sub 3} partially stabilized plasma sprayed zircon oxide TBC on top and a TGO between the two layers. A 2-dimensional FEM simulation was developed to calculate the growth stress inside the simplified coating system. The simulation permits the study of failure mechanisms by identifying compression and tension areas which are established by the growth of the oxide layer. This provides an insight into the possible crack paths in the coating and it allows to draw conclusions for optimizing real thermal barrier coating systems.

  4. The modelling and control of failure in bi-material ceramic laminates

    International Nuclear Information System (INIS)

    Phillipps, A.J.; Howard, S.J.; Clegg, W.J.; Clyne, T.W.

    1993-01-01

    Recent experimental and theoretical work on simple, single phase, laminated systems has indicated that failure resistant ceramics can be produced using an elegant method that avoids many of the problems and limitations of comparable fibrous ceramic composites. Theoretical work on these laminated systems has shown good agreement with experiment and simulated the effects of material properties and laminate structure on the composite performance. This work has provided guidelines for optimised laminate performance. In the current study, theoretical work has been simply extended to predict the behaviour of bi-material laminates with alternating layers of weak and strong material with different stiffnesses. Expressions for the strain energy release rates of internal advancing cracks are derived and combined with existing criteria to predict the failure behaviour of these laminates during bending. The modelling indicates three modes of failure dictated by the relative proportions, thicknesses and interfacial properties of the weak and strong phases. A critical percentage of strong phase is necessary to improve failure behaviour, in an identical argument to that for fibre composites. Incorporation of compliant layers is also investigated and implications for laminate design discussed. (orig.)

  5. 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.

  6. Analysis of terminated TOP accidents in the FTR using the Los Alamos failure model

    International Nuclear Information System (INIS)

    Mast, P.K.; Scott, J.H.

    1978-01-01

    A new fuel pin failure model (the Los Alamos Failure Model), based on a linear life fraction rule failure criterion, has been developed and is reported herein. Excellent agreement between calculated and observed failure time and location has been obtained for a number of TOP TREAT tests. Because of the nature of the failure criterion used, the code has also been used to investigate the extent of cladding damage incurred in terminated as well as unterminated TOP transients in the FTR

  7. 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

  8. Pile group program for full material modeling and progressive failure.

    Science.gov (United States)

    2008-12-01

    Strain wedge (SW) model formulation has been used, in previous work, to evaluate the response of a single pile or a group of piles (including its : pile cap) in layered soils to lateral loading. The SW model approach provides appropriate prediction f...

  9. SWOT analysis of breach models for common dike failure mechanisms

    NARCIS (Netherlands)

    Peeters, P.; Van Hoestenberghe, T.; Vincke, L.; Visser, P.J.

    2011-01-01

    The use of breach models includes two tasks: predicting breach characteristics and estimating flow through the breach. Strengths and weaknesses as well as opportunities and threats of different simplified and detailed physically-based breach models are listed following theoretical and practical

  10. Modeling cascading failures in interdependent infrastructures under terrorist attacks

    International Nuclear Information System (INIS)

    Wu, Baichao; Tang, Aiping; Wu, Jie

    2016-01-01

    An attack strength degradation model has been introduced to further capture the interdependencies among infrastructures and model cascading failures across infrastructures when terrorist attacks occur. A medium-sized energy system including oil network and power network is selected for exploring the vulnerabilities from independent networks to interdependent networks, considering the structural vulnerability and the functional vulnerability. Two types of interdependencies among critical infrastructures are involved in this paper: physical interdependencies and geographical interdependencies, shown by tunable parameters based on the probabilities of failures of nodes in the networks. In this paper, a tolerance parameter α is used to evaluation of the overloads of the substations based on power flow redistribution in power transmission systems under the attack. The results of simulation show that the independent networks or interdependent networks will be collapsed when only a small fraction of nodes are attacked under the attack strength degradation model, especially for the interdependent networks. The methodology introduced in this paper with physical interdependencies and geographical interdependencies involved in can be applied to analyze the vulnerability of the interdependent infrastructures further, and provides the insights of vulnerability of interdependent infrastructures to mitigation actions for critical infrastructure protections. - Highlights: • An attack strength degradation model based on the specified locations has been introduced. • Interdependencies considering both physical and geographical have been analyzed. • The structural vulnerability and the functional vulnerability have been considered.

  11. ISSUES ASSOCIATED WITH PROBABILISTIC FAILURE MODELING OF DIGITAL SYSTEMS

    International Nuclear Information System (INIS)

    CHU, T.L.; MARTINEZ-GURIDI, G.; LIHNER, J.; OVERLAND, D.

    2004-01-01

    The current U.S. Nuclear Regulatory Commission (NRC) licensing process of instrumentation and control (I and C) systems is based on deterministic requirements, e.g., single failure criteria, and defense in depth and diversity. Probabilistic considerations can be used as supplements to the deterministic process. The National Research Council has recommended development of methods for estimating failure probabilities of digital systems, including commercial off-the-shelf (COTS) equipment, for use in probabilistic risk assessment (PRA). NRC staff has developed informal qualitative and quantitative requirements for PRA modeling of digital systems. Brookhaven National Laboratory (BNL) has performed a review of the-state-of-the-art of the methods and tools that can potentially be used to model digital systems. The objectives of this paper are to summarize the review, discuss the issues associated with probabilistic modeling of digital systems, and identify potential areas of research that would enhance the state of the art toward a satisfactory modeling method that could be integrated with a typical probabilistic risk assessment

  12. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    Science.gov (United States)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

  13. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  14. 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.

  15. 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.

  16. 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.

  17. Failure Behavior and Constitutive Model of Weakly Consolidated Soft Rock

    Directory of Open Access Journals (Sweden)

    Wei-ming Wang

    2013-01-01

    Full Text Available Mining areas in western China are mainly located in soft rock strata with poor bearing capacity. In order to make the deformation failure mechanism and strength behavior of weakly consolidated soft mudstone and coal rock hosted in Ili No. 4 mine of Xinjiang area clear, some uniaxial and triaxial compression tests were carried out according to the samples of rocks gathered in the studied area, respectively. Meanwhile, a damage constitutive model which considered the initial damage was established by introducing a damage variable and a correction coefficient. A linearization process method was introduced according to the characteristics of the fitting curve and experimental data. The results showed that samples under different moisture contents and confining pressures presented completely different failure mechanism. The given model could accurately describe the elastic and plastic yield characteristics as well as the strain softening behavior of collected samples at postpeak stage. Moreover, the model could precisely reflect the relationship between the elastic modulus and confining pressure at prepeak stage.

  18. Mode I Failure of Armor Ceramics: Experiments and Modeling

    Science.gov (United States)

    Meredith, Christopher; Leavy, Brian

    2017-06-01

    The pre-notched edge on impact (EOI) experiment is a technique for benchmarking the damage and fracture of ceramics subjected to projectile impact. A cylindrical projectile impacts the edge of a thin rectangular plate with a pre-notch on the opposite edge. Tension is generated at the notch tip resulting in the initiation and propagation of a mode I crack back toward the impact edge. The crack can be quantitatively measured using an optical method called Digital Gradient Sensing, which measures the crack-tip deformation by simultaneously quantifying two orthogonal surface slopes via measuring small deflections of light rays from a specularly reflective surface around the crack. The deflections in ceramics are small so the high speed camera needs to have a very high pixel count. This work reports on the results from pre-crack EOI experiments of SiC and B4 C plates. The experimental data are quantitatively compared to impact simulations using an advanced continuum damage model. The Kayenta ceramic model in Alegra will be used to compare fracture propagation speeds, bifurcations and inhomogeneous initiation of failure will be compared. This will provide insight into the driving mechanisms required for the macroscale failure modeling of ceramics.

  19. 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

  20. 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...

  1. 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.

  2. A phenomenological variational multiscale constitutive model for intergranular failure in nanocrystalline materials

    KAUST Repository

    Siddiq, A.

    2013-09-01

    We present a variational multiscale constitutive model that accounts for intergranular failure in nanocrystalline fcc metals due to void growth and coalescence in the grain boundary region. Following previous work by the authors, a nanocrystalline material is modeled as a two-phase material consisting of a grain interior phase and a grain boundary affected zone (GBAZ). A crystal plasticity model that accounts for the transition from partial dislocation to full dislocation mediated plasticity is used for the grain interior. Isotropic porous plasticity model with further extension to account for failure due to the void coalescence was used for the GBAZ. The extended model contains all the deformation phases, i.e. elastic deformation, plastic deformation including deviatoric and volumetric plasticity (void growth) followed by damage initiation and evolution due to void coalescence. Parametric studies have been performed to assess the model\\'s dependence on the different input parameters. The model is then validated against uniaxial loading experiments for different materials. Lastly we show the model\\'s ability to predict the damage and fracture of a dog-bone shaped specimen as observed experimentally. © 2013 Elsevier B.V.

