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Sample records for prognostic modeling approach

  1. A Model-based Prognostics Approach Applied to Pneumatic Valves

    Directory of Open Access Journals (Sweden)

    Matthew J. Daigle

    2011-01-01

    Full Text Available Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

  2. A Model-based Prognostics Approach Applied to Pneumatic Valves

    Data.gov (United States)

    National Aeronautics and Space Administration — Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain...

  3. A Model-Based Prognostics Approach Applied to Pneumatic Valves

    Data.gov (United States)

    National Aeronautics and Space Administration — Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain...

  4. A Comparison of Filter-based Approaches for Model-based Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is...

  5. Prognostic factors for urachal cancer: a bayesian model-averaging approach.

    Science.gov (United States)

    Kim, In Kyong; Lee, Joo Yong; Kwon, Jong Kyou; Park, Jae Joon; Cho, Kang Su; Ham, Won Sik; Hong, Sung Joon; Yang, Seung Choul; Choi, Young Deuk

    2014-09-01

    This study was conducted to evaluate prognostic factors and cancer-specific survival (CSS) in a cohort of 41 patients with urachal carcinoma by use of a Bayesian model-averaging approach. Our cohort included 41 patients with urachal carcinoma who underwent extended partial cystectomy, total cystectomy, transurethral resection, chemotherapy, or radiotherapy at a single institute. All patients were classified by both the Sheldon and the Mayo staging systems according to histopathologic reports and preoperative radiologic findings. Kaplan-Meier survival curves and Cox proportional-hazards regression models were carried out to investigate prognostic factors, and a Bayesian model-averaging approach was performed to confirm the significance of each variable by using posterior probabilities. The mean age of the patients was 49.88 ± 13.80 years and the male-to-female ratio was 24:17. The median follow-up was 5.42 years (interquartile range, 2.8-8.4 years). Five- and 10-year CSS rates were 55.9% and 43.4%, respectively. Lower Sheldon (p=0.004) and Mayo (pcancer-specific mortality in urachal carcinoma. The Mayo staging system might be more effective than the Sheldon staging system. In addition, the multivariate analyses suggested that tumor size may be a prognostic factor for urachal carcinoma.

  6. Using prognostic models in CLL to personalize approach to clinical care: Are we there yet?

    Science.gov (United States)

    Mina, Alain; Sandoval Sus, Jose; Sleiman, Elsa; Pinilla-Ibarz, Javier; Awan, Farrukh T; Kharfan-Dabaja, Mohamed A

    2017-10-28

    Four decades ago, two staging systems were developed to help stratify CLL into different prognostic categories. These systems, the Rai and the Binet staging, depended entirely on abnormal exam findings and evidence of anemia and thrombocytopenia. Better understanding of biologic, genetic, and molecular characteristics of CLL have contributed to better appreciating its clinical heterogeneity. New prognostic models, the GCLLSG prognostic index and the CLL-IPI, emerged. They incorporate biologic and genetic information related to CLL and are capable of predicting survival outcomes and cases anticipated to need therapy earlier in the disease course. Accordingly, these newer models are helping develop better informed surveillance strategies and ultimately tailor treatment intensity according to presence (or lack thereof) of certain prognostic markers. This represents a step towards personalizing care of CLL patients. We anticipate that as more prognostic factors continue to be identified, the GCLLSG prognostic index and CLL-IPI models will undergo further revisions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Intercomparisons of Prognostic, Diagnostic, and Inversion Modeling Approaches for Estimation of Net Ecosystem Exchange over the Pacific Northwest Region

    Science.gov (United States)

    Turner, D. P.; Jacobson, A. R.; Nemani, R. R.

    2013-12-01

    The recent development of large spatially-explicit datasets for multiple variables relevant to monitoring terrestrial carbon flux offers the opportunity to estimate the terrestrial land flux using several alternative, potentially complimentary, approaches. Here we developed and compared regional estimates of net ecosystem exchange (NEE) over the Pacific Northwest region of the U.S. using three approaches. In the prognostic modeling approach, the process-based Biome-BGC model was driven by distributed meteorological station data and was informed by Landsat-based coverages of forest stand age and disturbance regime. In the diagnostic modeling approach, the quasi-mechanistic CFLUX model estimated net ecosystem production (NEP) by upscaling eddy covariance flux tower observations. The model was driven by distributed climate data and MODIS FPAR (the fraction of incident PAR that is absorbed by the vegetation canopy). It was informed by coarse resolution (1 km) data about forest stand age. In both the prognostic and diagnostic modeling approaches, emissions estimates for biomass burning, harvested products, and river/stream evasion were added to model-based NEP to get NEE. The inversion model (CarbonTracker) relied on observations of atmospheric CO2 concentration to optimize prior surface carbon flux estimates. The Pacific Northwest is heterogeneous with respect to land cover and forest management, and repeated surveys of forest inventory plots support the presence of a strong regional carbon sink. The diagnostic model suggested a stronger carbon sink than the prognostic model, and a much larger sink that the inversion model. The introduction of Landsat data on disturbance history served to reduce uncertainty with respect to regional NEE in the diagnostic and prognostic modeling approaches. The FPAR data was particularly helpful in capturing the seasonality of the carbon flux using the diagnostic modeling approach. The inversion approach took advantage of a global

  8. Model-Based Prognostics of Hybrid Systems

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

    2015-01-01

    Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

  9. A Distributed Approach to System-Level Prognostics

    Science.gov (United States)

    Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, Indranil

    2012-01-01

    Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

  10. Model-based Prognostics with Concurrent Damage Progression Processes

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    National Aeronautics and Space Administration — Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several...

  11. Distributed Prognostics Based on Structural Model Decomposition

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    National Aeronautics and Space Administration — Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based...

  12. Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

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    National Aeronautics and Space Administration — Model-based prognostics approaches capture system knowl- edge in the form of physics-based models of components that include how they fail. These methods consist of...

  13. Multiple Damage Progression Paths in Model-based Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent...

  14. Physics-of-Failure Approach to Prognostics

    Science.gov (United States)

    Kulkarni, Chetan S.

    2017-01-01

    As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

  15. Concordance for prognostic models with competing risks

    DEFF Research Database (Denmark)

    Wolbers, Marcel; Blanche, Paul; Koller, Michael T

    2014-01-01

    The concordance probability is a widely used measure to assess discrimination of prognostic models with binary and survival endpoints. We formally define the concordance probability for a prognostic model of the absolute risk of an event of interest in the presence of competing risks and relate i...... of the working model. We further illustrate the methods by computing the concordance probability for a prognostic model of coronary heart disease (CHD) events in the presence of the competing risk of non-CHD death.......The concordance probability is a widely used measure to assess discrimination of prognostic models with binary and survival endpoints. We formally define the concordance probability for a prognostic model of the absolute risk of an event of interest in the presence of competing risks and relate...

  16. Physics based Degradation Modeling and Prognostics of Electrolytic Capacitors under Electrical Overstress Conditions

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    National Aeronautics and Space Administration — This paper proposes a physics based degradation modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors are critical components in...

  17. Prognostics Health Management and Physics based failure Models for Electrolytic Capacitors

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    National Aeronautics and Space Administration — This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors and MOSFETs are the two major...

  18. State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2017-09-01

    Integrating prognostics to a real application requires a certain maturity level and for this reason there is a lack of success stories about development of a complete Prognostics and Health Management system. In fact, the maturity of prognostics is closely linked to data and domain specific entities like modeling. Basically, prognostics task aims at predicting the degradation of engineering assets. However, practically it is not possible to precisely predict the impending failure, which requires a thorough understanding to encounter different sources of uncertainty that affect prognostics. Therefore, different aspects crucial to the prognostics framework, i.e., from monitoring data to remaining useful life of equipment need to be addressed. To this aim, the paper contributes to state of the art and taxonomy of prognostics approaches and their application perspectives. In addition, factors for prognostics approach selection are identified, and new case studies from component-system level are discussed. Moreover, open challenges toward maturity of the prognostics under uncertainty are highlighted and scheme for an efficient prognostics approach is presented. Finally, the existing challenges for verification and validation of prognostics at different technology readiness levels are discussed with respect to open challenges.

  19. Prognostic modeling in pediatric acute liver failure.

    Science.gov (United States)

    Jain, Vandana; Dhawan, Anil

    2016-10-01

    Liver transplantation (LT) is the only proven treatment for pediatric acute liver failure (PALF). However, over a period of time, spontaneous native liver survival is increasingly reported, making us wonder if we are overtransplanting children with acute liver failure (ALF). An effective prognostic model for PALF would help direct appropriate organ allocation. Only patients who would die would undergo LT, and those who would spontaneously recover would avoid unnecessary LT. Deriving and validating such a model for PALF, however, encompasses numerous challenges. In particular, the heterogeneity of age and etiology in PALF, as well as a lack of understanding of the natural history of the disease, contributed by the availability of LT has led to difficulties in prognostic model development. Several prognostic laboratory variables have been identified, and the incorporation of these variables into scoring systems has been attempted. A reliable targeted prognostic model for ALF in Wilson's disease has been established and externally validated. The roles of physiological, immunological, and metabolomic parameters in prognosis are being investigated. This review discusses the challenges with prognostic modeling in PALF and describes predictive methods that are currently available and in development for the future. Liver Transplantation 22 1418-1430 2016 AASLD. © 2016 by the American Association for the Study of Liver Diseases.

  20. Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

    Data.gov (United States)

    National Aeronautics and Space Administration — The success of model-based approaches to systems health management depends largely on the quality of the underly- ing models. In model-based prognostics, it is...

  1. Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

    Science.gov (United States)

    Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

    2017-11-01

    Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  2. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2015-12-01

    Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

  3. Physicians' perceptions of the value of prognostic models: the benefits and risks of prognostic confidence.

    Science.gov (United States)

    Hallen, Sarah A M; Hootsmans, Norbert A M; Blaisdell, Laura; Gutheil, Caitlin M; Han, Paul K J

    2015-12-01

    The communication of prognosis in end-of-life (EOL) care is a challenging task that is limited by prognostic uncertainty and physicians' lack of confidence in their prognostic estimates. Clinical prediction models (CPMs) are increasingly common evidence-based tools that may mitigate these problems and facilitate the communication and use of prognostic information in EOL care; however, little is known about physicians' perceptions of the value of these tools. To explore physicians' perceptions of the value of CPMs in EOL care. Qualitative study using semi-structured individual interviews which were analysed using a constant comparative method. Convenience sample of 17 attending physicians representing five different medical specialties at a single large tertiary care medical centre. Physicians perceived CPMs as having three main benefits in EOL care: (i) enhancing their prognostic confidence; (ii) increasing their prognostic authority; and (iii) enabling patient persuasion in circumstances of low prognostic and therapeutic uncertainty. However, physicians also perceived CPMs as having potential risks, which include producing emotional distress in patients and promoting prognostic overconfidence in EOL care. Physicians perceive CPMs as a potentially valuable means of increasing their prognostic confidence, communication and explicit use of prognostic information in EOL care. However, physicians' perceptions of CPMs also indicate a need to establish broad and consistent implementation processes to engage patients in shared decision making in EOL care, to effectively communicate uncertainty in prognostic information and to help both patients and physicians manage uncertainty in EOL care decisions. © 2014 John Wiley & Sons Ltd.

  4. Contemporary approach to neurologic prognostication of coma after cardiac arrest.

    Science.gov (United States)

    Ben-Hamouda, Nawfel; Taccone, Fabio S; Rossetti, Andrea O; Oddo, Mauro

    2014-11-01

    Coma after cardiac arrest (CA) is an important cause of admission to the ICU. Prognosis of post-CA coma has significantly improved over the past decade, particularly because of aggressive postresuscitation care and the use of therapeutic targeted temperature management (TTM). TTM and sedatives used to maintain controlled cooling might delay neurologic reflexes and reduce the accuracy of clinical examination. In the early ICU phase, patients' good recovery may often be indistinguishable (based on neurologic examination alone) from patients who eventually will have a poor prognosis. Prognostication of post-CA coma, therefore, has evolved toward a multimodal approach that combines neurologic examination with EEG and evoked potentials. Blood biomarkers (eg, neuron-specific enolase [NSE] and soluble 100-β protein) are useful complements for coma prognostication; however, results vary among commercial laboratory assays, and applying one single cutoff level (eg, > 33 μg/L for NSE) for poor prognostication is not recommended. Neuroimaging, mainly diffusion MRI, is emerging as a promising tool for prognostication, but its precise role needs further study before it can be widely used. This multimodal approach might reduce false-positive rates of poor prognosis, thereby providing optimal prognostication of comatose CA survivors. The aim of this review is to summarize studies and the principal tools presently available for outcome prediction and to describe a practical approach to the multimodal prognostication of coma after CA, with a particular focus on neuromonitoring tools. We also propose an algorithm for the optimal use of such multimodal tools during the early ICU phase of post-CA coma.

  5. Prognostics

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    National Aeronautics and Space Administration — Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as “predicting the time at...

  6. Particle filter-based prognostic approach for railway track geometry

    Science.gov (United States)

    Mishra, Madhav; Odelius, Johan; Thaduri, Adithya; Nissen, Arne; Rantatalo, Matti

    2017-11-01

    Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

  7. Prognostics Approach for Power MOSFET Under Thermal-Stress

    Science.gov (United States)

    Galvan, Jose Ramon Celaya; Saxena, Abhinav; Kulkarni, Chetan S.; Saha, Sankalita; Goebel, Kai

    2012-01-01

    The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real

  8. On prognostic models, artificial intelligence and censored observations.

    Science.gov (United States)

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  9. Comparison of two prognostic models for acute pulmonary embolism

    Directory of Open Access Journals (Sweden)

    Abd-ElRahim Ibrahim Youssef

    2016-10-01

    Conclusion: (1 There is an agreement to great extent in risk stratification of APE patients by PESI and ESC prognostic models, where mortality rate is increased among high risk classes of both models, (2 ESC prognostic model is more accurate than PESI model in mortality prediction of APE patients especially in the high risk class, (3 echocardiographic evidence of RVD and elevated plasma BNP can help to identify APE patients at increased risk of adverse short-term outcome and (4 integration of RVD assessment by echocardiography and BNP to clinical findings improves the prognostic value of ESC model.

  10. Prognostic stratification in pulmonary hypertension: A multi-biomarker approach.

    Science.gov (United States)

    Plácido, Rui; Cortez-Dias, Nuno; Robalo Martins, Susana; Gomes Almeida, Ana; Calisto, Carina; Gonçalves, Susana; Sadoune, Malha; Nunes Diogo, António; Mebazaa, Alexandre; Pinto, Fausto José

    2017-02-01

    Pulmonary hypertension (PH) covers a group of conditions characterized by an increase in pulmonary vascular resistance leading to right ventricular failure. Risk stratification is crucial for adequate prognostic and therapeutic assessment. However, the accuracy of conventional parameters is limited, especially biomarkers. To determine the prognostic value of new biomarkers and their combination in a multi-biomarker approach to predict outcome in patients with PH. In this prospective cohort study, PH patients underwent clinical, echocardiographic and laboratory assessment, including quantification of serum N-terminal pro-brain natriuretic peptide (NT-proBNP) and of the following new biomarkers: mid-regional pro-adrenomedullin (MR-proADM), copeptin, endothelin-1, mid-regional pro-atrial natriuretic peptide (MR-proANP) and soluble ST2 (sST2), the interleukin-33 receptor. The accuracy of the different parameters for predicting all-cause mortality and death or hospitalization of cardiac causes was determined. The prognostic value of a multi-biomarker score based on the tertile distribution of serum NT-proBNP, MR-proANP, renin and sST2 was compared to conventional markers. Forty-three patients (72.1% female, age 59±15 years) were included, most of whom (65.1%) had group 1 PH. During a median follow-up of 34 months, 26% of the patients died and 35% were hospitalized for cardiac causes. Atrial and ventricular dimensions and right ventricular fractional area change were prognostic predictors. Log NT-proBNP (HR: 31.14; 95% CI: 3.12-310.7; p=0.003) and renin (HR: 1.02; 95% CI: 1.005-1.038; p=0.009) were independent predictors of mortality. MR-proANP (HR: 1.008; 95% CI 1.004-1.011; p<0.001) and sST2 (HR: 1.005; 95% CI 1.001-1.009; p=0.04) were predictors of death or hospitalization. The prognostic value of the multi-biomarker score was higher than any of the conventional parameters, and enabled identification of risk groups (the high-risk group had three-year mortality of 77

  11. Model-based Prognostics under Limited Sensing

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    National Aeronautics and Space Administration — Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often...

  12. Diagnostic and Prognostic Models for Generator Step-Up Transformers

    Energy Technology Data Exchange (ETDEWEB)

    Vivek Agarwal; Nancy J. Lybeck; Binh T. Pham

    2014-09-01

    In 2014, the online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) focused on diagnostic and prognostic capabilities for generator step-up transformers. INL worked with subject matter experts from the Electric Power Research Institute (EPRI) to augment and revise the GSU fault signatures previously implemented in the Electric Power Research Institute’s (EPRI’s) Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. Two prognostic models were identified and implemented for GSUs in the FW-PHM Suite software. INL and EPRI demonstrated the use of prognostic capabilities for GSUs. The complete set of fault signatures developed for GSUs in the Asset Fault Signature Database of the FW-PHM Suite for GSUs is presented in this report. Two prognostic models are described for paper insulation: the Chendong model for degree of polymerization, and an IEEE model that uses a loading profile to calculates life consumption based on hot spot winding temperatures. Both models are life consumption models, which are examples of type II prognostic models. Use of the models in the FW-PHM Suite was successfully demonstrated at the 2014 August Utility Working Group Meeting, Idaho Falls, Idaho, to representatives from different utilities, EPRI, and the Halden Research Project.

  13. Model Adaptation for Prognostics in a Particle Filtering Framework

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    National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....

  14. A Model-based Avionic Prognostic Reasoner (MAPR)

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    National Aeronautics and Space Administration — The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and...

  15. Model-based Prognostics with Fixed-lag Particle Filters

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    National Aeronautics and Space Administration — Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a...