  3. 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.

  4. 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

  5. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  6. Immunohistochemical and molecular imaging biomarker signature for the prediction of failure site after chemoradiation for head and neck squamous cell carcinoma

    DEFF Research Database (Denmark)

    Rasmussen, Gregers Brünnich; Håkansson, Katrin E; Vogelius, Ivan R

    2017-01-01

    .23; p: .025), Bcl-2 (HR: 2.6; p: .08), SUVmax (HR: 3.5; p: .095) and GTV (HR: 1.7; p: .063). CONCLUSIONS: The models successfully distinguished between risk of locoregional failure and risk of distant metastasis, which is important information for clinical decision-making. High p53 expression has......OBJECTIVE: To identify a failure site-specific prognostic model by combining immunohistochemistry (IHC) and molecular imaging information to predict long-term failure type in squamous cell carcinoma of the head and neck. PATIENT AND METHODS: Tissue microarray blocks of 196 head and neck squamous...... cell carcinoma cases were stained for a panel of biomarkers using IHC. Gross tumor volume (GTV) from the PET/CT radiation treatment planning CT scan, maximal Standard Uptake Value (SUVmax) of fludeoxyglucose (FDG) and clinical information were included in the model building using Cox proportional...

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  12. Neurological Disorders in a Murine Model of Chronic Renal Failure

    Directory of Open Access Journals (Sweden)

    Jean-Marc Chillon

    2014-01-01

    Full Text Available Cardiovascular disease is highly prevalent in patients with chronic renal failure (CRF. However, data on the impact of CRF on the cerebral circulatory system are scarce—despite the fact that stroke is the third most common cause of cardiovascular death in people with CRF. In the present study, we examined the impact of CRF on behavior (anxiety, recognition and ischemic stroke severity in a well-defined murine model of CRF. We did not observe any significant increases between CRF mice and non-CRF mice in terms of anxiety. In contrast, CRF mice showed lower levels of anxiety in some tests. Recognition was not impaired (vs. controls after 6 weeks of CRF but was impaired after 10 weeks of CRF. Chronic renal failure enhances the severity of ischemic stroke, as evaluated by the infarct volume size in CRF mice after 34 weeks of CRF. Furthermore, neurological test results in non-CRF mice tended to improve in the days following ischemic stroke, whereas the results in CRF mice tended to worsen. In conclusion, we showed that a murine model of CRF is suitable for evaluating uremic toxicity and the associated neurological disorders. Our data confirm the role of uremic toxicity in the genesis of neurological abnormalities (other than anxiety.

  13. Modelling Dynamic Behaviour and Spall Failure of Aluminium Alloy AA7010

    Science.gov (United States)

    Ma'at, N.; Nor, M. K. Mohd; Ismail, A. E.; Kamarudin, K. A.; Jamian, S.; Ibrahim, M. N.; Awang, M. K.

    2017-10-01

    A finite strain constitutive model to predict the dynamic deformation behaviour of Aluminium Alloy 7010 including shockwaves and spall failure is developed in this work. The important feature of this newly hyperelastic-plastic constitutive formulation is a new Mandel stress tensor formulated using new generalized orthotropic pressure. This tensor is combined with a shock equation of state (EOS) and Grady spall failure. The Hill’s yield criterion is adopted to characterize plastic orthotropy by means of the evolving structural tensors that is defined in the isoclinic configuration. This material model was developed and integration into elastic and plastic parts. The elastic anisotropy is taken into account through the newly stress tensor decomposition of a generalized orthotropic pressure. Plastic anisotropy is considered through yield surface and an isotropic hardening defined in a unique alignment of deviatoric plane within the stress space. To test its ability to describe shockwave propagation and spall failure, the new material model was implemented into the LLNL-DYNA3D code of UTHM’s. The capability of this newly constitutive model were compared against published experimental data of Plate Impact Test at 234m/s, 450m/s and 895m/s impact velocities. A good agreement is obtained between experimental and simulation in each test.

  14. High Precision Clock Bias Prediction Model in Clock Synchronization System

    Directory of Open Access Journals (Sweden)

    Zan Liu

    2016-01-01

    Full Text Available Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the first-order grey model with one variable (GM(1,1 model is proposed. In the traditional GM(1,1 model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO is used to optimize GM(1,1 model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%~76.7%.

  15. [Discussion of Chinese syndrome typing in acute hepatic failure model].

    Science.gov (United States)

    Zhang, Jin-liang; Zeng, Hui; Wang, Xian-bo

    2011-05-01

    To study Chinese syndrome typing of acute hepatic failure (AHF) mice model by screening effective formulae. Lipoplysaccharides (LPS)/D-galactosamine (D-GaIN) was intraperitoneally injected to mice to establish the AHF mice model. Yinchenhao Decoction, Huanglian Jiedu Decoction, Buzhong Yiqi Decoction, and Xijiao Dihuang Decoction were administered to model mice respectively by gastrogavage. The behavior and the survival rate were monitored. The liver function and pathological changes of liver tissues were detected. In all the tested classic recipes, the survival rate was elevated from 10% to 60% by administration of Xijiao Dihuang Decoction. Five h after modeling, the serum alanine aminotransferase (ALT) level was (183.95 +/- 52.00) U/L, and aspartate aminotransferase (AST) (235.70 +/- 34.03) U/L in Xijiao Di-huang Decoction Group, lower than those of the model control group, but with insignificant difference (ALT: 213.32 +/- 71.93 U/L; AST: 299.48 +/- 70.56 U/L, both P > 0.05). Xijiao Dihuang Decoction could obviously alleviate the liver injury. Xijiao Dihuang Decoction was an effective formula for LPS/D-GaIN induced AHF model. According to syndrome typing through formula effect, heat toxin and blood stasis syndrome dominated in the LPS/D-GalN induced AHF mice model.

  16. Development of a Predictive Model for Induction Success of Labour

    Directory of Open Access Journals (Sweden)

    Cristina Pruenza

    2018-03-01

    Full Text Available Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements or less frequently, social (elective inductions for convenience. The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn, costs (medication, hospitalization, qualified staff and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the

  17. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  18. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  19. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

  20. Modelling river bank erosion processes and mass failure mechanisms using 2-D depth averaged numerical model

    Science.gov (United States)

    Die Moran, Andres; El kadi Abderrezzak, Kamal; Tassi, Pablo; Herouvet, Jean-Michel

    2014-05-01

    Bank erosion is a key process that may cause a large number of economic and environmental problems (e.g. land loss, damage to structures and aquatic habitat). Stream bank erosion (toe erosion and mass failure) represents an important form of channel morphology changes and a significant source of sediment. With the advances made in computational techniques, two-dimensional (2-D) numerical models have become valuable tools for investigating flow and sediment transport in open channels at large temporal and spatial scales. However, the implementation of mass failure process in 2D numerical models is still a challenging task. In this paper, a simple, innovative algorithm is implemented in the Telemac-Mascaret modeling platform to handle bank failure: failure occurs whether the actual slope of one given bed element is higher than the internal friction angle. The unstable bed elements are rotated around an appropriate axis, ensuring mass conservation. Mass failure of a bank due to slope instability is applied at the end of each sediment transport evolution iteration, once the bed evolution due to bed load (and/or suspended load) has been computed, but before the global sediment mass balance is verified. This bank failure algorithm is successfully tested using two laboratory experimental cases. Then, bank failure in a 1:40 scale physical model of the Rhine River composed of non-uniform material is simulated. The main features of the bank erosion and failure are correctly reproduced in the numerical simulations, namely the mass wasting at the bank toe, followed by failure at the bank head, and subsequent transport of the mobilised material in an aggradation front. Volumes of eroded material obtained are of the same order of magnitude as the volumes measured during the laboratory tests.