  16. A framework for quantifying net benefits of alternative prognostic models

    DEFF Research Database (Denmark)

    Rapsomaniki, Eleni; White, Ian R; Wood, Angela M

    2012-01-01

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit......) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk...... reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple...

  17. Development and validation of logistic prognostic models by predefined SAS-macros

    Directory of Open Access Journals (Sweden)

    Ziegler, Christoph

    2006-02-01

    Full Text Available In medical decision making about therapies or diagnostic procedures in the treatment of patients the prognoses of the course or of the magnitude of diseases plays a relevant role. Beside of the subjective attitude of the clinician mathematical models can help in providing such prognoses. Such models are mostly multivariate regression models. In the case of a dichotomous outcome the logistic model will be applied as the standard model. In this paper we will describe SAS-macros for the development of such a model, for examination of the prognostic performance, and for model validation. The rational for this developmental approach of a prognostic modelling and the description of the macros can only given briefly in this paper. Much more details are given in. These 14 SAS-macros are a tool for setting up the whole process of deriving a prognostic model. Especially the possibility of validating the model by a standardized software tool gives an opportunity, which is not used in general in published prognostic models. Therefore, this can help to develop new models with good prognostic performance for use in medical applications.

  18. A Systems Engineering Approach to Electro-Mechanical Actuator Diagnostic and Prognostic Development

    Data.gov (United States)

    National Aeronautics and Space Administration — The authors have formulated a Comprehensive Systems Engineering approach to Electro-Mechanical Actuator (EMA) Prognostics and Health Management (PHM) system...

  19. An Approach to Prognostic Decision Making in the Aerospace Domain

    Data.gov (United States)

    National Aeronautics and Space Administration — The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for...

  20. Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

    Directory of Open Access Journals (Sweden)

    Lei Mao

    2017-09-01

    Full Text Available In this paper, the sensor selection approach is investigated with the aim of using fewer sensors to provide reliable fuel cell diagnostic and prognostic results. The sensitivity of sensors is firstly calculated with a developed fuel cell model. With sensor sensitivities to different fuel cell failure modes, the available sensors can be ranked. A sensor selection algorithm is used in the analysis, which considers both sensor sensitivity to fuel cell performance and resistance to noise. The performance of the selected sensors in polymer electrolyte membrane (PEM fuel cell prognostics is also evaluated with an adaptive neuro-fuzzy inference system (ANFIS, and results show that the fuel cell voltage can be predicted with good quality using the selected sensors. Furthermore, a fuel cell test is performed to investigate the effectiveness of selected sensors in fuel cell fault diagnosis. From the results, different fuel cell states can be distinguished with good quality using the selected sensors.

  1. Physics Based Modeling and Prognostics of Electrolytic Capacitors

    Science.gov (United States)

    Kulkarni, Chetan; Ceyla, Jose R.; Biswas, Gautam; Goebel, Kai

    2012-01-01

    This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors have become critical components in electronics systems in aeronautics and other domains. Degradations and faults in DC-DC converter unit propagates to the GPS and navigation subsystems and affects the overall solution. Capacitors and MOSFETs are the two major components, which cause degradations and failures in DC-DC converters. This type of capacitors are known for its low reliability and frequent breakdown on critical systems like power supplies of avionics equipment and electrical drivers of electromechanical actuators of control surfaces. Some of the more prevalent fault effects, such as a ripple voltage surge at the power supply output can cause glitches in the GPS position and velocity output, and this, in turn, if not corrected will propagate and distort the navigation solution. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

  2. Prognostic transcriptional association networks: a new supervised approach based on regression trees

    Science.gov (United States)

    Nepomuceno-Chamorro, Isabel; Azuaje, Francisco; Devaux, Yvan; Nazarov, Petr V.; Muller, Arnaud; Aguilar-Ruiz, Jesús S.; Wagner, Daniel R.

    2011-01-01

    Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains. Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21098433

  3. A review on prognostics approaches for remaining useful life of lithium-ion battery

    Science.gov (United States)

    Su, C.; Chen, H. J.

    2017-11-01

    Lithium-ion (Li-ion) battery is a core component for various industrial systems, including satellite, spacecraft and electric vehicle, etc. The mechanism of performance degradation and remaining useful life (RUL) estimation correlate closely to the operating state and reliability of the aforementioned systems. Furthermore, RUL prediction of Li-ion battery is crucial for the operation scheduling, spare parts management and maintenance decision for such kinds of systems. In recent years, performance degradation prognostics and RUL estimation approaches have become a focus of the research concerning with Li-ion battery. This paper summarizes the approaches used in Li-ion battery RUL estimation. Three categories are classified accordingly, i.e. model-based approach, data-based approach and hybrid approach. The key issues and future trends for battery RUL estimation are also discussed.

  4. An intelligent approach to machine component health prognostics by utilizing only truncated histories

    Science.gov (United States)

    Lu, Chen; Tao, Laifa; Fan, Huanzhen

    2014-01-01

    Numerous techniques and methods have been proposed to reduce the production downtime, spare-part inventory, maintenance cost, and safety hazards of machineries and equipment. Prognostics are regarded as a significant and promising tool for achieving these benefits for machine maintenance. However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances. In practice, engineering assets are rarely being permitted to run to failure. Many studies have reported valuable models and methods that engage in maximizing both truncated and failure data. However, limited studies have focused on cases where only truncated data are available, which is common in machine condition monitoring. Therefore, this study develops an intelligent machine component prognostics system by utilizing only truncated histories. First, the truncated Minimum Quantization Error (MQE) histories were obtained by Self-organizing Map network after feature extraction. The chaos-based parallel multilayer perceptron network and polynomial fitting for residual errors were adopted to generate the predicted MQEs and failure times following the truncation times. The feed-forward neural network (FFNN) was trained with inputs both from the truncated MQE histories and from the predicted MQEs. The target vectors of survival probabilities were estimated by intelligent product limit estimator using the truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual units. To validate the proposed method, two cases were considered by using the degradation data generated by bearing testing rig. Results demonstrate that the proposed method is a promising intelligent prognostics approach for machine component health.

  5. A Distributed Approach to System-Level Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key tech- nology for systems health management that leads...

  6. Prognostics Approach For Power Mosfet Under Thermal-Stress Aging

    Data.gov (United States)

    National Aeronautics and Space Administration — The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes...

  7. Risk factors and prognostic models for perinatal asphyxia at term

    NARCIS (Netherlands)

    Ensing, S.

    2015-01-01

    This thesis will focus on the risk factors and prognostic models for adverse perinatal outcome at term, with a special focus on perinatal asphyxia and obstetric interventions during labor to reduce adverse pregnancy outcomes. For the majority of the studies in this thesis we were allowed to use data

  8. Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

    Science.gov (United States)

    Ramasso, Emannuel; Saxena, Abhinav

    2014-01-01

    Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

  9. Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast...

  10. A self-cognizant dynamic system approach for prognostics and health management

    Science.gov (United States)

    Bai, Guangxing; Wang, Pingfeng; Hu, Chao

    2015-03-01

    Prognostics and health management (PHM) is an emerging engineering discipline that diagnoses and predicts how and when a system will degrade its performance and lose its partial or whole functionality. Due to the complexity and invisibility of rules and states of most dynamic systems, developing an effective approach to track evolving system states becomes a major challenge. This paper presents a new self-cognizant dynamic system (SCDS) approach that incorporates artificial intelligence into dynamic system modeling for PHM. A feed-forward neural network (FFNN) is selected to approximate a complex system response which is challenging task in general due to inaccessible system physics. The trained FFNN model is then embedded into a dual extended Kalman filter algorithm to track down system dynamics. A recursive computation technique used to update the FFNN model using online measurements is also derived. To validate the proposed SCDS approach, a battery dynamic system is considered as an experimental application. After modeling the battery system by a FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated by updating the FFNN model using the proposed approach. Experimental results suggest that the proposed approach improves the efficiency and accuracy for battery health management.

  11. Model Adaptation for Prognostics in a Particle Filtering Framework

    Directory of Open Access Journals (Sweden)

    Bhaskar Saha

    2011-01-01

    Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

  12. Model Adaptation for Prognostics in a Particle Filtering Framework

    Science.gov (United States)

    Saha, Bhaskar; Goebel, Kai Frank

    2011-01-01

    One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

  13. Electrochemistry-based Battery Modeling for Prognostics

    Science.gov (United States)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2013-01-01

    Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

  14. A Physics-Based Modeling Framework for Prognostic Studies

    Science.gov (United States)

    Kulkarni, Chetan S.

    2014-01-01

    Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable

  15. Prognostic survival model for people diagnosed with invasive cutaneous melanoma.

    Science.gov (United States)

    Baade, Peter D; Royston, Patrick; Youl, Philipa H; Weinstock, Martin A; Geller, Alan; Aitken, Joanne F

    2015-01-31

    The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Data from the Queensland Cancer Registry for people (20-89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive lymph nodes was manually extracted from pathology forms. Flexible parametric survival models were combined with multivariable fractional polynomial for selecting variables and transformations of continuous variables. Multiple imputation was used for missing covariate values. The MSI contained the variables thickness (transformed, explained 40.6% of variation in survival), body site (additional 1.9% in variation), metastasis (1.8%), positive nodes (0.7%), ulceration (1.3%), age (1.1%). Royston and Sauerbrei's D statistic (measure of discrimination) was 1.50 (95% CI = 1.44, 1.56) and the corresponding RD2 (measure of explained variation) was 0.47 (0.45, 0.49), demonstrating strong explanatory performance. The Harrell-C statistic was 0.88 (0.88, 0.89). Lacking an external validation dataset, we applied internal-external cross validation to demonstrate the consistency of the prognostic information across geographically-defined subsets of the cohort. The MSI provides good ability to predict survival for melanoma patients. Beyond the immediate clinical use, the MSI may have important public health and research applications for evaluations of public health interventions aimed at reducing deaths from melanoma.

  16. A Prognostic Model for Development of Profound Shock among Children Presenting with Dengue Shock Syndrome.

    Directory of Open Access Journals (Sweden)

    Phung Khanh Lam

    Full Text Available To identify risk factors and develop a prediction model for the development of profound and recurrent shock amongst children presenting with dengue shock syndrome (DSS.We analyzed data from a prospective cohort of children with DSS recruited at the Paediatric Intensive Care Unit of the Hospital for Tropical Disease in Ho Chi Minh City, Vietnam. The primary endpoint was "profound DSS", defined as ≥2 recurrent shock episodes (for subjects presenting in compensated shock, or ≥1 recurrent shock episodes (for subjects presenting initially with decompensated/hypotensive shock, and/or requirement for inotropic support. Recurrent shock was evaluated as a secondary endpoint. Risk factors were pre-defined clinical and laboratory variables collected at the time of presentation with shock. Prognostic model development was based on logistic regression and compared to several alternative approaches.The analysis population included 1207 children of whom 222 (18% progressed to "profound DSS" and 433 (36% had recurrent shock. Independent risk factors for both endpoints included younger age, earlier presentation, higher pulse rate, higher temperature, higher haematocrit and, for females, worse hemodynamic status at presentation. The final prognostic model for "profound DSS" showed acceptable discrimination (AUC=0.69 for internal validation and calibration and is presented as a simple score-chart.Several risk factors for development of profound or recurrent shock among children presenting with DSS were identified. The score-chart derived from the prognostic models should improve triage and management of children presenting with DSS in dengue-endemic areas.

  17. A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Fernando Sánchez Lasheras

    2015-03-01

    Full Text Available Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS technique with the principal component analysis (PCA, dendrograms and classification and regression trees (CARTs. Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.. Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

  18. Prognostics for Microgrid Components

    Science.gov (United States)

    Saxena, Abhinav

    2012-01-01

    Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

  19. Remote sensing data assimilation for a prognostic phenology model

    Energy Technology Data Exchange (ETDEWEB)

    Thornton, Peter E [ORNL; Stockli, Reto [Colorado State University, Fort Collins

    2008-01-01

    Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought-deciduous ecosystems where correlation of predicted versus satellite-observed FPAR and LAI increases from negative to 0.7-0.8. Data assimilation effectively overcomes the cloud- and aerosol-related deficiencies of satellite data sets in tropical areas. Validation with a 49-year-long phenology data set reveals that the temperature-driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter-annual and decadal variability. Predicted SOS shows a higher inter-annual variability with a negative bias of 5-20 days compared to species-level SOS. It is however accurate to within 1-2 days compared to SOS derived from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.

  20. Updating and prospective validation of a prognostic model for high sickness absence

    NARCIS (Netherlands)

    Roelen, C.A.M.; Heymans, M.W.; Twisk, J.W.R.; van Rhenen, W.; Pallesen, S.; Bjorvatn, B.; Moen, B.E.; Mageroy, N.

    2015-01-01

    Objectives To further develop and validate a Dutch prognostic model for high sickness absence (SA). Methods Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by

  1. Stage Separation Failure: Model Based Diagnostics and Prognostics

    Science.gov (United States)

    Luchinsky, Dmitry; Hafiychuk, Vasyl; Kulikov, Igor; Smelyanskiy, Vadim; Patterson-Hine, Ann; Hanson, John; Hill, Ashley

    2010-01-01

    Safety of the next-generation space flight vehicles requires development of an in-flight Failure Detection and Prognostic (FD&P) system. Development of such system is challenging task that involves analysis of many hard hitting engineering problems across the board. In this paper we report progress in the development of FD&P for the re-contact fault between upper stage nozzle and the inter-stage caused by the first stage and upper stage separation failure. A high-fidelity models and analytical estimations are applied to analyze the following sequence of events: (i) structural dynamics of the nozzle extension during the impact; (ii) structural stability of the deformed nozzle in the presence of the pressure and temperature loads induced by the hot gas flow during engine start up; and (iii) the fault induced thrust changes in the steady burning regime. The diagnostic is based on the measurements of the impact torque. The prognostic is based on the analysis of the correlation between the actuator signal and fault-induced changes in the nozzle structural stability and thrust.

  2. A Distributed Approach to System-Level Prognostics

    Science.gov (United States)

    2012-09-01

    parameter estimate p(x(t),θ(t)|y0:t) based on the history of observations up to time t, y0:t. This estimate is represented as a probability...distributed approach and 98.74% for the cen- tralized approach. Using relative standard deviation ( RSD ) as a measure of spread, and averaged over all...prediction points, RSD is 0.40% for the distributed approach and 0.43% for the centralized approach. The distributed approach is only slightly less

  3. Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

    Science.gov (United States)

    Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

  4. A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Science.gov (United States)

    Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

  5. Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study

    Science.gov (United States)

    Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille; Moxley, Katherine; Moore, Kathleen; Mannel, Robert; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2017-03-01

    Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838+/-0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.

  6. Genomics-based Approach and Prognostic Stratification Significance of Gene Mutations in Intermediate-risk Acute Myeloid Leukemia

    Directory of Open Access Journals (Sweden)

    Bian-Hong Wang

    2015-01-01

    Conclusions: NGS represents a pioneering and helpful approach to prognostic risk stratification of IR-AML patients. Further large-scale studies for comprehensive molecular analysis are needed to provide guidance and a theoretical basis for IR-AML prognostic stratification and clinical management.

  7. Multidisciplinary Rehabilitation Treatment of Patients With Chronic Low Back Pain: A Prognostic Model for Its Outcome

    NARCIS (Netherlands)

    van der Hulst, Marije; Vollenbroek-Hutten, Miriam Marie Rosé; Groothuis-Oudshoorn, Catharina Gerarda Maria; Hermens, Hermanus J.

    Objectives: (1) To determine if treatment outcome in chronic low back pain can be predicted by a predefined multivariate prognostic model based on consistent predictors from the literature and (2) to explore the value of potentially prognostic factors further. Methods: Data were derived from a

  8. Systematic review of multivariable prognostic models for mild traumatic brain injury.

    Science.gov (United States)

    Silverberg, Noah D; Gardner, Andrew J; Brubacher, Jeffrey R; Panenka, William J; Li, Jun Jian; Iverson, Grant L

    2015-04-15

    Prognostic models can guide clinical management and increase statistical power in clinical trials. The availability and adequacy of prognostic models for mild traumatic brain injury (MTBI) is uncertain. The present study aimed to (1) identify and evaluate multivariable prognostic models for MTBI, and (2) determine which pre-, peri-, and early post-injury variables have independent prognostic value in the context of multivariable models. An electronic search of MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL databases for English-language MTBI cohort studies from 1970-2013 was supplemented by Web of Science citation and hand searching. This search strategy identified 7789 articles after removing duplicates. Of 182 full-text articles reviewed, 26 met eligibility criteria including (1) prospective inception cohort design, (2) prognostic information collected within 1 month post-injury, and (3) 2+variables combined to predict clinical outcome (e.g., post-concussion syndrome) at least 1 month later. Independent reviewers extracted sample characteristics, study design features, clinical outcome variables, predictor selection methods, and prognostic model discrimination, calibration, and cross-validation. These data elements were synthesized qualitatively. The present review found no multivariable prognostic model that adequately predicts individual patient outcomes from MTBI. Suboptimal methodology limits their reproducibility and clinical usefulness. The most robust prognostic factors in the context of multivariable models were pre-injury mental health and early post-injury neuropsychological functioning. Women and adults with early post-injury anxiety also have worse prognoses. Relative to these factors, the severity of MTBI had little long-term prognostic value. Future prognostic studies should consider a broad range of biopsychosocial predictors in large inception cohorts.

  9. Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

    Science.gov (United States)

    Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

    2002-12-30

    Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

  10. Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of...

  11. Aircraft Anomaly Prognostics Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Ridgetop Group will leverage its proven Electromechanical Actuator (EMA) prognostics methodology to develop an advanced model-based actuator prognostic reasoner...