  1. 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

  2. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  3. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  4. Models and analysis for multivariate failure time data

    Science.gov (United States)

    Shih, Joanna Huang

    The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the

  5. Compressive failure model for fiber composites by kink band initiation from obliquely aligned, shear-dislocated fiber breaks

    Energy Technology Data Exchange (ETDEWEB)

    Bai, J.; Phoenix, S.L. [Cornell University, Ithaca, NY (United States). Dept. of Theoretical and Applied Mechanics

    2005-04-01

    Predicting compressive failure of a unidirectional fibrous composite is a longstanding and challenging problem that we study from a new perspective. Motivated by previous modelling of tensile failure as well as experimental observations on compressive failures in single carbon fibers, we develop a new micromechanical model for the compressive failure process in unidirectional, planar composites. As the compressive load is increased, random fiber failures are assumed to occur due to statistically distributed flaws, analogous to what occurs in tension. These breaks are often shear-mode failures with slanted surfaces that induce shear dislocations, especially when they occur in small groups aligned obliquely. Our model includes interactions of dislocated and neighboring intact fibers through a system of fourth-order, differential equations governing transverse deformation, and also allows for local matrix plastic yielding and debonding from the fiber near and within the dislocation arrays. Using the Discrete Fourier Transform method, we find a 'building-block' analytical solution form, which naturally embodies local length scales of fiber microbuckling and instability. Based on the influence function, superposition approach, a computationally efficient scheme is developed to model the evolution of fiber and matrix stresses. Under increasing compressive strain the simulations show that matrix yielding and debonding crucially lead to large increases in bending strains in fibers next to small groups of obliquely aligned, dislocated breaks. From the paired locations of maximum fiber bending in flanking fibers, the triggering of an unstable kink band becomes realistic. The geometric features of the kink band, such as the fragment lengths and orientation angles, will depend on the fiber and matrix mechanical and geometric properties. In carbon fiber-polymer matrix systems our model predicts a much lower compressive failure stress than obtained from Rosen

  6. A Macaca mulatta model of fulminant hepatic failure

    Institute of Scientific and Technical Information of China (English)

    Ping Zhou; Hong Bu; Jie Xia; Gang Guo; Li Li; Yu-Jun Shi; Zi-Xing Huang; Qiang Lu; Hong-Xia Li

    2012-01-01

    AIM: To establish an appropriate primate model of fulminant hepatic failure (FHF). METHODS: We have, for the first time, established a large animal model of FHF in Macaca mulatta by intraperitoneal infusion of amatoxin and endotoxin. Clinical features, biochemical indexes, histopathology and iconography were examined to dynamically investigate the progress and outcome of the animal model. RESULTS: Our results showed that the enzymes and serum bilirubin were markedly increased and the enzyme-bilirubin segregation emerged 36 h after toxin administration. Coagulation activity was significantly decreased. Gradually deteriorated parenchymal abnormality was detected by magnetic resonance imaging (MRI) and ultrasonography at 48 h. The liver biopsy showed marked hepatocyte steatosis and massive parenchymal necrosis at 36 h and 49 h, respectively. The autopsy showed typical yellow atrophy of the liver. Hepatic encephalopathy of the models was also confirmed by hepatic coma, MRI and pathological changes of cerebral edema. The lethal effects of the extrahepatic organ dysfunction were ruled out by their biochemical indices, imaging and histopathology. CONCLUSION: We have established an appropriate large primate model of FHF, which is closely similar to clinic cases, and can be used for investigation of the mechanism of FHF and for evaluation of potential medical therapies.

  7. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  8. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  9. Failure Models and Criteria for FRP Under In-Plane or Three-Dimensional Stress States Including Shear Non-Linearity

    Science.gov (United States)

    Pinho, Silvestre T.; Davila, C. G.; Camanho, P. P.; Iannucci, L.; Robinson, P.

    2005-01-01

    A set of three-dimensional failure criteria for laminated fiber-reinforced composites, denoted LaRC04, is proposed. The criteria are based on physical models for each failure mode and take into consideration non-linear matrix shear behaviour. The model for matrix compressive failure is based on the Mohr-Coulomb criterion and it predicts the fracture angle. Fiber kinking is triggered by an initial fiber misalignment angle and by the rotation of the fibers during compressive loading. The plane of fiber kinking is predicted by the model. LaRC04 consists of 6 expressions that can be used directly for design purposes. Several applications involving a broad range of load combinations are presented and compared to experimental data and other existing criteria. Predictions using LaRC04 correlate well with the experimental data, arguably better than most existing criteria. The good correlation seems to be attributable to the physical soundness of the underlying failure models.

  10. FRESS pin failure model and its application to E-8 TREAT test

    International Nuclear Information System (INIS)

    Kalimullah.

    1979-01-01

    FRESS is a cladding rupture prediction model for an irradiated mixed-oxide LMFBR fuel pin during transient heating based only on the internal pressurization of the cladding by the fission gas released from fuel grains during the transient. The model is applied to the analysis of the hottest PNL-10-53 pin in the 7-pin E-8 TREAT test which simulates a $3/sec transient overpower. Although the uncertainties of the inputs to the temperature calculation done with the COBRA code have not been included, the uncertain input parameters to FRESS have been varied over their estimated uncertainties. The cladding rupture predictions are a few tens of milliseconds late compared to the most probable failure time detected in the test. However, these calculations seem to indicate that fisson gas pressure is a significant mechanism for causing clad rupture in this test

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. Failure Analysis of Nonvolatile Residue (NVR) Analyzer Model SP-1000

    Science.gov (United States)

    Potter, Joseph C.

    2011-01-01

    National Aeronautics and Space Administration (NASA) subcontractor Wiltech contacted the NASA Electrical Lab (NE-L) and requested a failure analysis of a Solvent Purity Meter; model SP-IOOO produced by the VerTis Instrument Company. The meter, used to measure the contaminate in a solvent to determine the relative contamination on spacecraft flight hardware and ground servicing equipment, had been inoperable and in storage for an unknown amount of time. NE-L was asked to troubleshoot the unit and make a determination on what may be required to make the unit operational. Through the use of general troubleshooting processes and the review of a unit in service at the time of analysis, the unit was found to be repairable but would need the replacement of multiple components.

  17. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  18. The prediction of epidemics through mathematical modeling.

    Science.gov (United States)

    Schaus, Catherine

    2014-01-01

    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  19. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  20. [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.

  1. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  2. Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.

    Science.gov (United States)

    Suresh, Krithika; Taylor, Jeremy M G; Spratt, Daniel E; Daignault, Stephanie; Tsodikov, Alexander

    2017-11-01

    Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness-death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time-dependent covariate for predicting death following radiation therapy. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  4. 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...

  5. 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...

  6. The prediction of necking and failure in 3 D. Sheet forming analysis using damage variable

    International Nuclear Information System (INIS)

    Brunet, M.; Sabourin, F.; Mguil-Touchal, S.

    1996-01-01

    The modeling of necking occurrence in sheet metal forming is a real challenge for the engineer concerned with processing of new geometries and materials. As fracture in metal forming is mainly due to the development of ductile damage and to represent the failure of anisotropic sheet-metals, an extension of the Gurson-Tvergaard model is presented and implemented in the context of plane-stress for shell elements. A one dimensional problem is solved and compared with the exact solution of the literature. The paper closes with a numerical and experimental study of the necking of a square cup deep-drawing using the modified Gurson's model to described the constitutive behavior of the material. Finally, a numerical necking criterion is proposed. (orig.)

  7. The prediction of necking and failure in 3 D. Sheet forming analysis using damage variable

    Energy Technology Data Exchange (ETDEWEB)

    Brunet, M. [INSA, Villeurbanne (France). Lab. de Mecanique des Solides; Sabourin, F. [INSA, Villeurbanne (France). Lab. de Mecanique des Solides; Mguil-Touchal, S. [INSA, Villeurbanne (France). Lab. de Mecanique des Solides

    1996-10-01

    The modeling of necking occurrence in sheet metal forming is a real challenge for the engineer concerned with processing of new geometries and materials. As fracture in metal forming is mainly due to the development of ductile damage and to represent the failure of anisotropic sheet-metals, an extension of the Gurson-Tvergaard model is presented and implemented in the context of plane-stress for shell elements. A one dimensional problem is solved and compared with the exact solution of the literature. The paper closes with a numerical and experimental study of the necking of a square cup deep-drawing using the modified Gurson`s model to described the constitutive behavior of the material. Finally, a numerical necking criterion is proposed. (orig.).

  8. Modeling combined tension-shear failure of ductile materials

    International Nuclear Information System (INIS)

    Partom, Y

    2014-01-01

    Failure of ductile materials is usually expressed in terms of effective plastic strain. Ductile materials can fail by two different failure modes, shear failure and tensile failure. Under dynamic loading shear failure has to do with shear localization and formation of adiabatic shear bands. In these bands plastic strain rate is very high, dissipative heating is extensive, and shear strength is lost. Shear localization starts at a certain value of effective plastic strain, when thermal softening overcomes strain hardening. Shear failure is therefore represented in terms of effective plastic strain. On the other hand, tensile failure comes about by void growth under tension. For voids in a tension field there is a threshold state of the remote field for which voids grow spontaneously (cavitation), and the material there fails. Cavitation depends on the remote field stress components and on the flow stress. In this way failure in tension is related to shear strength and to failure in shear. Here we first evaluate the cavitation threshold for different remote field situations, using 2D numerical simulations with a hydro code. We then use the results to compute examples of rate dependent tension-shear failure of a ductile material.

  9. Analysis of lower head failure with simplified models and a finite element code

    Energy Technology Data Exchange (ETDEWEB)

    Koundy, V. [CEA-IPSN-DPEA-SEAC, Service d' Etudes des Accidents, Fontenay-aux-Roses (France); Nicolas, L. [CEA-DEN-DM2S-SEMT, Service d' Etudes Mecaniques et Thermiques, Gif-sur-Yvette (France); Combescure, A. [INSA-Lyon, Lab. Mecanique des Solides, Villeurbanne (France)

    2001-07-01

    The objective of the OLHF (OECD lower head failure) experiments is to characterize the timing, mode and size of lower head failure under high temperature loading and reactor coolant system pressure due to a postulated core melt scenario. Four tests have been performed at Sandia National Laboratories (USA), in the frame of an OECD project. The experimental results have been used to develop and validate predictive analysis models. Within the framework of this project, several finite element calculations were performed. In parallel, two simplified semi-analytical methods were developed in order to get a better understanding of the role of various parameters on the creep phenomenon, e.g. the behaviour of the lower head material and its geometrical characteristics on the timing, mode and location of failure. Three-dimensional modelling of crack opening and crack propagation has also been carried out using the finite element code Castem 2000. The aim of this paper is to present the two simplified semi-analytical approaches and to report the status of the 3D crack propagation calculations. (authors)

  10. Enhancing pavement performance prediction models for the Illinois Tollway System

    Directory of Open Access Journals (Sweden)

    Laxmikanth Premkumar

    2016-01-01

    Full Text Available Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway with over 2000 lane miles of pavement utilizes the condition rating survey (CRS methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT are used by the Tollway to predict the future condition of its network. The model projects future CRS ratings based on pavement type, thickness, traffic, pavement age and current CRS rating. However, with time and inclusion of newer pavement types there was a need to calibrate the existing pavement performance models, as well as, develop models for newer pavement types.This study presents the results of calibrating the existing models, and developing new models for the various pavement types in the Illinois Tollway network. The predicted future condition of the pavements is used in estimating its remaining service life to failure, which is of immediate use in recommending future maintenance and rehabilitation requirements for the network. Keywords: Pavement performance models, Remaining life, Pavement management

  11. Longitudinal modeling to predict vital capacity in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Jahandideh, Samad; Taylor, Albert A; Beaulieu, Danielle; Keymer, Mike; Meng, Lisa; Bian, Amy; Atassi, Nazem; Andrews, Jinsy; Ennist, David L

    2018-05-01

    Death in amyotrophic lateral sclerosis (ALS) patients is related to respiratory failure, which is assessed in clinical settings by measuring vital capacity. We developed ALS-VC, a modeling tool for longitudinal prediction of vital capacity in ALS patients. A gradient boosting machine (GBM) model was trained using the PRO-ACT (Pooled Resource Open-access ALS Clinical Trials) database of over 10,000 ALS patient records. We hypothesized that a reliable vital capacity predictive model could be developed using PRO-ACT. The model was used to compare FVC predictions with a 30-day run-in period to predictions made from just baseline. The internal root mean square deviations (RMSD) of the run-in and baseline models were 0.534 and 0.539, respectively, across the 7L FVC range captured in PRO-ACT. The RMSDs of the run-in and baseline models using an unrelated, contemporary external validation dataset (0.553 and 0.538, respectively) were comparable to the internal validation. The model was shown to have similar accuracy for predicting SVC (RMSD = 0.562). The most important features for both run-in and baseline models were "Baseline forced vital capacity" and "Days since baseline." We developed ALS-VC, a GBM model trained with the PRO-ACT ALS dataset that provides vital capacity predictions generalizable to external datasets. The ALS-VC model could be helpful in advising and counseling patients, and, in clinical trials, it could be used to generate virtual control arms against which observed outcomes could be compared, or used to stratify patients into slowly, average, and rapidly progressing subgroups.

  12. Applicability of out-of-pile fretting wear tests to in-reactor fretting wear-induced failure time prediction

    Science.gov (United States)

    Kim, Kyu-Tae

    2013-02-01

    In order to investigate whether or not the grid-to-rod fretting wear-induced fuel failure will occur for newly developed spacer grid spring designs for the fuel lifetime, out-of-pile fretting wear tests with one or two fuel assemblies are to be performed. In this study, the out-of-pile fretting wear tests were performed in order to compare the potential for wear-induced fuel failure in two newly-developed, Korean PWR spacer grid designs. Lasting 20 days, the tests simulated maximum grid-to-rod gap conditions and the worst flow induced vibration effects that might take place over the fuel life time. The fuel rod perforation times calculated from the out-of-pile tests are greater than 1933 days for 2 μm oxidized fuel rods with a 100 μm grid-to-rod gap, whereas those estimated from in-reactor fretting wear failure database may be about in the range of between 60 and 100 days. This large discrepancy in fuel rod perforation may occur due to irradiation-induced cladding oxide microstructure changes on the one hand and a temperature gradient-induced hydrogen content profile across the cladding metal region on the other hand, which may accelerate brittleness in the grid-contacting cladding oxide and metal regions during the reactor operation. A three-phase grid-to-rod fretting wear model is proposed to simulate in-reactor fretting wear progress into the cladding, considering the microstructure changes of the cladding oxide and the hydrogen content profile across the cladding metal region combined with the temperature gradient. The out-of-pile tests cannot be directly applicable to the prediction of in-reactor fretting wear-induced cladding perforations but they can be used only for evaluating a relative wear resistance of one grid design against the other grid design.

  13. A Dynamic Approach to Modeling Dependence Between Human Failure Events

    Energy Technology Data Exchange (ETDEWEB)

    Boring, Ronald Laurids [Idaho National Laboratory

    2015-09-01

    In practice, most HRA methods use direct dependence from THERP—the notion that error be- gets error, and one human failure event (HFE) may increase the likelihood of subsequent HFEs. In this paper, we approach dependence from a simulation perspective in which the effects of human errors are dynamically modeled. There are three key concepts that play into this modeling: (1) Errors are driven by performance shaping factors (PSFs). In this context, the error propagation is not a result of the presence of an HFE yielding overall increases in subsequent HFEs. Rather, it is shared PSFs that cause dependence. (2) PSFs have qualities of lag and latency. These two qualities are not currently considered in HRA methods that use PSFs. Yet, to model the effects of PSFs, it is not simply a matter of identifying the discrete effects of a particular PSF on performance. The effects of PSFs must be considered temporally, as the PSFs will have a range of effects across the event sequence. (3) Finally, there is the concept of error spilling. When PSFs are activated, they not only have temporal effects but also lateral effects on other PSFs, leading to emergent errors. This paper presents the framework for tying together these dynamic dependence concepts.

  14. Agent autonomy approach to probabilistic physics-of-failure modeling of complex dynamic systems with interacting failure mechanisms

    Science.gov (United States)

    Gromek, Katherine Emily

    A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007.

  15. Filter design for failure detection and isolation in the presence of modeling errors and disturbances

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    1996-01-01

    The design problem of filters for robust failure detection and isolation, (FDI) is addressed in this paper. The failure detection problem will be considered with respect to both modeling errors and disturbances. Both an approach based on failure detection observers as well as an approach based...

  16. Filter Design for Failure Detection and Isolation in the Presence of Modeling Erros and Disturbances

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1996-01-01

    The design problem of filters for robust Failure Detectionand Isolation, (FDI) is addressed in this paper. The failure detectionproblem will be considered with respect to both modeling errors anddisturbances. Both an approach based on failure detection observes aswell as an approach based...

  17. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  18. Cladding failure model III (CFM III). A simple model for iodine induced stress corrosion cracking of zirconium-lined barrier and standard zircaloy cladding

    International Nuclear Information System (INIS)

    Tasooji, A.; Miller, A.K.