  12. Plaque Brachytherapy for Uveal Melanoma: A Vision Prognostication Model

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Niloufer [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio (United States); Khan, Mohammad K. [Department of Radiation Oncology, Emory University School of Medicine, Atlanta, Georgia (United States); Bena, James [Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (United States); Macklis, Roger [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio (United States); Singh, Arun D., E-mail: singha@ccf.org [Department of Ophthalmic Oncology, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio (United States)

    2012-11-01

    Purpose: To generate a vision prognostication model after plaque brachytherapy for uveal melanoma. Methods and Materials: All patients with primary single ciliary body or choroidal melanoma treated with iodine-125 or ruthenium-106 plaque brachytherapy between January 1, 2005, and June 30, 2010, were included. The primary endpoint was loss of visual acuity. Only patients with initial visual acuity better than or equal to 20/50 were used to evaluate visual acuity worse than 20/50 at the end of the study, and only patients with initial visual acuity better than or equal to 20/200 were used to evaluate visual acuity worse than 20/200 at the end of the study. Factors analyzed were sex, age, cataracts, diabetes, tumor size (basal dimension and apical height), tumor location, and radiation dose to the tumor apex, fovea, and optic disc. Univariate and multivariable Cox proportional hazards were used to determine the influence of baseline patient factors on vision loss. Kaplan-Meier curves (log rank analysis) were used to estimate freedom from vision loss. Results: Of 189 patients, 92% (174) were alive as of February 1, 2011. At presentation, visual acuity was better than or equal to 20/50 and better than or equal to 20/200 in 108 and 173 patients, respectively. Of these patients, 44.4% (48) had post-treatment visual acuity of worse than 20/50 and 25.4% (44) had post-treatment visual acuity worse than 20/200. By multivariable analysis, increased age (hazard ratio [HR] of 1.01 [1.00-1.03], P=.05), increase in tumor height (HR of 1.35 [1.22-1.48], P<.001), and a greater total dose to the fovea (HR of 1.01 [1.00-1.01], P<.001) were predictive of vision loss. This information was used to develop a nomogram predictive of vision loss. Conclusions: By providing a means to predict vision loss at 3 years after treatment, our vision prognostication model can be an important tool for patient selection and treatment counseling.

  13. Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis.

    Science.gov (United States)

    Carreiro, André V; Amaral, Pedro M T; Pinto, Susana; Tomás, Pedro; de Carvalho, Mamede; Madeira, Sara C

    2015-12-01

    Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. The Practicability of a Novel Prognostic Index (PI) Model and Comparison with Nottingham Prognostic Index (NPI) in Stage I-III Breast Cancer Patients Undergoing Surgical Treatment.

    Science.gov (United States)

    Wen, Jiahuai; Ye, Feng; Li, Shuaijie; Huang, Xiaojia; Yang, Lu; Xiao, Xiangsheng; Xie, Xiaoming

    2015-01-01

    Previous studies have indicated the prognostic value of various laboratory parameters in cancer patients. This study was to establish a prognostic index (PI) model for breast cancer patients based on the potential prognostic factors. A retrospective study of 1661 breast cancer patients who underwent surgical treatment between January 2002 and December 2008 at Sun Yat-sen University Cancer Center was conducted. Multivariate analysis (Cox regression model) was performed to determine the independent prognostic factors and a prognostic index (PI) model was devised based on these factors. Survival analyses were used to estimate the prognostic value of PI, and the discriminatory ability of PI was compared with Nottingham Prognostic Index (NPI) by evaluating the area under the receiver operating characteristics curves (AUC). The mean survival time of all participants was 123.6 months. The preoperative globulin >30.0g/L, triglyceride >1.10mmol/L and fibrinogen >2.83g/L were identified as risk factors for shorter cancer-specific survival. The novel prognostic index model was established and enrolled patients were classified as low- (1168 patients, 70.3%), moderate- (410 patients, 24.7%) and high-risk groups (83 patients, 5.0%), respectively. Compared with the low-risk group, higher risks of poor clinical outcome were indicated in the moderate-risk group [Hazard ratio (HR): 1.513, 95% confidence interval (CI): 1.169-1.959, p = 0.002] and high-risk group (HR: 2.481, 95%CI: 1.653-3.724, p< 0.001). The prognostic index based on three laboratory parameters was a novel and practicable prognostic tool. It may serve as complement to help predict postoperative survival in breast cancer patients.

  15. Context-Dependent Prognostics and Health Assessment: A Condition-Based Maintenance Approach That Supports Mission Compliance

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.; Kercel, S.W.

    1999-04-19

    In today's manufacturing environment, plants, systems, and equipment are being asked to perform at levels not thought possible a decade ago. The intent is to improve process operations and equipment reliability, availability, and maintainability without costly upgrades. Of course these gains must be achieved without impacting operational performance. Downsizing is also taking its toll on operations. Loss of personnel, particularly those who represent the corporate history, is depleting US industries of their valuable experiential base which has been relied on so heavily in the past. These realizations are causing companies to rethink their condition-based maintenance policies by moving away from reacting to equipment problems to taking a proactive approach by anticipating needs based on market and customer requirements. This paper describes a different approach to condition-based maintenance-context-dependent prognostics and health assessment. This diagnostic capability is developed around a context-dependent model that provides a capability to anticipate impending failures and determine machine performance over a protracted period of time. This prognostic capability links operational requirements to an economic performance model. In this context, a system may provide 100% operability with less than 100% functionality. This paradigm is used to facilitate optimal logistic supply and support.

  16. Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman...

  17. Cumulative Intracranial Tumor Volume Augments the Prognostic Value of Diagnosis-Specific Graded Prognostic Assessment Model for Survival in Patients with Melanoma Cerebral Metastases

    DEFF Research Database (Denmark)

    Hirshman, Brian R; Wilson, Bayard R; Ali, Mir Amaan

    2017-01-01

    intracranial tumor volume (CITV) into the ds-GPA model for melanoma augmented its prognostic value. OBJECTIVE: To determine whether or not CITV augments the ds-GPA prognostic scale for melanoma. METHODS: We analyzed the survival pattern of 344 melanoma patients with BM treated with stereotactic radiosurgery...... (SRS) at separate institutions and validated our findings in an independent cohort of 201 patients. The prognostic value of ds-GPA for melanoma was quantitatively compared with and without the addition of CITV using the net reclassification index (NRI > 0) and integrated discrimination improvement (IDI...... validated these findings that CITV improves the prognostic utility of melanoma ds-GPA in an independent cohort of 201 melanoma cohort. CONCLUSION: The prognostic value of the ds-GPA scale for melanoma BM is enhanced by the incorporation of CITV....

  18. An approach to understanding the interaction of hope and desire for explicit prognostic information among individuals with severe chronic obstructive pulmonary disease or advanced cancer.

    Science.gov (United States)

    Curtis, J Randall; Engelberg, Ruth; Young, Jessica P; Vig, Lisa K; Reinke, Lynn F; Wenrich, Marjorie D; McGrath, Barbara; McCown, Ellen; Back, Anthony L

    2008-05-01

    Physicians often report that they are reluctant to discuss prognosis for life-threatening illnesses with patients and family out of concern for destroying their hope, yet there is little empirical research describing how patients and family incorporate their needs for hope with desires for prognostic information. We conducted a qualitative study to examine the perspectives of patients, family, physicians, and nurses on the simultaneous need for supporting hope and discussing prognosis. We conducted in-depth longitudinal qualitative interviews with patients with either advanced cancer or severe chronic obstructive pulmonary disease (COPD), along with their family, physicians, and nurses. We used principles of grounded theory to analyze the transcripts and evaluated a conceptual model with four diagrams depicting different types of approaches to hope and prognostic information. We interviewed 55 patients, 36 family members, 31 physicians, and 25 nurses representing 220 hours of interviews. Asking patients directly "how much information" they wanted was, by itself, not useful for identifying information needs, but in-depth questioning identified variability in patients' and family members' desires for explicit prognostic information. All but 2 patients endorsed at least one of the diagrams concerning the interaction of hope and prognostic information and some patients described moving from one diagram to another over the course of their illness. Respondents also described two different approaches to communication about prognosis based on the diagram selected: two of the four diagrams suggested a direct approach and the other two suggested a cautious, indirect approach. This study found important variability in the ways different patients with life-limiting illnesses approach the interaction of wanting support for hope and prognostic information from their clinicians. The four-diagram approach may help clinicians understand individual patients and families, but further

  19. Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

    Science.gov (United States)

    Daigle, Matthew J.; Sankararaman, Shankar

    2013-01-01

    Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

  20. [Prognostic factors of postoperative delayed gastric emptying after pancreaticoduodenectomy: a predictive model].

    Science.gov (United States)

    Tan, H T; Zong, Y; Zhao, Z Q; Wu, L F; Liu, J; Sun, B; Jiang, H C

    2017-05-01

    Objective: To study the prognostic factors of delayed gastric emptying(DGE) after pancreaticoduodenectomy(PD) and construct a prognostic predictive model for clinical application. Methods: Clinic data of 401 consecutive patients who underwent PD between January 2012 and July 2016 in the First Affiliated Hospital of Harbin Medical University were retrospectively collected and analyzed. The patients were randomly selected to modeling group(n=299) and validation group(n=102) at a ratio of 3∶1. The data of modeling group were subjected to univariate and multivariate analysis for prognostic factors and to construct a prognostic predictive model of DGE after PD. The data of validation group were applied to test the prognostic predictive model. Results: DGE after PD occurred in 35 of 299 patients(11.7%) in the modeling group. The multivariate analysis of the modeling group showed that upper abdominal operation history(χ(2)=6.533, P=0.011), diabetes mellitus(χ(2)=17.872, P=0.000), preoperative hemoglobin predictive model of DGE after PD was constructed based on these factors and successfully tested. The area under the receiver operating characteristic(ROC) curve was 0.761(95%CI: 0.666-0.856) of the modeling group and 0.750(95% CI: 0.577-0.923) of the validation group. Conclusions: Upper abdominal operation history, diabetes mellitus, preoperative hemoglobinmodel is a valid tool to take precautions against DGE after PD.

  1. Prognostic immune-related gene models for breast cancer: a pooled analysis.

    Science.gov (United States)

    Zhao, Jianli; Wang, Ying; Lao, Zengding; Liang, Siting; Hou, Jingyi; Yu, Yunfang; Yao, Herui; You, Na; Chen, Kai

    2017-01-01

    Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER-) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER- breast cancer models achieved overall C-indices of 0.62-0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER-, LN+, and LN- breast

  2. A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

    Science.gov (United States)

    2014-10-02

    consumption, MOSFET voltage, diode reverse voltage, and 47K resistance consumption. ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT...methodology based on an equivalent circuit system simulation model developed from a commercially available switch-mode power supply, and empirical...of an integrated simulation model combining two empirical models in the application of SMPS: a circuit -based SMPS simulation model and the

  3. A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs.

    Science.gov (United States)

    Li, Xianqiu; An, Zhaoling; Li, Peihui; Liu, Haihua

    2017-09-01

    There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.

  4. A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data

    Directory of Open Access Journals (Sweden)

    Giulia Barbati

    2014-11-01

    Full Text Available Background: Focus of this work was on evaluating the prognostic accuracy of two approaches for modelling binary longitudinal outcomes, a Generalized Estimating Equation (GEE and a likelihood based method, Marginalized Transition Model (MTM, in which a transition model is combined with a marginal generalized linear model describing the average response as a function of measured predictors.Methods: A retrospective study on cardiovascular patients and a prospective study on sciatic pain were used to evaluate discrimination by computing the Area Under the Receiver-Operating-Characteristics curve, (AUC, the Integrated Discrimination Improvement (IDI and the Net Reclassification Improvement (NRI at different time occasions. Calibration was also evaluated. A simulation study was run in order to compare model’s performance in a context of a perfect knowledge of the data generating mechanism. Results: Similar regression coefficients estimates and comparable calibration were obtained; an higher discrimination level for MTM was observed. No significant differences in calibration and MSE (Mean Square Error emerged in the simulation study, that instead confirmed the MTM higher discrimination level. Conclusions: The choice of the regression approach should depend on the scientific question being addressed, i.e. if the overall population-average and calibration or the subject-specific patterns and discrimination are the objectives of interest, and some recently proposed discrimination indices are useful in evaluating predictive accuracy also in a context of longitudinal studies.

  5. The search for stable prognostic models in multiple imputed data sets

    NARCIS (Netherlands)

    Vergouw, D.; Heijmans, M.W.; Peat, G.M.; Kuijpers, T.; Croft, P.R.; de Vet, H.C.W.; van der Horst, H.E.; van der Windt, D.A.W.M.

    2010-01-01

    Background: In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods: Models were constructed

  6. Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model.

    Science.gov (United States)

    Audureau, Etienne; Chivet, Anaïs; Ursu, Renata; Corns, Robert; Metellus, Philippe; Noel, Georges; Zouaoui, Sonia; Guyotat, Jacques; Le Reste, Pierre-Jean; Faillot, Thierry; Litre, Fabien; Desse, Nicolas; Petit, Antoine; Emery, Evelyne; Lechapt-Zalcman, Emmanuelle; Peltier, Johann; Duntze, Julien; Dezamis, Edouard; Voirin, Jimmy; Menei, Philippe; Caire, François; Dam Hieu, Phong; Barat, Jean-Luc; Langlois, Olivier; Vignes, Jean-Rodolphe; Fabbro-Peray, Pascale; Riondel, Adeline; Sorbets, Elodie; Zanello, Marc; Roux, Alexandre; Carpentier, Antoine; Bauchet, Luc; Pallud, Johan

    2017-11-20

    We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.

  7. Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma.

    Science.gov (United States)

    Tadeo, Irene; Piqueras, Marta; Montaner, David; Villamón, Eva; Berbegall, Ana P; Cañete, Adela; Navarro, Samuel; Noguera, Rosa

    2014-02-01

    Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.

  8. Prognostic biopsy of choroidal melanoma: an optimised surgical and laboratory approach.

    Science.gov (United States)

    Angi, Martina; Kalirai, Helen; Taktak, Azzam; Hussain, Rumana; Groenewald, Carl; Damato, Bertil E; Heimann, Heinrich; Coupland, Sarah E

    2017-08-01

    Accurate survival prognostication for patients with uveal melanoma (UM) enables effective patient counselling and permits personalised systemic surveillance for the early detection of metastases and, in high-risk patients, enrolment in any trials of systemic adjuvant therapy. The aim of this work is to determine the success of prognostic UM tumour biopsy using an improved surgical approach and optimised sample handling workflow. Patients with UM treated by primary radiotherapy between 2011 and 2013 and who underwent a prognostic biopsy with cytology, multiplex ligation-dependent probe amplification and/or microsatellite analysis were included. The main outcomes and measures were success of cytology and genetic studies, and surgical complications. The cohort comprised 232 patients with UM having a median age of 59 years (range, 25-82) at treatment. The median largest basal diameter was 11.4 mm (range, 4.1-20.8) and tumour height was 3.4 mm (range, 0.7-10.3). Ciliary body involvement was noted in 42 cases. Treatment consisted of Ru-106 brachytherapy in 151 cases (65%) and proton beam radiotherapy in 81 cases (35%). With improvements in surgical techniques and laboratory methods over time, cytology success increased from 92% (131/142) to 99% (89/90) and the numbers of samples with sufficient DNA for genetic testing increased from 79% (104/131) to 93% (83/89). Overall, chromosome 3 loss was noted in 64/187 (34%) cases. Surgical complications, including transient localised bleeding, vitreous haemorrhage and retinal perforation, decreased over time. Eight patients required additional surgery. Improved surgical techniques and laboratory methods yielded successful cytology and genetic information in the majority of cases. Analysis of data from 232 patients with uveal melanoma undergoing prognostic tumour biopsy demonstrated that improved surgical techniques and laboratory methods yielded successful cytology and genetic information in 99% and 89% of cases, respectively

  9. Melanoma prognostic model using tissue microarrays and genetic algorithms.

    Science.gov (United States)

    Gould Rothberg, Bonnie E; Berger, Aaron J; Molinaro, Annette M; Subtil, Antonio; Krauthammer, Michael O; Camp, Robert L; Bradley, William R; Ariyan, Stephan; Kluger, Harriet M; Rimm, David L

    2009-12-01

    As a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence. Protein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004. Multiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than -0.052, p21(WAF1) nuclear compartment AQUA score of more than 12.98, p16(INK4A) ln(non-nuclear/nuclear AQUA score ratio) of < or = -0.083, beta-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of < or = 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts. This multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy.

  10. Watershed modeling tools and data for prognostic and diagnostic

    Science.gov (United States)

    Chambel-Leitao, P.; Brito, D.; Neves, R.

    2009-04-01

    When eutrophication is considered an important process to control it can be accomplished reducing nitrogen and phosphorus losses from both point and nonpoint sources and helping to assess the effectiveness of the pollution reduction strategy. HARP-NUT guidelines (Guidelines on Harmonized Quantification and Reporting Procedures for Nutrients) (Borgvang & Selvik, 2000) are presented by OSPAR as the best common quantification and reporting procedures for calculating the reduction of nutrient inputs. In 2000, OSPAR HARP-NUT guidelines on a trial basis. They were intended to serve as a tool for OSPAR Contracting Parties to report, in a harmonized manner, their different commitments, present or future, with regard to nutrients under the OSPAR Convention, in particular the "Strategy to Combat Eutrophication". HARP-NUT Guidelines (Borgvang and Selvik, 2000; Schoumans, 2003) were developed to quantify and report on the individual sources of nitrogen and phosphorus discharges/losses to surface waters (Source Orientated Approach). These results can be compared to nitrogen and phosphorus figures with the total riverine loads measured at downstream monitoring points (Load Orientated Approach), as load reconciliation. Nitrogen and phosphorus retention in river systems represents the connecting link between the "Source Orientated Approach" and the "Load Orientated Approach". Both approaches are necessary for verification purposes and both may be needed for providing the information required for the various commitments. Guidelines 2,3,4,5 are mainly concerned with the sources estimation. They present a set of simple calculations that allow the estimation of the origin of loads. Guideline 6 is a particular case where the application of a model is advised, in order to estimate the sources of nutrients from diffuse sources associated with land use/land cover. The model chosen for this was SWAT (Arnold & Fohrer, 2005) model because it is suggested in the guideline 6 and because it

  11. Prognostics of Power MOSFET

    Science.gov (United States)

    Celaya, Jose Ramon; Saxena, Abhinav; Vashchenko, Vladislay; Saha, Sankalita; Goebel, Kai Frank

    2011-01-01

    This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and Gaussian process regression to perform prognostics. The approach is validated with experiments on 100V power MOSFETs. The failure mechanism for the stress conditions is determined to be die-attachment degradation. Change in ON-state resistance is used as a precursor of failure due to its dependence on junction temperature. The experimental data is augmented with a finite element analysis simulation that is based on a two-transistor model. The simulation assists in the interpretation of the degradation phenomena and SOA (safe operation area) change.