    1984-01-01

    A previously developed unified model (SCCIG*) for predicting iodine induced SCC in standard Zircaloy cladding was modified recently into the ''SCCIG-B'' model which predicts the stress corrosion cracking behaviour of zirconium lined barrier cladding. Several published papers have presented the capability of these models for predicting various observed behaviours related to SCC. A closed form equation, called Cladding Failure Model III (CMFIII), has been derived from the SCCIG-B model. CFMIII takes the form of an explicit equation for the radial crack growth rate dc/dt as a function of hoop strain, crack depth, temperature, and surface iodine concentration in irradiated cladding (both barrier and standard Zircaloy). CMFIII has approximately the same predictive capabilities as the physically based SCCIG and/or SCCIG-B models but is computationally faster and more convenient and can be easily utilized in fuel performance codes for predicting the behaviour of barrier and standard claddings in reactor operations. (author)

  19. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  20. Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

    Directory of Open Access Journals (Sweden)

    Shanee Honig

    2018-06-01

    Full Text Available While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI, and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI, human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP, that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1 communicating failures, (2 perception and comprehension of failures, and (3 solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a

  1. 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.

  2. Modeling of container failure and radionuclide release from a geologic nuclear waste repository

    International Nuclear Information System (INIS)

    Kim, Chang Lak; Kim, Jhin Wung; Choi, Kwang Sub; Cho, Chan Hee

    1989-02-01

    Generally, two processes are involved in leaching and dissolution; (1) chemical reactions and (2) mass transfer by diffusion. The chemical reaction controls the dissolution rates only during the early stage of exposure to groundwater. The exterior-field mass transfer may control the long-term dissolution rates from the waste solid in a geologic repository. Masstransfer analyses rely on detailed and careful application of the governing equations that describe the mechanistic processes of transport of material between and within phases. We develop analytical models to predict the radionuclide release rate into the groundwater with five different approaches: a measurement-based model, a diffusion model, a kinetics model, a diffusion-and-kinetics model, and a modified diffusion model. We also collected experimental leaching data for a partial validation of the radionuclide release model based on the mass transfer theory. Among various types of corrosions, pitting is the most significant because of its rapid growth. The failure time of the waste container, which also can be interpreted as the containment time, is a milestone of the performance of a repository. We develop analytical models to predict the pit growth rate on the container surface with three different approaches: an experimental method, a statistical method, and a mathematical method based on the diffusion theory. (Author)

  3. A ductile fracture mechanics methodology for predicting pressure vessel and piping failure

    International Nuclear Information System (INIS)

    Landes, J.D.; Zhou, Z.

    1991-01-01

    This paper reports on a ductile fracture methodology based on one used more generally for the prediction of fracture behavior that was applied to the prediction of fracture behavior in pressure vessel and piping components. The model uses the load versus displacement record from a fracture toughness test to develop inputs for predicting the behavior of the structural component. The principle of load separation is used to convert the test record into two pieces of information, calibration functions which describe the structural deformation behavior and fracture toughness which describes the response of a crack-like flaw to the loading. These calibration functions and fracture toughness values which relate to the test specimen are then transformed to those appropriate to the structure. Often in this step computation procedures could be used but are not always necessary. The calibration functions and fracture for the structure are recombined to predict a load versus displacement behavior for the structure. The input for the model was generated from tests of compact specimen geometries; this geometry is often used for fracture toughness testing. The predictions were done for five model structures

  4. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  5. 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.

  6. 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.

  7. Successes and failures of the constituent quark model

    International Nuclear Information System (INIS)

    Lipkin, H.J.

    1982-01-01

    Our approach considers the model as a possible bridge between QCD and the experimental data and examines its predictions to see where these succeed and where they fail. We also attempt to improve the model by looking for additional simple assumptions which give better fits to the experimental data. But we avoid complicated models with too many ad hoc assumptions and too many free parameters; these can fit everything but teach us nothing. We define our constituent quark model by analogy with the constituent electron model of the atom and the constituent nucleon model of the nucleus. In the same way that an atom is assumed to consist only of constituent electrons and a central Coulomb field and a nucleus is assumed to consist only of constituent nucleons hadrons are assumed to consist only of their constituent valence quarks with no bag, no glue, no ocean, nor other constituents. Although these constituent models are oversimplified and neglect other constituents we push them as far as we can. Atomic physics has photons and vacuum polarization as well as constituent electrons, but the constituent model is adequate for calculating most features of the spectrum when finer details like the Lamb shift are neglected. 54 references

  8. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  9. Probabilistic common cause failure modeling for auxiliary feedwater system after the introduction of flood barriers

    International Nuclear Information System (INIS)

    Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi

    2013-01-01

    Causal inference is capable of assessing common cause failure (CCF) events from the viewpoint of causes' risk significance. Authors proposed the alpha decomposition method for probabilistic CCF analysis, in which the classical alpha factor model and causal inference are integrated to conduct a quantitative assessment of causes' CCF risk significance. The alpha decomposition method includes a hybrid Bayesian network for revealing the relationship between component failures and potential causes, and a regression model in which CCF parameters (global alpha factors) are expressed by explanatory variables (causes' occurrence frequencies) and parameters (decomposed alpha factors). This article applies this method and associated databases needed to predict CCF parameters of auxiliary feedwater (AFW) system when defense barriers against internal flood are introduced. There is scarce operation data for functionally modified safety systems and the utilization of generic CCF databases is of unknown uncertainty. The alpha decomposition method has the potential of analyzing the CCF risk of modified AFW system reasonably based on generic CCF databases. Moreover, the sources of uncertainty in parameter estimation can be studied. An example is presented to demonstrate the process of applying Bayesian inference in the alpha decomposition process. The results show that the system-specific posterior distributions for CCF parameters can be predicted. (author)

  10. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  11. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  12. 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.

  13. 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.

  14. 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

  15. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  16. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

  17. Numerical Predictions of Damage and Failure in Carbon Fiber Reinforced Laminates Using a Thermodynamically-Based Work Potential Theory

    Science.gov (United States)

    Pineda, Evan Jorge; Waas, Anthony M.

    2013-01-01

    A thermodynamically-based work potential theory for modeling progressive damage and failure in fiber-reinforced laminates is presented. The current, multiple-internal state variable (ISV) formulation, referred to as enhanced Schapery theory (EST), utilizes separate ISVs for modeling the effects of damage and failure. Consistent characteristic lengths are introduced into the formulation to govern the evolution of the failure ISVs. Using the stationarity of the total work potential with respect to each ISV, a set of thermodynamically consistent evolution equations for the ISVs are derived. The theory is implemented into a commercial finite element code. The model is verified against experimental results from two laminated, T800/3900-2 panels containing a central notch and different fiber-orientation stacking sequences. Global load versus displacement, global load versus local strain gage data, and macroscopic failure paths obtained from the models are compared against the experimental results.

  18. Using Probablilistic Risk Assessment to Model Medication System Failures in Long-Term Care Facilities

    National Research Council Canada - National Science Library

    Comden, Sharon C; Marx, David; Murphy-Carley, Margaret; Hale, Misti

    2005-01-01

    .... Discussion: The models provide contextual maps of the errors and behaviors that lead to medication delivery system failures, including unanticipated risks associated with regulatory practices and common...

  19. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

    OpenAIRE

    David Ebert

    2006-01-01

    One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

  20. Predicting Error Bars for QSAR Models

    International Nuclear Information System (INIS)

    Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Mueller, Klaus-Robert

    2007-01-01

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D 7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches

  1. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  2. Multi-model analysis in hydrological prediction

    Science.gov (United States)

    Lanthier, M.; Arsenault, R.; Brissette, F.

    2017-12-01

    Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

  3. A robust Bayesian approach to modeling epistemic uncertainty in common-cause failure models

    International Nuclear Information System (INIS)

    Troffaes, Matthias C.M.; Walter, Gero; Kelly, Dana

    2014-01-01

    In a standard Bayesian approach to the alpha-factor model for common-cause failure, a precise Dirichlet prior distribution models epistemic uncertainty in the alpha-factors. This Dirichlet prior is then updated with observed data to obtain a posterior distribution, which forms the basis for further inferences. In this paper, we adapt the imprecise Dirichlet model of Walley to represent epistemic uncertainty in the alpha-factors. In this approach, epistemic uncertainty is expressed more cautiously via lower and upper expectations for each alpha-factor, along with a learning parameter which determines how quickly the model learns from observed data. For this application, we focus on elicitation of the learning parameter, and find that values in the range of 1 to 10 seem reasonable. The approach is compared with Kelly and Atwood's minimally informative Dirichlet prior for the alpha-factor model, which incorporated precise mean values for the alpha-factors, but which was otherwise quite diffuse. Next, we explore the use of a set of Gamma priors to model epistemic uncertainty in the marginal failure rate, expressed via a lower and upper expectation for this rate, again along with a learning parameter. As zero counts are generally less of an issue here, we find that the choice of this learning parameter is less crucial. Finally, we demonstrate how both epistemic uncertainty models can be combined to arrive at lower and upper expectations for all common-cause failure rates. Thereby, we effectively provide a full sensitivity analysis of common-cause failure rates, properly reflecting epistemic uncertainty of the analyst on all levels of the common-cause failure model

  4. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  16. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  17. Antithrombin III in animal models of sepsis and organ failure.

    Science.gov (United States)

    Dickneite, G

    1998-01-01

    Antithrombin III (AT III) is the physiological inhibitor of thrombin and other serine proteases of the clotting cascade. In the development of sepsis, septic shock and organ failure, the plasma levels of AT III decrease considerably, suggesting the concept of a substitution therapy with the inhibitor. A decrease of AT III plasma levels might also be associated with other pathological disorders like trauma, burns, pancreatitis or preclampsia. Activation of coagulation and consumption of AT III is the consequence of a generalized inflammation called SIRS (systemic inflammatory response syndrome). The clotting cascade is also frequently activated after organ transplantation, especially if organs are grafted between different species (xenotransplantation). During the past years AT III has been investigated in numerous corresponding disease models in different animal species which will be reviewed here. The bulk of evidence suggests, that AT III substitution reduces morbidity and mortality in the diseased animals. While gaining more experience with AT III, the concept of substitution therapy to maximal baseline plasma levels (100%) appears to become insufficient. Evidence from clinical and preclinical studies now suggests to adjust the AT III plasma levels to about 200%, i.e., doubling the normal value. During the last few years several authors proposed that AT III might not only be an anti-thrombotic agent, but to have in addition an anti-inflammatory effect.

  18. Intuitionistic fuzzy-based model for failure detection.

    Science.gov (United States)

    Aikhuele, Daniel O; Turan, Faiz B M

    2016-01-01

    In identifying to-be-improved product component(s), the customer/user requirements which are mainly considered, and achieved through customer surveys using the quality function deployment (QFD) tool, often fail to guarantee or cover aspects of the product reliability. Even when they do, there are always many misunderstandings. To improve the product reliability and quality during product redesigning phase and to create that novel product(s) for the customers, the failure information of the existing product, and its component(s) should ordinarily be analyzed and converted to appropriate design knowledge for the design engineer. In this paper, a new intuitionistic fuzzy multi-criteria decision-making method has been proposed. The new approach which is based on an intuitionistic fuzzy TOPSIS model uses an exponential-related function for the computation of the separation measures from the intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) of alternatives. The proposed method has been applied to two practical case studies, and the result from the different cases has been compared with some similar computational approaches in the literature.

  19. Spent fuel: prediction model development

    International Nuclear Information System (INIS)

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.

    1979-07-01

    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  20. Navy Recruit Attrition Prediction Modeling

    Science.gov (United States)

    2014-09-01

    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  1. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  2. Predictive Models and Computational Toxicology (II IBAMTOX)

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  3. Finding furfural hydrogenation catalysts via predictive modelling

    NARCIS (Netherlands)

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.

    2010-01-01

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes

  4. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  5. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic

  6. 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).

  7. 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 ...

  8. Predicting short-term mortality in advanced decompensated heart failure - role of the updated acute decompensated heart failure/N-terminal pro-B-type natriuretic Peptide risk score.

    Science.gov (United States)

    Scrutinio, Domenico; Ammirati, Enrico; Passantino, Andrea; Guida, Pietro; D'Angelo, Luciana; Oliva, Fabrizio; Ciccone, Marco Matteo; Iacoviello, Massimo; Dentamaro, Ilaria; Santoro, Daniela; Lagioia, Rocco; Sarzi Braga, Simona; Guzzetti, Daniela; Frigerio, Maria

    2015-01-01

    The first few months after admission are the most vulnerable period in patients with acute decompensated heart failure (ADHF). We assessed the association of the updated ADHF/N-terminal pro-B-type natriuretic peptide (NT-proBNP) risk score with 90-day and in-hospital mortality in 701 patients admitted with advanced ADHF, defined as severe symptoms of worsening HF, severely depressed left ventricular ejection fraction, and the need for i.v. diuretic and/or inotropic drugs. A total of 15.7% of the patients died within 90 days of admission and 5.2% underwent ventricular assist device (VAD) implantation or urgent heart transplantation (UHT). The C-statistic of the ADHF/NT-proBNP risk score for 90-day mortality was 0.810 (95% CI: 0.769-0.852). Predicted and observed mortality rates were in close agreement. When the composite outcome of death/VAD/UHT at 90 days was considered, the C-statistic decreased to 0.741. During hospitalization, 7.6% of the patients died. The C-statistic for in-hospital mortality was 0.815 (95% CI: 0.761-0.868) and Hosmer-Lemeshow χ(2)=3.71 (P=0.716). The updated ADHF/NT-proBNP risk score outperformed the Acute Decompensated Heart Failure National Registry, the Organized Program to Initiate Lifesaving Treatment in Patients Hospitalized for Heart Failure, and the American Heart Association Get with the Guidelines Program predictive models. Updated ADHF/NT-proBNP risk score is a valuable tool for predicting short-term mortality in severe ADHF, outperforming existing inpatient predictive models.

  9. Formability prediction for AHSS materials using damage models

    Science.gov (United States)

    Amaral, R.; Santos, Abel D.; José, César de Sá; Miranda, Sara

    2017-05-01

    Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches.

  10. Formability prediction for AHSS materials using damage models

    International Nuclear Information System (INIS)

    Amaral, R.; Miranda, Sara; Santos, Abel D.; José, César de Sá

    2017-01-01

    Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches. (paper)

  11. [Surgical model of chronic renal failure: study in rabbits].

    Science.gov (United States)

    Costa, Andrei Ferreira Nicolau da; Pereira, Lara de Paula Miranda; Ferreira, Manoel Luiz; Silva, Paulo Cesar; Chagar, Vera Lucia Antunes; Schanaider, Alberto

    2009-02-01

    To establish a model of chronic renal failure in rabbits, with perspectives of its use for therapeutic and repairing actions. Nineteen males, adults rabbits (New Zealand) randomly distributed into three groups were used: Group 1 - Control (n =5); Group 2-Sham (n =7); and Group 3 - Experimental (n =7). They were anaesthetized by using intramuscular Cetamine, Diazepam and Fentanyl followed by Sevorane with vaporizer device. In Group 3, a bipolar left nephrectomy was carried out and after four weeks, it was also done a right nephrectomy. All the samples of the renal tissue were weighed. The Group 2 was only submitted to both abdominal laparotomies, without nephrectomy. Biochemical evaluations, with urea, creatinina, sodium and potassium measurement; abdominal ultrasound scan; scintigraphy and histological analysis were performed in all animals. In group 3 there was a progressive increase of urea (p=0.0001), creatinine (p=0.0001), sodium (p = 0,0002) and potassium (p=0,0003). The comparison of these results with those one of the Groups 1 and 2, in all intervals, revealed blood rising with statistical significant level (p < 0,05). In Group 3, the ultrasound scan identified an increasing of the left kidney size, after 16 weeks and at the 4th week the scintigraphy confirmed the loss of 75% of the left renal mass. In the same group, the histological evaluation showed subcapsular and intersticial fibrosis and also tubular regeneration. The experimental model of IRC is feasible, with animal's survival in middle term which allows the use of this interval like a therapeutic window for testing different approaches in order to repair the kidney damages.