  12. Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

    Directory of Open Access Journals (Sweden)

    Buddhi Arachchige

    2017-11-01

    Full Text Available This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC and State of Life (SOL by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge time with cycling has also been included, integrating both EOL (End of Life and EOD prediction models in order to get more accuracy in the estimations.

  13. Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model-Based Descriptor Consistent Across Processes, Phase 1 Final Report Context-Dependent Prognostics and Health Assessment: A New Paradigm for Condition-based Maintenance SBIR Topic No. N98-114

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.; Dress, W.B.; Kercel, S.W.

    1999-06-01

    The objective of this research, and subsequent testing, was to identify specific features of cavitation that could be used as a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor is based on the physics of the phenomena, capturing the salient features of the process dynamics. The test methodology and approach were developed to make the cavitation features the dominant effect in the process and collected signatures. This would allow the accurate characterization of the salient cavitation features at different operational states. By developing such an abstraction, these attributes can be used as a general diagnostic for a system or any of its components. In this study, the particular focus will be pumps. As many as 90% of pump failures are catastrophic. They seem to be operating normally and fail abruptly without warning. This is true whether the failure is sudden hardware damage requiring repair, such as a gasket failure, or a transition into an undesired operating mode, such as cavitation. This means that conventional diagnostic methods fail to predict 90% of incipient failures and that in addressing this problem, model-based methods can add value where it is actually needed.

  14. Clinical prediction of 5-year survival in systemic sclerosis: validation of a simple prognostic model in EUSTAR centres

    NARCIS (Netherlands)

    Fransen, J.; Popa-Diaconu, D.A.; Hesselstrand, R.; Carreira, P.; Valentini, G.; Beretta, L.; Airo, P.; Inanc, M.; Ullman, S.; Balbir-Gurman, A.; Sierakowski, S.; Allanore, Y.; Czirjak, L.; Riccieri, V.; Giacomelli, R.; Gabrielli, A.; Riemekasten, G.; Matucci-Cerinic, M.; Farge, D.; Hunzelmann, N.; Hoogen, F.H. Van den; Vonk, M.C.

    2011-01-01

    OBJECTIVE: Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually

  15. Prognostic models for physical capacity at discharge and 1 year postdischarge from rehabilitation in persons with spinal cord injury

    NARCIS (Netherlands)

    Haisma, J.A.; van der Woude, L.H.V.; Stam, H.J.; Bergen, M.P.; Sluis, T.A.; de Groot, S.; Dallmeijer, A.J.; Bussmann, J.B.J.

    2007-01-01

    Haisma JA, van der Woude LH, Stam HJ, Bergen MP, Sluis TA, de Groot S, Dallmeijer AJ, Bussmann JB. Prognostic models for physical capacity at discharge and 1 year postdischarge from rehabilitation in persons with spinal cord injury. Objective: To develop prognostic models for physical capacity at

  16. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort : prospective multicentre cohort study

    NARCIS (Netherlands)

    Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

    2016-01-01

    OBJECTIVE: To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. DESIGN: External validation of all published prognostic models in

  17. Various approaches to the modelling of large scale 3-dimensional circulation in the Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Shaji, C.; Bahulayan, N.; Rao, A.D.; Dube, S.K.

    In this paper, the three different approaches to the modelling of large scale 3-dimensional flow in the ocean such as the diagnostic, semi-diagnostic (adaptation) and the prognostic are discussed in detail. Three-dimensional solutions are obtained...

  18. Prognostic Model for Survival in Patients With Early Stage Cervical Cancer

    NARCIS (Netherlands)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J.; Stalpers, Lukas J. A.; Schilthuis, Marten S.; van der Steeg, Jan Willem; Burger, Matthé P. M.; Buist, Marrije R.

    2011-01-01

    BACKGROUND: In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer

  19. Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term

    NARCIS (Netherlands)

    van der Velde-Visser, S.D.; Hermes, W.; Twisk, J; Franx, A.|info:eu-repo/dai/nl/157009939; Pampus, M.G.; Koopmans, C.; Mol, B. W J; de Groot, J.C.M.J.

    2017-01-01

    Introduction The association between hypertensive pregnancy disorders and cardiovascular disease later in life is well described. In this study we aim to develop a prognostic model from patients characteristics known before, early in, during and after pregnancy to identify women at increased risk of

  20. Prognostic model for survival in patients with early stage cervical cancer.

    Science.gov (United States)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

    2011-02-15

    In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

  1. The search for stable prognostic models in multiple imputed data sets

    Directory of Open Access Journals (Sweden)

    de Vet Henrica CW

    2010-09-01

    Full Text Available Abstract Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B and Multiple imputation (MI. The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study. Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.

  2. Quantitative Assessment of Arrhythmia Using Non-linear Approach: A Non-invasive Prognostic Tool

    Science.gov (United States)

    Chakraborty, Monisha; Ghosh, Dipak

    2017-12-01

    Accurate prognostic tool to identify severity of Arrhythmia is yet to be investigated, owing to the complexity of the ECG signal. In this paper, we have shown that quantitative assessment of Arrhythmia is possible using non-linear technique based on "Hurst Rescaled Range Analysis". Although the concept of applying "non-linearity" for studying various cardiac dysfunctions is not entirely new, the novel objective of this paper is to identify the severity of the disease, monitoring of different medicine and their dose, and also to assess the efficiency of different medicine. The approach presented in this work is simple which in turn will help doctors in efficient disease management. In this work, Arrhythmia ECG time series are collected from MIT-BIH database. Normal ECG time series are acquired using POLYPARA system. Both time series are analyzed in thelight of non-linear approach following the method "Rescaled Range Analysis". The quantitative parameter, "Fractal Dimension" (D) is obtained from both types of time series. The major finding is that Arrhythmia ECG poses lower values of D as compared to normal. Further, this information can be used to access the severity of Arrhythmia quantitatively, which is a new direction of prognosis as well as adequate software may be developed for the use of medical practice.

  3. Updating and prospective validation of a prognostic model for high sickness absence.

    Science.gov (United States)

    Roelen, C A M; Heymans, M W; Twisk, J W R; van Rhenen, W; Pallesen, S; Bjorvatn, B; Moen, B E; Magerøy, N

    2015-01-01

    To further develop and validate a Dutch prognostic model for high sickness absence (SA). Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by adding person-related (age, gender, marital status, children at home, and coping strategies), health-related (BMI, physical activity, smoking, and caffeine and alcohol intake), and work-related (job satisfaction, job demands, decision latitude, social support at work, and both work-to-family and family-to-work spillover) variables. The updated model was then prospectively validated for predictions at wave 3. 1,557 (77 %) nurses had complete data at wave 2 and 1,342 (65 %) at wave 3. The risk of high SA was under-estimated by the Dutch model, but discrimination between high-risk and low-risk nurses was fair after re-calibration to the Norwegian data. Gender, marital status, BMI, physical activity, smoking, alcohol intake, job satisfaction, job demands, decision latitude, support at the workplace, and work-to-family spillover were identified as potential predictors of high SA. However, these predictors did not improve the model's discriminative ability, which remained fair at wave 3. The prognostic model correctly identifies 73 % of Norwegian nurses at risk of high SA, although additional predictors are needed before the model can be used to screen working populations for risk of high SA.

  4. Reduced nonlinear prognostic model construction from high-dimensional data

    Science.gov (United States)

    Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

    2017-04-01

    Construction of a data-driven model of evolution operator using universal approximating functions can only be statistically justified when the dimension of its phase space is small enough, especially in the case of short time series. At the same time in many applications real-measured data is high-dimensional, e.g. it is space-distributed and multivariate in climate science. Therefore it is necessary to use efficient dimensionality reduction methods which are also able to capture key dynamical properties of the system from observed data. To address this problem we present a Bayesian approach to an evolution operator construction which incorporates two key reduction steps. First, the data is decomposed into a set of certain empirical modes, such as standard empirical orthogonal functions or recently suggested nonlinear dynamical modes (NDMs) [1], and the reduced space of corresponding principal components (PCs) is obtained. Then, the model of evolution operator for PCs is constructed which maps a number of states in the past to the current state. The second step is to reduce this time-extended space in the past using appropriate decomposition methods. Such a reduction allows us to capture only the most significant spatio-temporal couplings. The functional form of the evolution operator includes separately linear, nonlinear (based on artificial neural networks) and stochastic terms. Explicit separation of the linear term from the nonlinear one allows us to more easily interpret degree of nonlinearity as well as to deal better with smooth PCs which can naturally occur in the decompositions like NDM, as they provide a time scale separation. Results of application of the proposed method to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical

  5. Electrohydraulic Servomechanisms Affected by Multiple Failures: A Model-Based Prognostic Method Using Genetic Algorithms

    OpenAIRE

    Dalla Vedova, Matteo Davide Lorenzo; Maggiore, Paolo

    2016-01-01

    In order to detect incipient failures due to a progressive wear of a primary flight command electro hydraulic actuator (EHA), prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able...

  6. Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data

    Directory of Open Access Journals (Sweden)

    Ghosh Debashis

    2004-12-01

    Full Text Available Abstract Background An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. Results By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. Conclusion The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta

  7. Population-specific prognostic models are needed to stratify outcomes for African-Americans with diffuse large B-cell lymphoma.

    Science.gov (United States)

    Chen, Qiushi; Ayer, Turgay; Nastoupil, Loretta J; Koff, Jean L; Staton, Ashley D; Chhatwal, Jagpreet; Flowers, Christopher R

    2016-01-01

    Diffuse large B-cell lymphoma (DLBCL) demonstrates significant racial differences in age of onset, stage, and survival. To examine whether population-specific models improve prediction of outcomes for African-American (AA) patients with DLBCL, we utilized Surveillance, Epidemiology, and End Results data and compared stratification by the international prognostic index (IPI) in general and AA populations. We also constructed and compared prognostic models for general and AA populations using multivariable logistic regression (LR) and artificial neural network approaches. While the IPI adequately stratified outcomes for the general population, it failed to separate AA DLBCL patients into distinct risk groups. Our AA LR model identified age ≥ 55 (odds ratio 0.45, [95% CI: 0.36, 0.56], male sex (0.75, [0.60, 0.93]), and stage III/IV disease (0.43, [0.34, 0.54]) as adverse predictors of 5-year survival for AA patients. In addition, general-population prognostic models were poorly calibrated for AAs with DLBCL, indicating a need for validated AA-specific prognostic models.

  8. Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

    Science.gov (United States)

    Kulkarni, Chetan S.; Celaya, Jose R.; Goebel, Kai; Biswas, Gautam

    2012-01-01

    Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

  9. Physical Modeling for Anomaly Diagnostics and Prognostics Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Ridgetop developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate...

  10. Prognostic Modeling of Valve Degradation within Power Stations

    Science.gov (United States)

    2014-10-02

    approach can be generated easily. The use of simulation can also satisfy the requirements stated by Wang et al. (2008) for a successful implementation...Simulation and Visualisation Based Training, Marine Electrical and Control Systems Safety Conference, (MECSS 2013), October 2-3, Amsterdam Heng, A

  11. Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

    Directory of Open Access Journals (Sweden)

    Gautam Biswas

    2012-12-01

    Full Text Available This paper presents a model-driven methodology for predict- ing the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avion- ics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remain- ing useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.

  12. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  13. A prognostic pollen emissions model for climate models (PECM1.0)

    Science.gov (United States)

    Wozniak, Matthew C.; Steiner, Allison L.

    2017-11-01

    We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the

  14. A prognostic pollen emissions model for climate models (PECM1.0

    Directory of Open Access Journals (Sweden)

    M. C. Wozniak

    2017-11-01

    Full Text Available We develop a prognostic model called Pollen Emissions for Climate Models (PECM for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus, evergreen needleleaf trees (Cupressaceae, Pinaceae, grasses (Poaceae; C3, C4, and ragweed (Ambrosia. This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4 over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1 a taxa-specific land cover database, phenology, and emission potential, and (2 a plant functional type (PFT land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions

  15. Model Updating and Uncertainty Management for Aircraft Prognostic Systems Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This proposal addresses the integration of physics-based damage propagation models with diagnostic measures of current state of health in a mathematically rigorous...

  16. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study.

    Directory of Open Access Journals (Sweden)

    Vincent J Gnanapragasam

    2016-08-01

    Full Text Available Over 80% of the nearly 1 million men diagnosed with prostate cancer annually worldwide present with localised or locally advanced non-metastatic disease. Risk stratification is the cornerstone for clinical decision making and treatment selection for these men. The most widely applied stratification systems use presenting prostate-specific antigen (PSA concentration, biopsy Gleason grade, and clinical stage to classify patients as low, intermediate, or high risk. There is, however, significant heterogeneity in outcomes within these standard groupings. The International Society of Urological Pathology (ISUP has recently adopted a prognosis-based pathological classification that has yet to be included within a risk stratification system. Here we developed and tested a new stratification system based on the number of individual risk factors and incorporating the new ISUP prognostic score.Diagnostic clinicopathological data from 10,139 men with non-metastatic prostate cancer were available for this study from the Public Health England National Cancer Registration Service Eastern Office. This cohort was divided into a training set (n = 6,026; 1,557 total deaths, with 462 from prostate cancer and a testing set (n = 4,113; 1,053 total deaths, with 327 from prostate cancer. The median follow-up was 6.9 y, and the primary outcome measure was prostate-cancer-specific mortality (PCSM. An external validation cohort (n = 1,706 was also used. Patients were first categorised as low, intermediate, or high risk using the current three-stratum stratification system endorsed by the National Institute for Health and Care Excellence (NICE guidelines. The variables used to define the groups (PSA concentration, Gleason grading, and clinical stage were then used to sub-stratify within each risk category by testing the individual and then combined number of risk factors. In addition, we incorporated the new ISUP prognostic score as a discriminator. Using this approach, a

  17. Introduction of prognostic rain in ECHAM5: design and Single Column Model simulations

    OpenAIRE

    Posselt, R.; Lohmann, U.

    2007-01-01

    International audience; Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG). To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoc...

  18. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud

    2017-01-01

    The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...... monitoring, fault detection and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution...

  19. Introduction of prognostic rain in ECHAM5: design and single column model simulations

    Directory of Open Access Journals (Sweden)

    R. Posselt

    2008-06-01

    Full Text Available Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG. To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoconversion rate than the standard ECHAM5 but shifts the emphasis towards the accretion rates in accordance with observations. ECHAM5-PROG is tested and evaluated with Single Column Model (SCM simulations for two cases: the marine stratocumulus study EPIC (October 2001 and the continental mid-latitude ARM Cloud IOP (shallow frontal cloud case – March 2000. In case of heavy precipitation events, the prognostic equations for rain hardly affect the amount and timing of precipitation at the surface in different SCM simulations because heavy rain depends mainly on the large-scale forcing. In case of thin, drizzling clouds (i.e., stratocumulus, surface precipitation is sensitive to the number of sub-time steps used in the prognostic rain scheme. Cloud microphysical quantities, such as cloud liquid and rain water within the atmosphere, are sensitive to the number of sub-time steps in both considered cases. This results from the decreasing autoconversion rate and increasing accretion rate.

  20. Climate feedbacks in a general circulation model incorporating prognostic clouds

    Energy Technology Data Exchange (ETDEWEB)

    Colman, R.; Fraser, J. [Bureau of Meteorology Research Centre, Melbourne, Vic. (Australia); Rotstayn, L. [CSIRO Atmospheric Research, Aspendale (Australia)

    2001-11-01

    This study performs a comprehensive feedback analysis on the Bureau of Meteorology Research Centre General Circulation Model, quantifying all important feedbacks operating under an increase in atmospheric CO{sub 2}. The individual feedbacks are analysed in detail, using an offline radiation perturbation method, looking at long- and shortwave components, latitudinal distributions, cloud impacts, non-linearities under 2xCO{sub 2} and 4xCO{sub 2} warmings and at interannual variability. The water vapour feedback is divided into terms due to moisture height and amount changes. The net cloud feedback is separated into terms due to cloud amount, height, water content, water phase, physical thickness and convective cloud fraction. Globally the most important feedbacks were found to be (from strongest positive to strongest negative) those due to water vapour, clouds, surface albedo, lapse rate and surface temperature. For the longwave (LW) response the most important term of the cloud 'optical property' feedbacks is due to the water content. In the shortwave (SW), both water content and water phase changes are important. Cloud amount and height terms are also important for both LW and SW. Feedbacks due to physical cloud thickness and convective cloud fraction are found to be relatively small. All cloud component feedbacks (other than height) produce conflicting LW/SW feedbacks in the model. Furthermore, the optical property and cloud fraction feedbacks are also of opposite sign. The result is that the net cloud feedback is the (relatively small) product of conflicting physical processes. Non-linearities in the feedbacks are found to be relatively small for all but the surface albedo response and some cloud component contributions. The cloud impact on non-cloud feedbacks is also discussed: greatest impact is on the surface albedo, but impact on water vapour feedback is also significant. The analysis method here proves to be a powerful tool for detailing the

  1. A simplified prognostic model to predict mortality in patients with acute variceal bleeding.

    Science.gov (United States)

    Lee, Han Hee; Park, Jae Myung; Han, Seunghoon; Park, Sung Min; Kim, Hee Yeon; Oh, Jung Hwan; Kim, Chang Wook; Yoon, Seung Kew; Choi, Myung-Gyu

    2017-11-24

    Acute variceal bleeding (AVB) is a major cause of death in patients with liver cirrhosis. The aim of this study was to investigate mortality predictors and develop a new simple prognostic model using easily verified factors at admission in AVB patients. Between January 2009 and May 2015, 333 consecutive patients with AVB were included. A simplified prognostic model was developed using multiple logistic regression after identifying significant predictors of 6-week mortality. Mortality prediction accuracy was assessed with area under the receiver operating characteristic (AUROC) curve. We compared the new model to existing models of model for end-stage liver disease (MELD) and Child-Pugh scores. The 6-week overall mortality rate was 12.9%. Multivariate analysis showed that C-reactive protein (CRP), total bilirubin, and the international normalized ratio were independent predictors of mortality. A new logistic model using these variables was developed. This model's AUROC was 0.834, which was significantly higher than that of MELD (0.764) or Child-Pugh scores (0.699). Two external validation studies showed that the AUROC of our model was consistently higher than 0.8. Our new simplified model accurately and consistently predicted 6-week mortality in patients with AVB using objective variables measured at admission. Our system can be used to identify high risk AVB patients. Copyright © 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

  2. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  3. Accounting for treatment use when validating a prognostic model: a simulation study

    Directory of Open Access Journals (Sweden)

    Romin Pajouheshnia

    2017-07-01

    Full Text Available Abstract Background Prognostic models often show poor performance when applied to independent validation data sets. We illustrate how treatment use in a validation set can affect measures of model performance and present the uses and limitations of available analytical methods to account for this using simulated data. Methods We outline how the use of risk-lowering treatments in a validation set can lead to an apparent overestimation of risk by a prognostic model that was developed in a treatment-naïve cohort to make predictions of risk without treatment. Potential methods to correct for the effects of treatment use when testing or validating a prognostic model are discussed from a theoretical perspective.. Subsequently, we assess, in simulated data sets, the impact of excluding treated individuals and the use of inverse probability weighting (IPW on the estimated model discrimination (c-index and calibration (observed:expected ratio and calibration plots in scenarios with different patterns and effects of treatment use. Results Ignoring the use of effective treatments in a validation data set leads to poorer model discrimination and calibration than would be observed in the untreated target population for the model. Excluding treated individuals provided correct estimates of model performance only when treatment was randomly allocated, although this reduced the precision of the estimates. IPW followed by exclusion of the treated individuals provided correct estimates of model performance in data sets where treatment use was either random or moderately associated with an individual's risk when the assumptions of IPW were met, but yielded incorrect estimates in the presence of non-positivity or an unobserved confounder. Conclusions When validating a prognostic model developed to make predictions of risk without treatment, treatment use in the validation set can bias estimates of the performance of the model in future targeted individuals, and

  4. Simplified prognostic model for critically ill patients in resource limited settings in South Asia.

    Science.gov (United States)

    Haniffa, Rashan; Mukaka, Mavuto; Munasinghe, Sithum Bandara; De Silva, Ambepitiyawaduge Pubudu; Jayasinghe, Kosala Saroj Amarasiri; Beane, Abi; de Keizer, Nicolette; Dondorp, Arjen M

    2017-10-17

    Current critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission. This was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures. Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model. Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models. Model 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence

  5. A hybrid prognostic model for multistep ahead prediction of machine condition

    Science.gov (United States)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

  6. Prognostic models in acute pulmonary embolism: a systematic review and meta-analysis

    Science.gov (United States)

    Elias, Antoine; Mallett, Susan; Daoud-Elias, Marie; Poggi, Jean-Noël; Clarke, Mike

    2016-01-01

    Objective To review the evidence for existing prognostic models in acute pulmonary embolism (PE) and determine how valid and useful they are for predicting patient outcomes. Design Systematic review and meta-analysis. Data sources OVID MEDLINE and EMBASE, and The Cochrane Library from inception to July 2014, and sources of grey literature. Eligibility criteria Studies aiming at constructing, validating, updating or studying the impact of prognostic models to predict all-cause death, PE-related death or venous thromboembolic events up to a 3-month follow-up in patients with an acute symptomatic PE. Data extraction Study characteristics and study quality using prognostic criteria. Studies were selected and data extracted by 2 reviewers. Data analysis Summary estimates (95% CI) for proportion of risk groups and event rates within risk groups, and accuracy. Results We included 71 studies (44 298 patients). Among them, 17 were model construction studies specific to PE prognosis. The most validated models were the PE Severity Index (PESI) and its simplified version (sPESI). The overall 30-day mortality rate was 2.3% (1.7% to 2.9%) in the low-risk group and 11.4% (9.9% to 13.1%) in the high-risk group for PESI (9 studies), and 1.5% (0.9% to 2.5%) in the low-risk group and 10.7% (8.8% to12.9%) in the high-risk group for sPESI (11 studies). PESI has proved clinically useful in an impact study. Shifting the cut-off or using novel and updated models specifically developed for normotensive PE improves the ability for identifying patients at lower risk for early death or adverse outcome (0.5–1%) and those at higher risk (up to 20–29% of event rate). Conclusions We provide evidence-based information about the validity and utility of the existing prognostic models in acute PE that may be helpful for identifying patients at low risk. Novel models seem attractive for the high-risk normotensive PE but need to be externally validated then be assessed in impact studies. PMID

  7. Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

    Science.gov (United States)

    2014-12-23

    Simulink R© en- vironment for simulating engine model of the 90,000 lb thrust class (Frederick et al., 2007). Using a number of editable input parameters... Chemical Engineering Transactions, 33, 115-120. Gouriveau, R., & Zerhouni, N. (2012). Connexionist- systems-based long term prediction approaches for...are five datasets from a turbo- fan engine simulation model - C-MAPSS (Commercial Mod- ular Aero-Propulsion System Simulation) (Frederick, DeCas- tro

  8. A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

    Directory of Open Access Journals (Sweden)

    Qihua Tan

    2009-01-01

    Full Text Available Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances.

  9. A combinatory approach for selecting prognostic genes in microarray studies of tumour survivals

    DEFF Research Database (Denmark)

    Tan, Qihua; Thomassen, Mads; Jochumsen, Kirsten M

    2009-01-01

    differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations...... for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates...

  10. A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics

    Directory of Open Access Journals (Sweden)

    Ning Wang

    2014-01-01

    Full Text Available Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM, including the Hidden Semi-Markov Model (HSMM: (1 it allows explicit modeling of state transition probabilities between the states; (2 it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3 it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

  11. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    Science.gov (United States)

    Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

    2016-08-30

     To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  12. Model-based prognostics for batteries which estimates useful life and uses a probability density function

    Science.gov (United States)

    Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor)

    2012-01-01

    This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

  13. Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms

    Directory of Open Access Journals (Sweden)

    Thamo Sutharssan

    2015-07-01

    Full Text Available Prognostics and health management (PHM has become an important component of many engineering systems and products, where algorithms are used to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL. PHM can provide many advantages to users and maintainers. Although primary goals are to ensure the safety, provide state of the health and estimate RUL of the components and systems, there are also financial benefits such as operational and maintenance cost reductions and extended lifetime. This study aims at reviewing the current status of algorithms and methods used to underpin different existing PHM approaches. The focus is on providing a structured and comprehensive classification of the existing state-of-the-art PHM approaches, data-driven approaches and algorithms.

  14. Geoinformational prognostic model of mudflows hazard and mudflows risk for the territory of Ukrainian Carpathians

    Science.gov (United States)

    Chepurna, Tetiana B.; Kuzmenko, Eduard D.; Chepurnyj, Igor V.

    2017-06-01

    The article is devoted to the geological issue of the space-time regional prognostication of mudflow hazard. The methodology of space-time prediction of mudflows hazard by creating GIS predictive model has been developed. Using GIS technologies the relevant and representative complex of significant influence of spatial and temporal factors, adjusted to use in the regional prediction of mudflows hazard, were selected. Geological, geomorphological, technological, climatic, and landscape factors have been selected as spatial mudflow factors. Spatial analysis is based on detection of a regular connection of spatial factor characteristics with spatial distribution of the mudflow sites. The function of a standard complex spatial index (SCSI) of the probability of the mudflow sites distribution has been calculated. The temporal, long-term prediction of the mudflows activity was based on the hypothesis of the regular reiteration of natural processes. Heliophysical, seismic, meteorological, and hydrogeological factors have been selected as time mudflow factors. The function of a complex index of long standing mudflow activity (CIMA) has been calculated. The prognostic geoinformational model of mudflow hazard up to 2020 year, a year of the next peak of the mudflows activity, has been created. Mudflow risks have been counted and carogram of mudflow risk assessment within the limits of administrative-territorial units has been built for 2020 year.

  15. A clinical prognostic model for the identification of low-risk patients with acute symptomatic pulmonary embolism and active cancer.

    Science.gov (United States)

    den Exter, Paul L; Gómez, Vicente; Jiménez, David; Trujillo-Santos, Javier; Muriel, Alfonso; Huisman, Menno V; Monreal, Manuel

    2013-01-01

    Physicians need a specific risk-stratification tool to facilitate safe and cost-effective approaches to the management of patients with cancer and acute pulmonary embolism (PE). The objective of this study was to develop a simple risk score for predicting 30-day mortality in patients with PE and cancer by using measures readily obtained at the time of PE diagnosis. Investigators randomly allocated 1,556 consecutive patients with cancer and acute PE from the international multicenter Registro Informatizado de la Enfermedad TromboEmbólica to derivation (67%) and internal validation (33%) samples. The external validation cohort for this study consisted of 261 patients with cancer and acute PE. Investigators compared 30-day all-cause mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. In the derivation sample, multivariable analyses produced the risk score, which contained six variables: age > 80 years, heart rate ≥ 110/min, systolic BP < 100 mm Hg, body weight < 60 kg, recent immobility, and presence of metastases. In the internal validation cohort (n = 508), the 22.2% of patients (113 of 508) classified as low risk by the prognostic model had a 30-day mortality of 4.4% (95% CI, 0.6%-8.2%) compared with 29.9% (95% CI, 25.4%-34.4%) in the high-risk group. In the external validation cohort, the 18% of patients (47 of 261) classified as low risk by the prognostic model had a 30-day mortality of 0%, compared with 19.6% (95% CI, 14.3%-25.0%) in the high-risk group. The developed clinical prediction rule accurately identifies low-risk patients with cancer and acute PE.

  16. A Prognostic Model for One-year Mortality in Patients Requiring Prolonged Mechanical Ventilation

    Science.gov (United States)

    Carson, Shannon S.; Garrett, Joanne; Hanson, Laura C.; Lanier, Joyce; Govert, Joe; Brake, Mary C.; Landucci, Dante L.; Cox, Christopher E.; Carey, Timothy S.

    2009-01-01

    Objective A measure that identifies patients who are at high risk of mortality after prolonged ventilation will help physicians communicate prognosis to patients or surrogate decision-makers. Our objective was to develop and validate a prognostic model for 1-year mortality in patients ventilated for 21 days or more. Design Prospective cohort study. Setting University-based tertiary care hospital Patients 300 consecutive medical, surgical, and trauma patients requiring mechanical ventilation for at least 21 days were prospectively enrolled. Measurements and Main Results Predictive variables were measured on day 21 of ventilation for the first 200 patients and entered into logistic regression models with 1-year and 3-month mortality as outcomes. Final models were validated using data from 100 subsequent patients. One-year mortality was 51% in the development set and 58% in the validation set. Independent predictors of mortality included requirement for vasopressors, hemodialysis, platelet count ≤150 ×109/L, and age ≥50. Areas under the ROC curve for the development model and validation model were 0.82 (se 0.03) and 0.82 (se 0.05) respectively. The model had sensitivity of 0.42 (se 0.12) and specificity of 0.99 (se 0.01) for identifying patients who had ≥90% risk of death at 1 year. Observed mortality was highly consistent with both 3- and 12-month predicted mortality. These four predictive variables can be used in a simple prognostic score that clearly identifies low risk patients (no risk factors, 15% mortality) and high risk patients (3 or 4 risk factors, 97% mortality). Conclusions Simple clinical variables measured on day 21 of mechanical ventilation can identify patients at highest and lowest risk of death from prolonged ventilation. PMID:18552692

  17. A simple but highly effective approach to evaluate the prognostic performance of gene expression signatures.

    Directory of Open Access Journals (Sweden)

    Maud H W Starmans

    Full Text Available BACKGROUND: Highly parallel analysis of gene expression has recently been used to identify gene sets or 'signatures' to improve patient diagnosis and risk stratification. Once a signature is generated, traditional statistical testing is used to evaluate its prognostic performance. However, due to the dimensionality of microarrays, this can lead to false interpretation of these signatures. PRINCIPAL FINDINGS: A method was developed to test batches of a user-specified number of randomly chosen signatures in patient microarray datasets. The percentage of random generated signatures yielding prognostic value was assessed using ROC analysis by calculating the area under the curve (AUC in six public available cancer patient microarray datasets. We found that a signature consisting of randomly selected genes has an average 10% chance of reaching significance when assessed in a single dataset, but can range from 1% to ∼40% depending on the dataset in question. Increasing the number of validation datasets markedly reduces this number. CONCLUSIONS: We have shown that the use of an arbitrary cut-off value for evaluation of signature significance is not suitable for this type of research, but should be defined for each dataset separately. Our method can be used to establish and evaluate signature performance of any derived gene signature in a dataset by comparing its performance to thousands of randomly generated signatures. It will be of most interest for cases where few data are available and testing in multiple datasets is limited.

  18. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

  19. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the international kidney cancer working group.

    Science.gov (United States)

    Manola, Judith; Royston, Patrick; Elson, Paul; McCormack, Jennifer Bacik; Mazumdar, Madhu; Négrier, Sylvie; Escudier, Bernard; Eisen, Tim; Dutcher, Janice; Atkins, Michael; Heng, Daniel Y C; Choueiri, Toni K; Motzer, Robert; Bukowski, Ronald

    2011-08-15

    To develop a single validated model for survival in metastatic renal cell carcinoma (mRCC) using a comprehensive international database. A comprehensive database of 3,748 patients including previously reported clinical prognostic factors was established by pooling patient-level data from clinical trials. Following quality control and standardization, descriptive statistics were generated. Univariate analyses were conducted using proportional hazards models. Multivariable analysis using a log-logistic model stratified by center and multivariable fractional polynomials was conducted to identify independent predictors of survival. Missing data were handled using multiple imputation methods. Three risk groups were formed using the 25th and 75th percentiles of the resulting prognostic index. The model was validated using an independent data set of 645 patients treated with tyrosine kinase inhibitor (TKI) therapy. Median survival in the favorable, intermediate and poor risk groups was 26.9 months, 11.5 months, and 4.2 months, respectively. Factors contributing to the prognostic index included treatment, performance status, number of metastatic sites, time from diagnosis to treatment, and pretreatment hemoglobin, white blood count, lactate dehydrogenase, alkaline phosphatase, and serum calcium. The model showed good concordance when tested among patients treated with TKI therapy (C statistic = 0.741, 95% CI: 0.714-0.768). Nine clinical factors can be used to model survival in mRCC and form distinct prognostic groups. The model shows utility among patients treated in the TKI era. ©2011 AACR.

  20. Datamining approaches for modeling tumor control probability.

    Science.gov (United States)

    Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

    2010-11-01

    Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

  1. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    NARCIS (Netherlands)

    Lamain-de Ruiter, M.; Kwee, A.; Naaktgeboren, C.A.; Groot, I. de; Evers, I.M.; Groenendaal, F.; Hering, Y.R.; Huisjes, A.J.M.; Kirpestein, C.; Monincx, W.M.; Siljee, J.E.; Zelfde, A. van't; Oirschot, C.M. van; Vankan-Buitelaar, S.A.; Vonk, M.A.A.W.; Wiegers, T.A.; Zwart, J.J.; Franx, A.; Moons, K.G.M.; Koster, M.P.H.

    2016-01-01

    Objective: To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. Design: External validation of all published prognostic models in

  2. A prognostic model of therapy-related myelodysplastic syndrome for predicting survival and transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Daver, Naval; Kim, Hawk; Dinardo, Courtney; Jabbour, Elias; Kadia, Tapan; Borthakur, Gautam; Pierce, Sherry; Shan, Jianqin; Cardenas-Turanzas, Marylou; Cortes, Jorge; Ravandi, Farhad; Wierda, William; Estrov, Zeev; Faderl, Stefan; Wei, Yue; Kantarjian, Hagop; Garcia-Manero, Guillermo

    2014-10-01

    We evaluated the characteristics of a cohort of patients with myelodysplastic syndrome (MDS) related to therapy (t-MDS) to create a prognostic model. We identified 281 patients with MDS who had received previous chemotherapy and/or radiotherapy for previous malignancy. Potential prognostic factors were determined using univariate and multivariate analyses. Multivariate Cox regression analysis identified 7 factors that independently predicted short survival in t-MDS: age ≥ 65 years (hazard ratio [HR], 1.63), Eastern Cooperative Oncology Group performance status 2-4 (HR, 1.86), poor cytogenetics (-7 and/or complex; HR, 2.47), World Health Organization MDS subtype (RARs or RAEB-1/2; HR, 1.92), hemoglobin (HR, 2.24), platelets (HR, 2.01), and transfusion dependency (HR, 1.59). These risk factors were used to create a prognostic model that segregated patients into 3 groups with distinct median overall survival: good (0-2 risk factors; 34 months), intermediate (3-4 risk factors; 12 months), and poor (5-7 risk factors; 5 months) (P < .001) and 1-year leukemia-free survival (96%, 84%, and 72%, respectively, P = .003). This model also identified distinct survival groups according to t-MDS therapy. In summary, we devised a prognostic model specifically for patients with t-MDS that predicted overall survival and leukemia-free survival. This model might facilitate the development of risk-adapted therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Prognostic Disclosures to Children: A Historical Perspective.