  12. Real Time Fire Reconnaissance Satellite Monitoring System Failure Model

    Science.gov (United States)

    Nino Prieto, Omar Ariosto; Colmenares Guillen, Luis Enrique

    2013-09-01

    In this paper the Real Time Fire Reconnaissance Satellite Monitoring System is presented. This architecture is a legacy of the Detection System for Real-Time Physical Variables which is undergoing a patent process in Mexico. The methodologies for this design are the Structured Analysis for Real Time (SA- RT) [8], and the software is carried out by LACATRE (Langage d'aide à la Conception d'Application multitâche Temps Réel) [9,10] Real Time formal language. The system failures model is analyzed and the proposal is based on the formal language for the design of critical systems and Risk Assessment; AltaRica. This formal architecture uses satellites as input sensors and it was adapted from the original model which is a design pattern for physical variation detection in Real Time. The original design, whose task is to monitor events such as natural disasters and health related applications, or actual sickness monitoring and prevention, as the Real Time Diabetes Monitoring System, among others. Some related work has been presented on the Mexican Space Agency (AEM) Creation and Consultation Forums (2010-2011), and throughout the International Mexican Aerospace Science and Technology Society (SOMECYTA) international congress held in San Luis Potosí, México (2012). This Architecture will allow a Real Time Fire Satellite Monitoring, which will reduce the damage and danger caused by fires which consumes the forests and tropical forests of Mexico. This new proposal, permits having a new system that impacts on disaster prevention, by combining national and international technologies and cooperation for the benefit of humankind.

  13. Reliability modelling for wear out failure period of a single unit system

    OpenAIRE

    Arekar, Kirti; Ailawadi, Satish; Jain, Rinku

    2012-01-01

    The present paper deals with two time-shifted density models for wear out failure period of a single unit system. The study, considered the time-shifted Gamma and Normal distributions. Wear out failures occur as a result of deterioration processes or mechanical wear and its probability of occurrence increases with time. A failure rate as a function of time deceases in an early failure period and it increases in wear out period. Failure rates for time shifted distributions and expression for m...

  14. 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.

  15. 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.

  16. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  17. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  18. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

  19. Inflammation, Self-Regulation, and Health: An Immunologic Model of Self-Regulatory Failure.

    Science.gov (United States)

    Shields, Grant S; Moons, Wesley G; Slavich, George M

    2017-07-01

    Self-regulation is a fundamental human process that refers to multiple complex methods by which individuals pursue goals in the face of distractions. Whereas superior self-regulation predicts better academic achievement, relationship quality, financial and career success, and lifespan health, poor self-regulation increases a person's risk for negative outcomes in each of these domains and can ultimately presage early mortality. Given its centrality to understanding the human condition, a large body of research has examined cognitive, emotional, and behavioral aspects of self-regulation. In contrast, relatively little attention has been paid to specific biologic processes that may underlie self-regulation. We address this latter issue in the present review by examining the growing body of research showing that components of the immune system involved in inflammation can alter neural, cognitive, and motivational processes that lead to impaired self-regulation and poor health. Based on these findings, we propose an integrated, multilevel model that describes how inflammation may cause widespread biobehavioral alterations that promote self-regulatory failure. This immunologic model of self-regulatory failure has implications for understanding how biological and behavioral factors interact to influence self-regulation. The model also suggests new ways of reducing disease risk and enhancing human potential by targeting inflammatory processes that affect self-regulation.

  20. 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.

  1. 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.

  2. 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

  3. Modeling Marrow Failure and MDS for Novel Therapeutics

    Science.gov (United States)

    2017-03-01

    syndrome (MDS) and leukemia is also markedly elevated in patients with inherited marrow failure syndromes compared to age-matched controls. Prognosis of...Novel Therapeutics W81XWH-16-1-0054 1. Introduction Clonal evolution is a potentially life threatening long-term complication of inherited and...The risk of early progression to myelodysplastic syndrome (MDS) and leukemia is also markedly elevated in patients with inherited marrow failure

  4. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  5. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  6. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  7. Using recurrent neural network models for early detection of heart failure onset.

    Science.gov (United States)

    Choi, Edward; Schuetz, Andy; Stewart, Walter F; Sun, Jimeng

    2017-03-01

    We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  8. Early experiences building a software quality prediction model

    Science.gov (United States)

    Agresti, W. W.; Evanco, W. M.; Smith, M. C.

    1990-01-01

    Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

  9. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  10. 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

  11. ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction

    International Nuclear Information System (INIS)

    Toledo, Maria Luíza Guerra de; Freitas, Marta A.; Colosimo, Enrico A.; Gilardoni, Gustavo L.

    2015-01-01

    An appropriate maintenance policy is essential to reduce expenses and risks related to equipment failures. A fundamental aspect to be considered when specifying such policies is to be able to predict the reliability of the systems under study, based on a well fitted model. In this paper, the classes of models Arithmetic Reduction of Age and Arithmetic Reduction of Intensity are explored. Likelihood functions for such models are derived, and a graphical method is proposed for model selection. A real data set involving failures in trucks used by a Brazilian mining is analyzed considering models with different memories. Parameters, namely, shape and scale for Power Law Process, and the efficiency of repair were estimated for the best fitted model. Estimation of model parameters allowed us to derive reliability estimators to predict the behavior of the failure process. These results are a valuable information for the mining company and can be used to support decision making regarding preventive maintenance policy. - Highlights: • Likelihood functions for imperfect repair models are derived. • A goodness-of-fit technique is proposed as a tool for model selection. • Failures in trucks owned by a Brazilian mining are modeled. • Estimation allowed deriving reliability predictors to forecast the future failure process of the trucks

  12. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  13. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  14. 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.

  15. 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

  16. Kansas City Cardiomyopathy Questionnaire Utility in Prediction of 30-Day Readmission Rate in Patients with Chronic Heart Failure

    Directory of Open Access Journals (Sweden)

    Shengchuan Dai

    2016-01-01

    Full Text Available Background. Heart failure (HF is one of the most common diagnoses associated with hospital readmission. We designed this prospective study to evaluate whether Kansas City Cardiomyopathy Questionnaire (KCCQ score is associated with 30-day readmission in patients hospitalized with decompensated HF. Methods and Results. We enrolled 240 patients who met the study criteria. Forty-eight (20% patients were readmitted for decompensated HF within thirty days of hospital discharge, and 192 (80% patients were not readmitted. Compared to readmitted patients, nonreadmitted patients had a higher average KCCQ score (40.8 versus 32.6, P = 0.019 before discharge. Multivariate analyses showed that a high KCCQ score was associated with low HF readmission rate (adjusted OR = 0.566, P = 0.022. The c-statistic for the base model (age + gender was 0.617. The combination of home medication and lab tests on the base model resulted in an integrated discrimination improvement (IDI increase of 3.9%. On that basis, the KCQQ further increased IDI of 2.7%. Conclusions. The KCCQ score determined before hospital discharge was significantly associated with 30-day readmission rate in patients with HF, which may provide a clinically useful measure and could significantly improve readmission prediction reliability when combined with other clinical components.

  17. Degradation Prediction Model Based on a Neural Network with Dynamic Windows

    Science.gov (United States)

    Zhang, Xinghui; Xiao, Lei; Kang, Jianshe

    2015-01-01

    Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failure condition monitoring data, i.e., from normal to failure. Only a certain number of condition indicators in certain period can be used to estimate RUL. In addition, some existing prediction methods have problems which block RUL estimation due to poor extrapolability. The predicted value converges to a certain constant or fluctuates in certain range. Moreover, the fluctuant condition features also have bad effects on prediction. In order to solve these dilemmas, this paper proposes a RUL prediction model based on neural network with dynamic windows. This model mainly consists of three steps: window size determination by increasing rate, change point detection and rolling prediction. The proposed method has two dominant strengths. One is that the proposed approach does not need to assume the degradation trajectory is subject to a certain distribution. The other is it can adapt to variation of degradation indicators which greatly benefits RUL prediction. Finally, the performance of the proposed RUL prediction model is validated by real field data and simulation data. PMID:25806873

  18. Predictive potential of macrophage migration inhibitory factor (MIF) in patients with heart failure with preserved ejection fraction (HFpEF).

    Science.gov (United States)

    Luedike, Peter; Alatzides, Georgios; Papathanasiou, Maria; Heisler, Martin; Pohl, Julia; Lehmann, Nils; Rassaf, Tienush

    2018-05-04

    Prognostication in heart failure with preserved ejection fraction (HFpEF) is challenging and novel biomarkers are urgently needed. Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine that plays a crucial role in cardiovascular and various inflammatory diseases. Whether MIF is involved in HFpEF is unknown. Sixty-two patients with HFpEF were enrolled and followed up for 180 days. MIF plasma levels as well as natriuretic peptide (NP) levels were assessed. High MIF levels significantly predicted the combined end-point of all-cause death or hospitalization at 180 days in the univariate analysis (HR 2.41, 95% CI 1.12-5.19, p = 0.025) and after adjustment for relevant covariates in a Cox proportional hazard regression model (HR 2.35, 95% CI 1.05-5.27, p = 0.0374). Furthermore, MIF levels above the median were associated with higher pulmonary artery systolic pressure (PASP) as assessed by echocardiography (PASP 31 mmHg vs 48 mmHg in the low- and high-MIF group, respectively, p = 0.017). NPs significantly correlated with MIF in HFpEF patients (BNP p = 0.011; r = 0.32; NT-proBNP p = 0.027; r = 0.28). MIF was associated with clinical outcomes and might be involved in the pathophysiology of pulmonary hypertension in patients with HFpEF. These first data on MIF in HFpEF should stimulate further research to elucidate the role of this cytokine in heart failure. Trial registration NCT03232671.