    Science.gov (United States)

    Sisk, Bryan A; Bluebond-Langner, Myra; Wiener, Lori; Mack, Jennifer; Wolfe, Joanne

    2016-09-01

    Prognostic disclosure to children has perpetually challenged clinicians and parents. In this article, we review the historical literature on prognostic disclosure to children in the United States using cancer as an illness model. Before 1948, there was virtually no literature focused on prognostic disclosure to children. As articles began to be published in the 1950s and 1960s, many clinicians and researchers initially recommended a "protective" approach to disclosure, where children were shielded from the harms of bad news. We identified 4 main arguments in the literature at this time supporting this "protective" approach. By the late 1960s, however, a growing number of clinicians and researchers were recommending a more "open" approach, where children were included in discussions of diagnosis, which at the time was often synonymous with a terminal prognosis. Four different arguments in the literature were used at this time supporting this "open" approach. Then, by the late 1980s, the recommended approach to prognostic disclosure in pediatrics shifted largely from "never tell" to "always tell." In recent years, however, there has been a growing appreciation for the complexity of prognostic disclosure in pediatrics. Current understanding of pediatric disclosure does not lead to simple "black-and-white" recommendations for disclosure practices. As with most difficult questions, we are left to balance competing factors on a case-by-case basis. We highlight 4 categories of current considerations related to prognostic disclosure in pediatrics, and we offer several approaches to prognostic disclosure for clinicians who care for these young patients and their families. Copyright © 2016 by the American Academy of Pediatrics.

  4. A Relevance Vector Machine-Based Approach with Application to Oil Sand Pump Prognostics

    Directory of Open Access Journals (Sweden)

    Peter W. Tse

    2013-09-01

    Full Text Available Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs with a sum of two exponential functions was developed to predict the remaining useful life (RUL of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers.

  5. A relevance vector machine-based approach with application to oil sand pump prognostics.

    Science.gov (United States)

    Hu, Jinfei; Tse, Peter W

    2013-09-18

    Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs) with a sum of two exponential functions was developed to predict the remaining useful life (RUL) of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers.

  6. Prognostic factors in gastric cancer evaluated by using Cox regression model.

    Science.gov (United States)

    Ghiandoni, G; Rocchi, M B; Signoretti, P; Belbusti, F

    1998-06-01

    To identify the most relevant short-term predictor variables in gastric cancer removal. A retrospective survival analysis executed by using the Cox regression model; the follow-up period is included between 18 and 90 months. A district general hospital surgery unit: "Divisione di Chirurgia Generale, Ospedale Civile di Urbino" (Marche, Italy). One hundred and twenty nine consecutive patients operated for gastric cancer. Surgery (total or subtotal gastrectomy). Survival times. Lymph node involvement (N) (p extension (T) (p < 0.001) and the age of the patients (p < 0.05) have been recognized as significant prognostic factors. Results show that the short-term prognosis largely depends on both the earliness of the diagnosis and the age of the patients.

  7. A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data

    NARCIS (Netherlands)

    Rutten, C.J.; Steeneveld, W.; Vernooij, J.C.M.; Huijps, K.; Nielen, M.; Hogeveen, H.

    2016-01-01

    A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first

  8. A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data

    NARCIS (Netherlands)

    Rutten, C J|info:eu-repo/dai/nl/353551031; Steeneveld, W|info:eu-repo/dai/nl/304833169; Vernooij, J C M|info:eu-repo/dai/nl/340304596; Huijps, K|info:eu-repo/dai/nl/304837881; Nielen, M|info:eu-repo/dai/nl/123535298; Hogeveen, H|info:eu-repo/dai/nl/126322864

    2016-01-01

    A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first

  9. Predicting stabilizing treatment outcomes for complex posttraumatic stress disorder and dissociative identity disorder: an expertise-based prognostic model

    NARCIS (Netherlands)

    Baars, E.W.; van der Hart, O.; Nijenhuis, E.R.S.; Chu, J.A.; Glas, G.; Draaijer, N.

    2011-01-01

    The purpose of this study was to develop an expertise-based prognostic model for the treatment of complex posttraumatic stress disorder (PTSD) and dissociative identity disorder (DID).We developed a survey in 2 rounds: In the first round we surveyed 42 experienced therapists (22 DID and 20 complex

  10. Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

    Energy Technology Data Exchange (ETDEWEB)

    Agarwal, Vivek [Idaho National Lab. (INL), Idaho Falls, ID (United States); Lybeck, Nancy J. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pham, Binh [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rusaw, Richard [Electric Power Research Inst. (EPRI), Palo Alto, CA (United States); Bickford, Randall [Expert Microsystems, Orangevale, CA (United States)

    2015-02-01

    Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Fault Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.

  11. Prognostic Modeling in Pathologic N1 Breast Cancer Without Elective Nodal Irradiation After Current Standard Systemic Management.

    Science.gov (United States)

    Yu, Jeong Il; Park, Won; Choi, Doo Ho; Huh, Seung Jae; Nam, Seok Jin; Kim, Seok Won; Lee, Jeong Eon; Kil, Won Ho; Im, Young-Hyuck; Ahn, Jin Seok; Park, Yeon Hee; Cho, Eun Yoon

    2015-08-01

    This study was conducted to establish a prognostic model in patients with pathologic N1 (pN1) breast cancer who have not undergone elective nodal irradiation (ENI) under the current standard management and to suggest possible indications for ENI. We performed a retrospective study with patients with pN1 breast cancer who received the standard local and preferred adjuvant chemotherapy treatment without neoadjuvant chemotherapy and ENI from January 2005 to June 2011. Most of the indicated patients received endocrine and trastuzumab therapy. In 735 enrolled patients, the median follow-up period was 58.4 months (range, 7.2-111.3 months). Overall, 55 recurrences (7.4%) developed, and locoregional recurrence was present in 27 patients (3.8%). Recurrence-free survival was significantly related to lymphovascular invasion (P = .04, hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.03-2.88), histologic grade (P = .03, HR, 2.57; 95% CI, 1.05-6.26), and nonluminal A subtype (P = .02, HR, 3.04; 95% CI, 1.23-7.49) in multivariate analysis. The prognostic model was established by these 3 prognostic factors. Recurrence-free survival was less than 90% at 5 years in cases with 2 or 3 factors. The prognostic model has stratified risk groups in pN1 breast cancer without ENI. Patients with 2 or more factors should be considered for ENI. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Verification of a prognostic meteorological and air pollution model for year-long predictions in the Kwinana industrial region of Western Australia

    Energy Technology Data Exchange (ETDEWEB)

    Hurley, P.J. [CSIRO Atmospheric Research, Aspendale, Vic (Australia); Blockley, A.; Rayner, K. [Department of Environmental Protection, Perth, WA (Australia)

    2001-04-01

    A prognostic air pollution model (TAPM) has been used to predict meteorology and sulphur dioxide concentration in the Kwinana industrial region of Western Australia for 1997, with a view to verifying TAPM for use in environmental impact assessments and associated air pollution studies. The regulatory plume model, DISPMOD, developed for the Kwinana region has also been run using both an observationally based meteorological file (denoted DISPMOD-O) and using a TAPM-based meteorological file (denoted DISPMOD-T). TAPM predictions of the meteorology for 1997 compare well with the observed values at each of the five monitoring sites. Root mean square error and index of agreement values for temperature and winds indicate that TAPM performs well at predicting the meteorology, compared to the performance of similar models from other studies. The yearly average, 99.9 percentile, maximum and mean of the top 10 ground-level sulphur dioxide concentrations for 1997 were predicted well by all of the model runs, although DISPMOD-O and DISPMOD-T tended to overpredict extreme statistics at sites furthest from the sources. Overall, TAPM performed better than DISPMOD-O, which in turn performed better than DISPMOD-T, for all statistics considered, but we consider that all three sets of results are sufficiently accurate for regulatory applications. The mean of the top ten concentrations is generally considered to be a robust performance statistic for air pollution applications, and we show that compared to the site-averaged observed value of 95{mu}gm{sup -3}, TAPM predicted 94{mu}gm{sup -3}, DISPMOD-O predicted 111{mu}gm{sup -3} and DISPMOD-T predicted 125{mu}gm{sup -3}. The results indicate that the prognostic meteorological and air pollution approach to regulatory modelling used by TAPM, gives comparable or better results than the current regulatory approach used in the Kwinana region (DISPMOD), and also indicates that the approach of using a currently accepted regulatory model with a

  13. Predicting stabilizing treatment outcomes for complex posttraumatic stress disorder and dissociative identity disorder: an expertise-based prognostic model.

    Science.gov (United States)

    Baars, Erik W; van der Hart, Onno; Nijenhuis, Ellert R S; Chu, James A; Glas, Gerrit; Draijer, Nel

    2011-01-01

    The purpose of this study was to develop an expertise-based prognostic model for the treatment of complex posttraumatic stress disorder (PTSD) and dissociative identity disorder (DID). We developed a survey in 2 rounds: In the first round we surveyed 42 experienced therapists (22 DID and 20 complex PTSD therapists), and in the second round we surveyed a subset of 22 of the 42 therapists (13 DID and 9 complex PTSD therapists). First, we drew on therapists' knowledge of prognostic factors for stabilization-oriented treatment of complex PTSD and DID. Second, therapists prioritized a list of prognostic factors by estimating the size of each variable's prognostic effect; we clustered these factors according to content and named the clusters. Next, concept mapping methodology and statistical analyses (including principal components analyses) were used to transform individual judgments into weighted group judgments for clusters of items. A prognostic model, based on consensually determined estimates of effect sizes, of 8 clusters containing 51 factors for both complex PTSD and DID was formed. It includes the clusters lack of motivation, lack of healthy relationships, lack of healthy therapeutic relationships, lack of other internal and external resources, serious Axis I comorbidity, serious Axis II comorbidity, poor attachment, and self-destruction. In addition, a set of 5 DID-specific items was constructed. The model is supportive of the current phase-oriented treatment model, emphasizing the strengthening of the therapeutic relationship and the patient's resources in the initial stabilization phase. Further research is needed to test the model's statistical and clinical validity.

  14. Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

    Science.gov (United States)

    Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

    2015-06-01

    To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

  15. A simple prognostic model for overall survival in metastatic renal cell carcinoma

    Science.gov (United States)

    Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

    2016-01-01

    Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

  16. Incorporating a prognostic representation of marine nitrogen fixers into the global ocean biogeochemical model HAMOCC

    Science.gov (United States)

    Paulsen, Hanna; Ilyina, Tatiana; Six, Katharina D.; Stemmler, Irene

    2017-03-01

    Nitrogen (N2) fixation is a major source of bioavailable nitrogen to the euphotic zone, thereby exerting an important control on ocean biogeochemical cycling. This paper presents the incorporation of prognostic N2 fixers into the HAMburg Ocean Carbon Cycle model (HAMOCC), a component of the Max Planck Institute Earth System Model (MPI-ESM). Growth dynamics of N2 fixers in the model are based on physiological characteristics of the cyanobacterium Trichodesmium. The applied temperature dependency confines diazotrophic growth and N2 fixation to the tropical and subtropical ocean roughly between 40°S and 40°N. Simulated large-scale spatial patterns compare well with observations, and the global N2 fixation rate of 135.6 Tg N yr-1 is within the range of current estimates. The vertical distribution of N2 fixation also matches well the observations, with a major fraction of about 85% occurring in the upper 20 m. The observed seasonal variability at the stations BATS and ALOHA is reasonably reproduced, with highest fixation rates in northern summer/fall. Iron limitation was found to be an important factor in controlling the simulated distribution of N2 fixation, especially in the Pacific Ocean. The new model component considerably improves the representation of present-day N2 fixation in HAMOCC. It provides the basis for further studies on the role of diazotrophs in global biogeochemical cycles, as well as on the response of N2 fixation to changing environmental conditions.

  17. A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival.

    Directory of Open Access Journals (Sweden)

    Branko Miladinovic

    Full Text Available Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R(2, scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R(2 =0.298; 95% CI: 0.236-0.358 than the Cox model (R(2 =0.156; 95% CI: 0.111-0.203. The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

  18. Elevated neutrophil and monocyte counts in peripheral blood are associated with poor survival in patients with metastatic melanoma: a prognostic model

    DEFF Research Database (Denmark)

    Schmidt, H; Bastholt, L; Geertsen, P

    2005-01-01

    We aimed to create a prognostic model in metastatic melanoma based on independent prognostic factors in 321 patients receiving interleukin-2 (IL-2)-based immunotherapy with a median follow-up time for patients currently alive of 52 months (range 15-189 months). The patients were treated as part...... factors in univariate analyses. Subsequently, a multivariate Cox's regression analysis identified elevated LDH (P

  19. System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing.

    Science.gov (United States)

    Malinowski, M L; Beling, P A; Haimes, Y Y; LaViers, A; Marvel, J A; Weiss, B A

    2015-01-01

    The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies - Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) - can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets - components, subsystems, or systems - that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output. HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders' understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired

  20. Material Modelling - Composite Approach

    DEFF Research Database (Denmark)

    Nielsen, Lauge Fuglsang

    1997-01-01

    , and internal stresses caused by drying shrinkage with experimental results reported in the literature on the mechanical behavior of mature concretes. It is then concluded that the model presented applied in general with respect to age at loading.From a stress analysis point of view the most important finding...... is successfully justified comparing predicted results with experimental data obtained in the HETEK-project on creep, relaxation, and shrinkage of very young concretes cured at a temperature of T = 20^o C and a relative humidity of RH = 100%. The model is also justified comparing predicted creep, shrinkage......, linear-viscoelastic analysis methods are justified from the age of approximately 10 hours.The rheological properties of plain cement paste are determined. These properties are the principal material properties needed in any stress analysis of concrete. Shrinkage (autogeneous or drying) of mortar...

  1. GPU Accelerated Prognostics

    Science.gov (United States)

    Gorospe, George E., Jr.; Daigle, Matthew J.; Sankararaman, Shankar; Kulkarni, Chetan S.; Ng, Eley

    2017-01-01

    Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

  2. Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

    Science.gov (United States)

    Mes, Steven W; Te Beest, Dennis; Poli, Tito; Rossi, Silvia; Scheckenbach, Kathrin; van Wieringen, Wessel N; Brink, Arjen; Bertani, Nicoletta; Lanfranco, Davide; Silini, Enrico M; van Diest, Paul J; Bloemena, Elisabeth; Leemans, C René; van de Wiel, Mark A; Brakenhoff, Ruud H

    2017-08-29

    Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.

  3. Prognostic scoring systems for mortality in intensive care units--the APACHE model.

    Science.gov (United States)

    Niewiński, Grzegorz; Starczewska, Małgorzata; Kański, Andrzej

    2014-01-01

    The APACHE (Acute Physiology and Chronic Health Evaluation) scoring system is time consuming. The mean time for introducing a patient's data to APACHE IV is 37.3 min. Nevertheless, statisticians have known for years that the higher the number of variables the mathematical model describes, the more accurate the model. Because of the necessity of gathering data over a 24-hour period and of determining one cause for ICU admission, the system is troublesome and prone to mistakes. The evolution of the APACHE scoring system is an example of unfulfilled hopes for accurately estimating the risk of death for patients admitted to the ICU; satisfactory prognostic effects resulting from the use of APACHE II and III have been recently studied in patients undergoing liver transplantations. Because no increase in the predictive properties of successive versions has been observed, the search for other solutions continues. The APACHE IV scoring system is helpful; however, its use without prepared spreadsheets is almost impractical. Therefore, although many years have passed since its original publication, APACHE II or its extension APACHE III is currently used in clinical practice.

  4. Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics

    Data.gov (United States)

    National Aeronautics and Space Administration — A general framework for probabilistic prognosis using maximum entropy approach, MRE, is proposed in this paper to include all available information and uncertainties...

  5. A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning

    Data.gov (United States)

    National Aeronautics and Space Administration — The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in...

  6. Clinical Risk Factors and Prognostic Model for Primary Graft Dysfunction after Lung Transplantation in Patients with Pulmonary Hypertension.

    Science.gov (United States)

    Porteous, Mary K; Lee, James C; Lederer, David J; Palmer, Scott M; Cantu, Edward; Shah, Rupal J; Bellamy, Scarlett L; Lama, Vibha N; Bhorade, Sangeeta M; Crespo, Maria M; McDyer, John F; Wille, Keith M; Localio, A Russell; Orens, Jonathan B; Shah, Pali D; Weinacker, Ann B; Arcasoy, Selim; Wilkes, David S; Ware, Lorraine B; Christie, Jason D; Kawut, Steven M; Diamond, Joshua M

    2017-10-01

    Pulmonary hypertension from pulmonary arterial hypertension or parenchymal lung disease is associated with an increased risk for primary graft dysfunction after lung transplantation. We evaluated the clinical determinants of severe primary graft dysfunction in pulmonary hypertension and developed and validated a prognostic model. We conducted a retrospective cohort study of patients in the multicenter Lung Transplant Outcomes Group with pulmonary hypertension at transplant listing. Severe primary graft dysfunction was defined as PaO2/FiO2 ≤200 with allograft infiltrates at 48 or 72 hours after transplantation. Donor, recipient, and operative characteristics were evaluated in a multivariable explanatory model. A prognostic model derived using donor and recipient characteristics was then validated in a separate cohort. In the explanatory model of 826 patients with pulmonary hypertension, donor tobacco smoke exposure, higher recipient body mass index, female sex, listing mean pulmonary artery pressure, right atrial pressure and creatinine at transplant, cardiopulmonary bypass use, transfusion volume, and reperfusion fraction of inspired oxygen were associated with primary graft dysfunction. Donor obesity was associated with a lower risk for primary graft dysfunction. Using a 20% threshold for elevated risk, the prognostic model had good negative predictive value in both derivation and validation cohorts (89.1% [95% confidence interval, 85.3-92.8] and 83.3% [95% confidence interval, 78.5-88.2], respectively), but low positive predictive value. Several recipient, donor, and operative characteristics were associated with severe primary graft dysfunction in patients with pulmonary hypertension, including several risk factors not identified in the overall transplant population. A prognostic model with donor and recipient clinical risk factors alone had low positive predictive value, but high negative predictive value, to rule out high risk for primary graft dysfunction.

  7. Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example.

    Directory of Open Access Journals (Sweden)

    Klaus-Jürgen Winzer

    Full Text Available Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches.Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived.The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.

  8. [Molecular biological evaluation of prognostic parameters in GIST. Development of an integrative model of tumor progression].

    Science.gov (United States)

    Haller, F

    2010-10-01

    Prognosis evaluation in gastrointestinal stromal tumors (GIST) is currently based on tumor diameter, mitotic counts and anatomic localisation. There are two risk classifications as well as the first ever TNM classification for GISTs, whereby the risk classification according to the Armed Forces Institute of Pathology (AFIP) has the best correlation with clinical follow-up according to own experiences. "Very low/low risk" GISTs are almost benign, while the majority of "high risk" GISTs metastasize and benefit from adjuvant therapy. Careful evaluation of mitotic counts in 50 high-power fields is of particular relevance for correct risk classification. Apart from these classical prognostic factors, many molecular genetic parameters with correlation to follow-up have been evaluated and may help to improve prognosis evaluation of GISTs in the future. Since most of the molecular genetic parameters are associated or even determined by the clinico-pathological parameters, an integrated model for tumor progression of GISTs is helpful to interpret the different factors in correlation to one another. In particular for "intermediate risk" GISTs, additional parameters are needed for improved prognosis evaluation.

  9. Validation of new prognostic and predictive scores by sequential testing approach

    Energy Technology Data Exchange (ETDEWEB)

    Nieder, Carsten [Radiation Oncology Unit, Nordland Hospital, Bodo (Norway); Inst. of Clinical Medicine, Univ. of Tromso (Norway); Haukland, Ellinor; Pawinski, Adam; Dalhaug, Astrid [Radiation Oncology Unit, Nordland Hospital, Bodo (Norway)

    2010-03-15

    Background and Purpose: For practitioners, the question arises how their own patient population differs from that used in large-scale analyses resulting in new scores and nomograms and whether such tools actually are valid at a local level and thus can be implemented. A recent article proposed an easy-to-use method for the in-clinic validation of new prediction tools with a limited number of patients, a so-called sequential testing approach. The present study evaluates this approach in scores related to radiation oncology. Material and Methods: Three different scores were used, each predicting short overall survival after palliative radiotherapy (bone metastases, brain metastases, metastatic spinal cord compression). For each scenario, a limited number of consecutive patients entered the sequential testing approach. The positive predictive value (PPV) was used for validation of the respective score and it was required that the PPV exceeded 80%. Results: For two scores, validity in the own local patient population could be confirmed after entering 13 and 17 patients, respectively. For the third score, no decision could be reached even after increasing the sample size to 30. Conclusion: In-clinic validation of new predictive tools with sequential testing approach should be preferred over uncritical adoption of tools which provide no significant benefit to local patient populations. Often the necessary number of patients can be reached within reasonable time frames even in small oncology practices. In addition, validation is performed continuously as the data are collected. (orig.)

  10. Significance analysis of prognostic signatures.

    Directory of Open Access Journals (Sweden)

    Andrew H Beck

    Full Text Available A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically

  11. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

    OpenAIRE

    Guinney, Justin; Tao WANG; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J. Christopher; Neto, Elias Chaibub; Khan, Suleiman A.; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M.; Shen, Liji; Abdallah, Kald

    2017-01-01

    Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for ...

  12. Prognostic value of a systemic inflammatory response index in metastatic renal cell carcinoma and construction of a predictive model

    Science.gov (United States)

    Li, Hongzhao; Chen, Luyao; Li, Xintao; Zhang, Yu; Xie, Yongpeng; Zhang, Xu

    2017-01-01

    Inflammation act as a crucial role in carcinogenesis and tumor progression. In this study, we aim to investigate the prognostic significance of systemic inflammatory biomarkers in metastatic renal cell carcinoma (mRCC) and develop a survival predictive model. One hundred and sixty-one mRCC patients who had undergone cytoreductive nephrectomy were enrolled from January 2006 to December 2013. We created a systemic inflammation response index (SIRI) basing on pretreatment hemoglobin and lymphocyte to monocyte ratio (LMR), and evaluated its associations with overall survival (OS) and clinicopathological features. Pretreatment hemoglobin and LMR both remained as independent factors adjusted for other markers of systemic inflammation responses and conventional clinicopathological parameters. A high SIRI seems to be an independent prognosis predictor of worse OS and was significantly correlated with aggressive tumor behaviors. Inclusion of the SIRI into a prognostic model including Fuhrman grade, histology, tumor necrosis and targeted therapy established a nomogram, which accurately predicted 1-year survival for mRCC patients. The SIRI seems to be a prognostic biomarker in mRCC patients. The proposed nomogram can be applied to predict OS of patients with mRCC after nephrectomy. PMID:28881716

  13. Generic Software Architecture for Prognostics (GSAP) User Guide

    Science.gov (United States)

    Teubert, Christopher Allen; Daigle, Matthew John; Watkins, Jason; Sankararaman, Shankar; Goebel, Kai

    2016-01-01

    The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

  14. Glycolytic activity in breast cancer using 18F-FDG PET/CT as prognostic predictor: A molecular phenotype approach.

    Science.gov (United States)

    Garcia Vicente, A M; Soriano Castrejón, A; Amo-Salas, M; Lopez Fidalgo, J F; Muñoz Sanchez, M M; Alvarez Cabellos, R; Espinosa Aunion, R; Muñoz Madero, V

    2016-01-01

    To explore the relationship between basal (18)F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an (18)F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan-Meier and univariate and multivariate tests. Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan-Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes. Copyright © 2015 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

  15. Baseline elevated leukocyte count in peripheral blood is associated with poor survival in patients with advanced non-small cell lung cancer: a prognostic model.

    Science.gov (United States)

    Tibaldi, C; Vasile, E; Bernardini, I; Orlandini, C; Andreuccetti, M; Falcone, A

    2008-10-01

    We aimed to investigate the prognostic significance of several baseline variables in stage IIIB-IV non-small cell lung cancer to create a model based on independent prognostic factors. A total of 320 patients were treated with last generation chemotherapy regimens. The majority of patients received treatment with cisplatin + gemcitabine or gemcitabine alone if older than 70 years or with an ECOG performance status (PS) = 2. Performance status of 2, squamous histology, number of metastatic sites >2, presence of bone, brain, liver and contralateral lung metastases and elevated leukocyte count in peripheral blood were all statistically significant prognostic factors in univariate analyses whereas the other tested variables (sex, stage, age, presence of adrenal gland and skin metastases) were not. Subsequently, a multivariate Cox's regression analysis identified PS 2 (P leukocyte count (P Leukocyte count resulted the stronger factor after performance status. If prospectly validated, the proposed prognostic model could be useful to stratify performance status 2 patients in specific future trials.

  16. Prognostics and health management of engineering systems an introduction

    CERN Document Server

    Kim, Nam-Ho; Choi, Joo-Ho

    2017-01-01

    This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application. Among the many topics discussed in-depth are: • Prognostics tutorials using least-squares • Bayesian inference and parameter estimation • Physics-based prognostics algorithms including non...

  17. Diagnostic and prognostic simulations with a full Stokes model accounting for superimposed ice of Midtre Lovénbreen, Svalbard

    Directory of Open Access Journals (Sweden)

    T. Zwinger

    2009-11-01

    Full Text Available We present steady state (diagnostic and transient (prognostic simulations of Midtre Lovénbreen, Svalbard performed with the thermo-mechanically coupled full-Stokes code Elmer. This glacier has an extensive data set of geophysical measurements available spanning several decades, that allow for constraints on model descriptions. Consistent with this data set, we included a simple model accounting for the formation of superimposed ice. Diagnostic results indicated that a dynamic adaptation of the free surface is necessary, to prevent non-physically high velocities in a region of under determined bedrock depths. Observations from ground penetrating radar of the basal thermal state agree very well with model predictions, while the dip angles of isochrones in radar data also match reasonably well with modelled isochrones, despite the numerical deficiencies of estimating ages with a steady state model.

    Prognostic runs for 53 years, using a constant accumulation/ablation pattern starting from the steady state solution obtained from the configuration of the 1977 DEM show that: 1 the unrealistic velocities in the under determined parts of the DEM quickly damp out; 2 the free surface evolution matches well measured elevation changes; 3 the retreat of the glacier under this scenario continues with the glacier tongue in a projection to 2030 being situated ≈500 m behind the position in 1977.

  18. Evaluation of Simulated Marine Aerosol Production Using the WaveWatchIII Prognostic Wave Model Coupled to the Community Atmosphere Model within the Community Earth System Model

    Energy Technology Data Exchange (ETDEWEB)

    Long, M. S. [Harvard Univ., Cambridge, MA (United States). School of Engineering and Applied Sciences; Keene, William C. [Univ. of Virginia, Charlottesville, VA (United States). Dept. of Environmental Sciences; Zhang, J. [Univ. of North Dakota, Grand Forks, ND (United States). Dept. of Atmospheric Sciences; Reichl, B. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Shi, Y. [Univ. of North Dakota, Grand Forks, ND (United States). Dept. of Atmospheric Sciences; Hara, T. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Reid, J. S. [Naval Research Lab. (NRL), Monterey, CA (United States); Fox-Kemper, B. [Brown Univ., Providence, RI (United States). Earth, Environmental and Planetary Sciences; Craig, A. P. [National Center for Atmospheric Research, Boulder, CO (United States); Erickson, D. J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division; Ginis, I. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Webb, A. [Univ. of Tokyo (Japan). Dept. of Ocean Technology, Policy, and Environment

    2016-11-08

    Primary marine aerosol (PMA) is emitted into the atmosphere via breaking wind waves on the ocean surface. Most parameterizations of PMA emissions use 10-meter wind speed as a proxy for wave action. This investigation coupled the 3rd generation prognostic WAVEWATCH-III wind-wave model within a coupled Earth system model (ESM) to drive PMA production using wave energy dissipation rate – analogous to whitecapping – in place of 10-meter wind speed. The wind speed parameterization did not capture basin-scale variability in relations between wind and wave fields. Overall, the wave parameterization did not improve comparison between simulated versus measured AOD or Na+, thus highlighting large remaining uncertainties in model physics. Results confirm the efficacy of prognostic wind-wave models for air-sea exchange studies coupled with laboratory- and field-based characterizations of the primary physical drivers of PMA production. No discernible correlations were evident between simulated PMA fields and observed chlorophyll or sea surface temperature.

  19. A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

    Directory of Open Access Journals (Sweden)

    Magdalena U Bogdańska

    Full Text Available Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas. In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.

  20. Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear Cox regression model and its artificial neural network extension.

    Science.gov (United States)

    Mariani, L; Coradini, D; Biganzoli, E; Boracchi, P; Marubini, E; Pilotti, S; Salvadori, B; Silvestrini, R; Veronesi, U; Zucali, R; Rilke, F

    1997-06-01

    The purpose of the present study was to assess prognostic factor for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.

  1. Development and internal validation of a prognostic model to predict recurrence free survival in patients with adult granulosa cell tumors of the ovary

    NARCIS (Netherlands)

    van Meurs, Hannah S.; Schuit, Ewoud; Horlings, Hugo M.; van der Velden, Jacobus; van Driel, Willemien J.; Mol, Ben Willem J.; Kenter, Gemma G.; Buist, Marrije R.

    2014-01-01

    Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model. We

  2. HEDR modeling approach: Revision 1

    Energy Technology Data Exchange (ETDEWEB)

    Shipler, D.B.; Napier, B.A.

    1994-05-01

    This report is a revision of the previous Hanford Environmental Dose Reconstruction (HEDR) Project modeling approach report. This revised report describes the methods used in performing scoping studies and estimating final radiation doses to real and representative individuals who lived in the vicinity of the Hanford Site. The scoping studies and dose estimates pertain to various environmental pathways during various periods of time. The original report discussed the concepts under consideration in 1991. The methods for estimating dose have been refined as understanding of existing data, the scope of pathways, and the magnitudes of dose estimates were evaluated through scoping studies.

  3. Modeling Approaches in Planetary Seismology

    Science.gov (United States)

    Weber, Renee; Knapmeyer, Martin; Panning, Mark; Schmerr, Nick

    2014-01-01

    Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. Given that the seismic data gathered on the Moon over 40 years ago revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure on and of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this chapter, we will review these approaches.

  4. A laboratory prognostic index model for patients with advanced non-small cell lung cancer.

    Directory of Open Access Journals (Sweden)

    Arife Ulas

    Full Text Available We aimed to establish a laboratory prognostic index (LPI in advanced non-small cell lung cancer (NSCLC patients based on hematologic and biochemical parameters and to analyze the predictive value of LPI on NSCLC survival.The study retrospectively reviewed 462 patients with advanced NSCLC diagnosed between 2000 and 2010 in a single institution. We developed an LPI that included serum levels of white blood cells (WBC, lactate dehydrogenase (LDH, albumin, calcium, and alkaline phosphatase (ALP, based on the results of a Cox regression analysis. The patients were classified into 3 LPI groups as follows: LPI 0: normal; LPI 1: one abnormal laboratory finding; and LPI 2: at least 2 abnormal laboratory findings.The median follow up period was 44 months; the median overall survival (OS and median progression-free survival (PFS were 11 and 6 months, respectively. A multivariate analysis revealed that the following could be used as independent prognostic factors: an Eastern Cooperative Oncology Group performance status score (ECOG PS ≥2, a high LDH level, serum albumin 10.5 g/dL, number of metastases>2, presence of liver metastases, malignant pleural effusion, or receiving chemotherapy ≥4 cycles. The 1-year OS rates according to LPI 0, LPI 1, and LPI 2 were 54%, 34%, and 17% (p<0.001, respectively and 6-month PFS rates were 44%, 27%, and 15% (p<0.001, respectively. The LPI was a significant predictor for OS (Hazard Ratio (HR: 1.41; 1.05-1.88, p<0.001 and PFS (HR: 1.48; 1.14-1.93, p<0.001.An LPI is an inexpensive, easily accessible and independent prognostic index for advanced NSCLC and may be helpful in making individualized treatment plans and predicting survival rates when combined with clinical parameters.

  5. Significance of stromal-1 and stromal-2 signatures and biologic prognostic model in diffuse large B-cell lymphoma

    Science.gov (United States)

    Abdou, Asmaa Gaber; Asaad, Nancy; Kandil, Mona; Shabaan, Mohammed; Shams, Asmaa

    2017-01-01

    Objective : Diffuse Large B Cell Lymphoma (DLBCL) is a heterogeneous group of tumors with different biological and clinical characteristics that have diverse clinical outcomes and response to therapy. Stromal-1 signature of tumor microenvironment of DLBCL represents extracellular matrix deposition and histiocytic infiltrate, whereas stromal-2 represents angiogenesis that could affect tumor progression. Methods : The aim of the present study is to assess the significance of stromal-1 signature using SPARC-1 and stromal-2 signature using CD31 expression and then finally to construct biologic prognostic model (BPM) in 60 cases of DLBCL via immunohistochemistry. Results : Microvessel density (PBPM showed that 42 cases (70%) were of low biologic score (0–1) and 18 cases (30%) were of high biologic score (2–3). Low BPM cases showed less probability for splenic involvement (P=0.04) and a higher rate of complete response to therapy compared with high score cases (P=0.08). Conclusions : The DLBCL microenvironment could modulate tumor progression behavior since angiogenesis and SPARC positive stromal cells promote dissemination by association with spleen involvement and capsular invasion. Biologic prognostic models, including modified BPM, which considered cell origin of DLBCL and stromal signature pathways, could determine DLBCL progression and response to therapy. PMID:28607806

  6. Composite prognostic models across the non-alcoholic fatty liver disease spectrum: Clinical application in developing countries

    Science.gov (United States)

    Lückhoff, Hilmar K; Kruger, Frederik C; Kotze, Maritha J

    2015-01-01

    Heterogeneity in clinical presentation, histological severity, prognosis and therapeutic outcomes characteristic of non-alcoholic fatty liver disease (NAFLD) necessitates the development of scientifically sound classification schemes to assist clinicians in stratifying patients into meaningful prognostic subgroups. The need for replacement of invasive liver biopsies as the standard method whereby NAFLD is diagnosed, graded and staged with biomarkers of histological severity injury led to the development of composite prognostic models as potentially viable surrogate alternatives. In the present article, we review existing scoring systems used to (1) confirm the presence of undiagnosed hepatosteatosis; (2) distinguish between simple steatosis and NASH; and (3) predict advanced hepatic fibrosis, with particular emphasis on the role of NAFLD as an independent cardio-metabolic risk factor. In addition, the incorporation of functional genomic markers and application of emerging imaging technologies are discussed as a means to improve the diagnostic accuracy and predictive performance of promising composite models found to be most appropriate for widespread clinical adoption. PMID:26019735

  7. Updated survivals and prognostic factor analysis in myeloma treated by a staged approach use of bortezomib/thalidomide/dexamethasone in transplant eligible patients

    Directory of Open Access Journals (Sweden)

    Chim Chor

    2010-11-01

    Full Text Available Abstract Background Bortezomib, an NFkB inhibitor, is an active agent for the treatment of myeloma (MM. We have reported a promising complete remission (CR rate for newly diagnosed myeloma patients treated by a staged approach, in which chemosensitive patients underwent autologous haematopoietic stem cell transplantation (auto-HSCT while less chemosensitive patients received salvage therapy with bortezomib/thalidomide/dexamethasone prior to auto-HSCT. Methods Herein, with an additional 13 months of follow-up, we reported the updated survivals, and examined potential prognostic factors impacting event-free (EFS and overall survival (OS. Results With a median follow-up of 30 months, the projected OS was 73% and EFS was 50.2%. Age, gender, clinical stage and DAPK methylation could not account for the differential chemosensitivity. Advanced ISS stage and DAPK methylation adversely impacted OS whereas oligoclonal reconstitution predicted superior EFS. Conclusions Our staged approach illustrated an economical use of expensive targeted agents while preserving a good CR rate and OS. The comparable survivals of chemosensitive and less chemosensitive patients suggested the staged approach might have abolished the adverse prognostic impact of suboptimal chemosensitivity. Finally, the adverse impact of DAPK methylation and favorable impact of oligoclonal reconstitution in myeloma warrants further study.