  19. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-09-10

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

  20. [Refractory heart failure. Models of hospital, ambulatory, and home management].

    Science.gov (United States)

    Oliva, Fabrizio; Alunni, Gianfranco

    2002-08-01

    Chronic heart failure is an enormous and growing public health problem and is reaching epidemic proportions. Its economic impact is dramatic; two thirds of expenses are for hospitalizations and relatively little is being spent for medications and outpatient visits. Most of the hospitalizations, deaths and costs are incurred by a relatively small minority of patients who may be described as having "complex", "advanced", "refractory" or "end-stage" heart failure; however, in essence they are patients who have severe symptoms and/or recurrent hospitalizations and/or emergency department visits despite maximal oral therapy. Many of the recommendations regarding the management of these patients are based more on experience than on evidence from controlled trials. This, because such patients require an individualized therapy which limits their inclusion in large trials and because support is less easily available when testing specific strategies than when testing specific agents. Improving the treatment of this group of patients by optimizing their medical regimen, aggressive monitoring and providing early intervention to avert heart failure can reduce their morbidity, mortality and costs of care. Refractory heart failure is not a single disease and it is extremely unlikely that all patients should be treated in a similar manner; before selecting the appropriate therapy, the clinician must categorize and profile the patient. The first step should be a re-evaluation of the previous treatment because many patients are treated suboptimally. It is also important to identify reversible or precipitating factors. For patients with advanced heart failure, the initial goal of therapy is to improve symptoms; the next goal is to maintain the improvement and to prevent later deterioration. The appropriate treatment plan will reflect the presence of comorbidities, the patients' history regarding previous responses to therapy, their own expectations with regard to daily life. The most

  1. Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

    Science.gov (United States)

    Hao, Hongxia; Zhou, Zhiguo; Li, Shulong; Maquilan, Genevieve; Folkert, Michael R.; Iyengar, Puneeth; Westover, Kenneth D.; Albuquerque, Kevin; Liu, Fang; Choy, Hak; Timmerman, Robert; Yang, Lin; Wang, Jing

    2018-05-01

    Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine classifier in comparison with intensity, geometry, gray level co-occurrence matrix-based texture, neighborhood gray tone difference matrix-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and AUC. Collectively, the shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC cohorts.

  2. Experimental program for physics-of-failure modeling of electrolytic capacitors towards prognostics and health management

    International Nuclear Information System (INIS)

    Rana, Y.S.; Banerjee, Shantanab; Singh, Tej; Varde, P.V.

    2017-01-01

    Prognostics and Health Management (PHM) is a method used for predicting reliability of a component or system by assessing its current health and future operating conditions. A physics-of-failure (PoF)-based program on PHM for reliability prediction has been initiated at our institute. As part of the program, we aim at developing PoF-based models for degradation of electronic components and their experimental validation. In this direction, a database on existing PoF models for different electronic components has been prepared. We plan to experimentally determine the model constants and propose suitable methodology for PHM. Electrolytic capacitors are one of the most common passive components which find their applications in devices such as power supplies in aircrafts and printed circuit boards (PCBs) for regulation and protection of a nuclear reactor. Experimental studies have established that electrolytic capacitors degrade under electrical and thermal stress and tend to fail before their anticipated useful life at normal operating conditions. Equivalent series resistance (ESR) and capacitance (C) are the two main parameters used for monitoring health of such capacitors. In this paper, we present an experimental program for thermal and electrical overstress studies towards degradation models for electrolytic capacitors. (author)

  3. A multiple shock model for common cause failures using discrete Markov chain

    International Nuclear Information System (INIS)

    Chung, Dae Wook; Kang, Chang Soon

    1992-01-01

    The most widely used models in common cause analysis are (single) shock models such as the BFR, and the MFR. But, single shock model can not treat the individual common cause separately and has some irrational assumptions. Multiple shock model for common cause failures is developed using Markov chain theory. This model treats each common cause shock as separately and sequently occuring event to implicate the change in failure probability distribution due to each common cause shock. The final failure probability distribution is evaluated and compared with that from the BFR model. The results show that multiple shock model which minimizes the assumptions in the BFR model is more realistic and conservative than the BFR model. The further work for application is the estimations of parameters such as common cause shock rate and component failure probability given a shock,p, through the data analysis

  4. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  5. A Competing Risk Model of First Failure Site after Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer.

    Science.gov (United States)

    Nygård, Lotte; Vogelius, Ivan R; Fischer, Barbara M; Kjær, Andreas; Langer, Seppo W; Aznar, Marianne C; Persson, Gitte F; Bentzen, Søren M

    2018-04-01

    The aim of the study was to build a model of first failure site- and lesion-specific failure probability after definitive chemoradiotherapy for inoperable NSCLC. We retrospectively analyzed 251 patients receiving definitive chemoradiotherapy for NSCLC at a single institution between 2009 and 2015. All patients were scanned by fludeoxyglucose positron emission tomography/computed tomography for radiotherapy planning. Clinical patient data and fludeoxyglucose positron emission tomography standardized uptake values from primary tumor and nodal lesions were analyzed by using multivariate cause-specific Cox regression. In patients experiencing locoregional failure, multivariable logistic regression was applied to assess risk of each lesion being the first site of failure. The two models were used in combination to predict probability of lesion failure accounting for competing events. Adenocarcinoma had a lower hazard ratio (HR) of locoregional failure than squamous cell carcinoma (HR = 0.45, 95% confidence interval [CI]: 0.26-0.76, p = 0.003). Distant failures were more common in the adenocarcinoma group (HR = 2.21, 95% CI: 1.41-3.48, p failure showed that primary tumors were more likely to fail than lymph nodes (OR = 12.8, 95% CI: 5.10-32.17, p failure (OR = 1.26 per unit increase, 95% CI: 1.12-1.40, p failure site-specific competing risk model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into NSCLC. Failure site-specific models add complementary information to conventional prognostic models. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  6. 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

  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. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  9. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  10. Failure time series prediction in industrial maintenance using neural networks; Previsao de series temporais de falhas em manutencao industrial usando redes neurais

    Energy Technology Data Exchange (ETDEWEB)

    Torres Junior, Rubiao G.; Machado, Maria Augusta S. [Instituto Brasileiro de Mercado de Capitais (IBMEC), Rio de Janeiro, RJ (Brazil); Souza, Reinaldo C. [Pontificia Univ. Catolica do Rio de Janeiro, RJ (Brazil)

    2005-07-01

    The objective of this work is the application of two failure prediction models in industrial maintenance with the use of Artificial Neural Networks (ANN). A characteristic of the modern industrial environment is a strong competition which leads companies to search for costs minimization methods. Thus, dada gathering and maintenance dada treatment becomes extremely important in this scenario for it aims the equipment and plant systems real repair necessity. Therefore, the objective becomes the widening of the system's full activity in a continuous manner, in the required period, without problems in their integrating parts. A daily time series is modeled based on maintenance interventions pauses dada from a five years period derived form many productive systems in the finalization areas of PETROFLEX Ind. and Com. S.A. Thus, the purpose is to introduce models based on neural networks and verify its system's pauses prediction capacity, so as to intervene with adequate timing before the system fails, extend the operational period and consequently increase its availability. The results obtained in this work demonstrate the employment of Neural Networks in the prediction of pauses in PETROFLEX industrial area maintenance. The ANN's prediction capacity in a group of dada with strong non-linear component where other statistical techniques have shown little efficient has also been confirmed. Discover neural models to predict failure systems time series has enable a breakthrough in the research field, especially due to the market demand. It's no doubt a technique that will evolve in the industrial maintenance area financing important managing decision. Prediction techniques, such as the ones illustrated in this study, work side by side maintenance planning and if carefully implemented and followed up can in the medium run supply a substantial increase in the available operational hours. (author)

  11. 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.

  12. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  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).