  8. Branding approach and valuation models

    Directory of Open Access Journals (Sweden)

    Mamula Tatjana

    2006-01-01

    Full Text Available Much of the skill of marketing and branding nowadays is concerned with building equity for products whose characteristics, pricing, distribution and availability are really quite close to each other. Brands allow the consumer to shop with confidence. The real power of successful brands is that they meet the expectations of those that buy them or, to put it another way, they represent a promise kept. As such they are a contract between a seller and a buyer: if the seller keeps to its side of the bargain, the buyer will be satisfied; if not, the buyer will in future look elsewhere. Understanding consumer perceptions and associations is an important first step to understanding brand preferences and choices. In this paper, we discuss different models to measure value of brand according to couple of well known approaches according to request by companies. We rely upon several empirical examples.

  9. A Generic Software Architecture For Prognostics

    Science.gov (United States)

    Teubert, Christopher; Daigle, Matthew J.; Sankararaman, Shankar; Goebel, Kai; Watkins, Jason

    2017-01-01

    Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. As a result, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

  10. Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery.

    Science.gov (United States)

    Henriksson, Martin; Palmer, Stephen; Chen, Ruoling; Damant, Jacqueline; Fitzpatrick, Natalie K; Abrams, Keith; Hingorani, Aroon D; Stenestrand, Ulf; Janzon, Magnus; Feder, Gene; Keogh, Bruce; Shipley, Martin J; Kaski, Juan-Carlos; Timmis, Adam; Sculpher, Mark; Hemingway, Harry

    2010-01-19

    To determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery. Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared. Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers. The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10,000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of pound20,000- pound30,000 (euro22,000-euro33,000; $32,000-$48,000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was < pound410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100,000 patients at an additional cost of pound 245,000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate. Evaluating the cost effectiveness of prognostic biomarkers is

  11. Noncutaneous malignant melanoma: a prognostic model from a retrospective multicenter study

    Directory of Open Access Journals (Sweden)

    Kim Jung Han

    2010-04-01

    Full Text Available Abstract Background We performed multicenter study to define clinical characteristics of noncutaneous melanomas and to establish prognostic factors patients who received curative resection. Methods Of the 141 patients who were diagnosed of non-cutaneous melanoma at 4 institutions in Korea between June 1992 and May 2005, 129 (91.5% satisfied the selection criteria. Results Of the 129 noncutaneous melanoma patients, 14 patients had ocular melanoma and 115 patients had mucosal melanoma. For mucosal melanoma, anorectum was the most common anatomic site (n = 39, 30.2% which was followed by nasal cavity (n = 30, 23.3%, genitourinary (n = 21, 16.3%, oral cavity (n = 14, 10.9%, upper gastrointestinal tract (n = 6, 4.7% and maxillary sinus (n = 5, 3.9% in the order of frequency. With the median 64.5 (range 4.3-213.0 months follow-up, the median overall survival were 24.4 months (95% CI 13.2-35.5 for all patients, and 34.6 (95% CI 24.5-44.7 months for curatively resected mucosal melanoma patients. Adverse prognostic factors of survival for 87 curatively resected mucosal melanoma patients were complete resection (R1 resection margin, and age > 50 years. For 14 ocular melanoma, Survival outcome was much better than mucosal melanoma with 73.3% of 2 year OS and 51.2 months of median OS (P = .04. Conclusion Prognosis differed according to primary sites of noncutaneous melanoma. Based on our study, noncutaneous melanoma patients should be treated differently to improve survival outcome.

  12. Implicit coupling of turbulent diffusion with chemical reaction mechanisms for prognostic atmospheric dispersion models

    Energy Technology Data Exchange (ETDEWEB)

    Berlowitz, D.R.

    1996-11-01

    In the last few decades the negative impact by humans on the thin atmospheric layer enveloping the earth, the basis for life on this planet, has increased steadily. In order to halt, or at least slow down this development, the knowledge and study of these anthropogenic influence has to be increased and possible remedies have to be suggested. An important tool for these studies are computer models. With their help the atmospheric system can be approximated and the various processes, which have led to the current situation can be quantified. They also serve as an instrument to assess short or medium term strategies to reduce this human impact. However, to assure efficiency as well as accuracy, a careful analysis of the numerous processes involved in the dispersion of pollutants in the atmosphere is called for. This should help to concentrate on the essentials and also prevent excessive usage of sometimes scarce computing resources. The basis of the presented work is the EUMAC Zooming Model (ETM), and particularly the component calculating the dispersion of pollutants in the atmosphere, the model MARS. The model has two main parts: an explicit solver, where the advection and the horizontal diffusion of pollutants are calculated, and an implicit solution mechanism, allowing the joint computation of the change of concentration due to chemical reactions, coupled with the respective influence of the vertical diffusion of the species. The aim of this thesis is to determine particularly the influence of the horizontal components of the turbulent diffusion on the existing implicit solver of the model. Suggestions for a more comprehensive inclusion of the full three dimensional diffusion operator in the implicit solver are made. This is achieved by an appropriate operator splitting. A selection of numerical approaches to tighten the coupling of the diffusion processes with the calculation of the applied chemical reaction mechanisms are examined. (author) figs., tabs., refs.

  13. Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

    Science.gov (United States)

    Matthaios, Vasileios N.; Triantafyllou, Athanasios G.; Albanis, Triantafyllos A.; Sakkas, Vasileios; Garas, Stelios

    2017-04-01

    Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009-2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO2 concentrations were assessed by the model with IOA varied from 0

  14. The added value that increasing levels of diagnostic information provide in prognostic models to estimate hospital mortality for adult intensive care patients

    NARCIS (Netherlands)

    de Keizer, N. F.; Bonsel, G. J.; Goldfad, C.; Rowan, K. M.

    2000-01-01

    To investigate in a systematic, reproducible way the potential of adding increasing levels of diagnostic information to prognostic models for estimating hospital mortality. Prospective cohort study. Thirty UK intensive care units (ICUs) participating in the ICNARC Case Mix Programme. Eight thousand

  15. Urbanization Effects on air Quality and Climate in the Acapulco Area Using a Prognostic Meteorological and air Quality Model

    Science.gov (United States)

    Ortinez, A. A.; Garcia, A. R.; Jazcilevich, A. D.; Caetano, E.; Moya, M. N.; Delgado, J. C.

    2007-05-01

    The effects of urbanization growth on the Acapulco coastal metropolitan area were estimated by using a prognostic meteorological and air quality model. To this end three urbanization scenarios are proposed: The current Acapulco urban area that we call "Control Scenario" and two possible urban growths that we call "Scenario 1" and "Scenario 2". We estimated the urban growth of scenarios 1 and 2, using economic factors, population distribution and historical data. The urban distribution in the "Control Scenario" was taken from the aerial photographs of Acapulco and processed by a Geographic Information System (GIS).The variables devised for the scenarios comparison was a Comfort Index based on humidity and temperature and the Potential Exposure Index for Ozone. The model used was the Penn State/NCAR MM5 mesoscale meteorological model and the Multiscale Climate and Chemistry Model (MCCM). Since there is no local information, the emissions were estimated by using data of similar socio- economic urban areas where emission data is available. The meteorology and air quality models were calibrated using data of a measuring campaign performed in December 2005. This is a preliminary effort to propose a planned urban expansion for Acapulco from the point of view of air quality and urban climate.

  16. Mucins as diagnostic and prognostic biomarkers in a fish-parasite model: transcriptional and functional analysis.

    Directory of Open Access Journals (Sweden)

    Jaume Pérez-Sánchez

    Full Text Available Mucins are O-glycosylated glycoproteins present on the apex of all wet-surfaced epithelia with a well-defined expression pattern, which is disrupted in response to a wide range of injuries or challenges. The aim of this study was to identify mucin gene sequences of gilthead sea bream (GSB, to determine its pattern of distribution in fish tissues and to analyse their transcriptional regulation by dietary and pathogenic factors. Exhaustive search of fish mucins was done in GSB after de novo assembly of next-generation sequencing data hosted in the IATS transcriptome database (www.nutrigroup-iats.org/seabreamdb. Six sequences, three categorized as putative membrane-bound mucins and three putative secreted-gel forming mucins, were identified. The transcriptional tissue screening revealed that Muc18 was the predominant mucin in skin, gills and stomach of GSB. In contrast, Muc19 was mostly found in the oesophagus and Muc13 was along the entire intestinal tract, although the posterior intestine exhibited a differential pattern with a high expression of an isoform that does not share a clear orthologous in mammals. This mucin was annotated as intestinal mucin (I-Muc. Its RNA expression was highly regulated by the nutritional background, whereas the other mucins, including Muc2 and Muc2-like, were expressed more constitutively and did not respond to high replacement of fish oil (FO by vegetable oils (VO in plant protein-based diets. After challenge with the intestinal parasite Enteromyxum leei, the expression of a number of mucins was decreased mainly in the posterior intestine of infected fish. But, interestingly, the highest down-regulation was observed for the I-Muc. Overall, the magnitude of the changes reflected the intensity and progression of the infection, making mucins and I-Muc, in particular, reliable markers of prognostic and diagnostic value of fish intestinal health.

  17. Learning Action Models: Qualitative Approach

    NARCIS (Netherlands)

    Bolander, T.; Gierasimczuk, N.; van der Hoek, W.; Holliday, W.H.; Wang, W.-F.

    2015-01-01

    In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite

  18. Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

    Science.gov (United States)

    Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

    2016-10-01

    The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

  19. Prognostic value of the seventh AJCC/UICC TNM classification of intestinal-type ethmoid adenocarcinoma: Systematic review and risk prediction model.

    Science.gov (United States)

    Fiaux-Camous, Domitille; Chevret, Sylvie; Oker, Natalie; Turri-Zanoni, Mario; Lombardi, Davide; Choussy, Olivier; Duprez, Frederic; Jorissen, Marc; de Gabory, Ludovic; Malard, Olivier; Herman, Philippe; Nicolai, Piero; Castelnuovo, Paolo; Verillaud, Benjamin

    2017-04-01

    The purpose of this study was to propose a prognostic classification of intestinal-type adenocarcinoma (ITAC) based on literature search and prognostic modeling of cohort data. We first conducted a literature search to assess the homogeneity of the reported estimates of 5-year survival and to identify the influence of T classification. We then pooled prospective data from 3 large French and Italian series to predict time to all-cause mortality. The sample was randomly split to derive and then to validate the proposed prognostic model. Literature analysis confirmed the heterogeneity in 5-year survival rates, partly explained in subsets of homogeneous T-values. The sample included 223 patients, randomly separated into a derivation (n = 141) and a validation set (n = 82). Invasion of the sphenoid lateral and/or posterior walls and dura/cerebral invasion were systematically associated with a poor survival. The incorporation of the invasion of the sphenoid lateral or posterior walls should be considered for ITAC management and prognostication. © 2017 Wiley Periodicals, Inc. Head Neck 39: 668-678, 2017. © 2017 Wiley Periodicals, Inc.

  20. Refining Prognosis in Lung Cancer: A Report on the Quality and Relevance of Clinical Prognostic Tools.

    Science.gov (United States)

    Mahar, Alyson L; Compton, Carolyn; McShane, Lisa M; Halabi, Susan; Asamura, Hisao; Rami-Porta, Ramon; Groome, Patti A

    2015-11-01

    Accurate, individualized prognostication for lung cancer patients requires the integration of standard patient and pathologic factors, biological, genetic, and other molecular characteristics of the tumor. Clinical prognostic tools aim to aggregate information on an individual patient to predict disease outcomes such as overall survival, but little is known about their clinical utility and accuracy in lung cancer. A systematic search of the scientific literature for clinical prognostic tools in lung cancer published from January 1, 1996 to January 27, 2015 was performed. In addition, web-based resources were searched. A priori criteria determined by the Molecular Modellers Working Group of the American Joint Committee on Cancer were used to investigate the quality and usefulness of tools. Criteria included clinical presentation, model development approaches, validation strategies, and performance metrics. Thirty-two prognostic tools were identified. Patients with metastases were the most frequently considered population in non-small-cell lung cancer. All tools for small-cell lung cancer covered that entire patient population. Included prognostic factors varied considerably across tools. Internal validity was not formally evaluated for most tools and only 11 were evaluated for external validity. Two key considerations were highlighted for tool development: identification of an explicit purpose related to a relevant clinical population and clear decision points and prioritized inclusion of established prognostic factors over emerging factors. Prognostic tools will contribute more meaningfully to the practice of personalized medicine if better study design and analysis approaches are used in their development and validation.

  1. Establishment and evaluation of a prognostic model for surgical outcomes of patients with atlanto-axial dislocations.

    Science.gov (United States)

    Guo, Shuai; Chen, Jie; Yang, Baohui; Li, Haopeng

    2016-12-01

    Objective Atlanto-axial dislocations (AADs) are potentially fatal disturbances with high spinal cord compression syndrome. As surgeons are still uncertain who is likely to benefit the most from surgery, a prediction tool is needed to provide decision-making support. Methods The model was established based on 108 patients with AADs using multiple binary logistic regression analysis and evaluated by calibration plot and the area under the receiver operating curve (AUC). Bootstrapping was used for internal validation. Results The prognostic model can be expressed as: logit(P) = -2.2428 + 0.3168SCOPE - 2.0375SIGNAL, in which two covariates were accepted (SCORE represents the preoperative modified Japanese Orthopedic Association (mJOA) score and SIGNAL represents the intramedullary hyperintense T2-weighted imaging (T2WI) with AUC = 0.8081). Conclusions The model was internally valid, and the preoperative mJOA score and hyperintense T2WI were important predictors of outcomes. The threshold was defined as logit(P) = -0.7282 according to the receiver operating curve (ROC).

  2. Geometrical approach to fluid models

    NARCIS (Netherlands)

    Kuvshinov, B. N.; Schep, T. J.

    1997-01-01

    Differential geometry based upon the Cartan calculus of differential forms is applied to investigate invariant properties of equations that describe the motion of continuous media. The main feature of this approach is that physical quantities are treated as geometrical objects. The geometrical

  3. Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach.

    Science.gov (United States)

    Peng, Changhui; Guiot, Joel; Wu, Haibin; Jiang, Hong; Luo, Yiqi

    2011-05-01

    It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e., palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services. © 2011 Blackwell Publishing Ltd/CNRS.

  4. Global energy modeling - A biophysical approach

    Energy Technology Data Exchange (ETDEWEB)

    Dale, Michael

    2010-09-15

    This paper contrasts the standard economic approach to energy modelling with energy models using a biophysical approach. Neither of these approaches includes changing energy-returns-on-investment (EROI) due to declining resource quality or the capital intensive nature of renewable energy sources. Both of these factors will become increasingly important in the future. An extension to the biophysical approach is outlined which encompasses a dynamic EROI function that explicitly incorporates technological learning. The model is used to explore several scenarios of long-term future energy supply especially concerning the global transition to renewable energy sources in the quest for a sustainable energy system.

  5. A POMDP approach to Affective Dialogue Modeling

    NARCIS (Netherlands)

    Bui Huu Trung, B.H.T.; Poel, Mannes; Nijholt, Antinus; Zwiers, Jakob; Keller, E.; Marinaro, M.; Bratanic, M.

    2007-01-01

    We propose a novel approach to developing a dialogue model that is able to take into account some aspects of the user's affective state and to act appropriately. Our dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user's

  6. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2015-01-01

    identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power......—they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...

  7. A prognostic model of triple-negative breast cancer based on miR-27b-3p and node status.

    Directory of Open Access Journals (Sweden)

    Songjie Shen

    Full Text Available Triple-negative breast cancer (TNBC is an aggressive but heterogeneous subtype of breast cancer. This study aimed to identify and validate a prognostic signature for TNBC patients to improve prognostic capability and to guide individualized treatment.We retrospectively analyzed the prognostic performance of clinicopathological characteristics and miRNAs in a training set of 58 patients with invasive ductal TNBC diagnosed between 2002 and 2012. A prediction model was developed based on independent clinicopathological and miRNA covariates. The prognostic value of the model was further validated in a separate set of 41 TNBC patients diagnosed between 2007 and 2008.Only lymph node status was marginally significantly associated with poor prognosis of TNBC (P = 0.054, whereas other clinicopathological factors, including age, tumor size, histological grade, lymphovascular invasion, P53 status, Ki-67 index, and type of surgery, were not. The expression levels of miR-27b-3p, miR-107, and miR-103a-3p were significantly elevated in the metastatic group compared with the disease-free group (P value: 0.008, 0.005, and 0.050, respectively. The Cox proportional hazards regression analysis revealed that lymph node status and miR-27b-3p were independent predictors of poor prognosis (P value: 0.012 and 0.027, respectively. A logistic regression model was developed based on these two independent covariates, and the prognostic value of the model was subsequently confirmed in a separate validation set. The two different risk groups, which were stratified according to the model, showed significant differences in the rates of distant metastasis and breast cancer-related death not only in the training set (P value: 0.001 and 0.040, respectively but also in the validation set (P value: 0.013 and 0.012, respectively.This model based on miRNA and node status covariates may be used to stratify TNBC patients into different prognostic subgroups for potentially

  8. A Unified Approach to Modeling and Programming

    DEFF Research Database (Denmark)

    Madsen, Ole Lehrmann; Møller-Pedersen, Birger

    2010-01-01

    of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...

  9. There’s Risk, and Then There’s RISK: The Latest Clinical Prognostic Risk Stratification Models in Myelodysplastic Syndromes

    OpenAIRE

    Zeidan, Amer M.; Komrokji, Rami S.

    2013-01-01

    Myelodysplastic syndromes (MDS) include a diverse group of clonal hematopoietic disorders characterized by progressive cytopenias and propensity for leukemic progression. The biologic heterogeneity that underlies MDS translates clinically in wide variations of clinical outcomes. Several prognostic schemes were developed to predict the natural course of MDS, counsel patients, and allow evidence-based, risk-adaptive implementation of therapeutic strategies. The prognostic schemes divide patient...

  10. Bayesian statistical approaches to evaluating cognitive models.

    Science.gov (United States)

    Annis, Jeffrey; Palmeri, Thomas J

    2017-11-28

    Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable. We explore three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection. We compare and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Traditional approaches rely on an array of seemingly ad hoc techniques, whereas Bayesian statistical approaches rely on a single, principled, internally consistent system. We illustrate the Bayesian statistical approach to evaluating cognitive models using a running example of the Linear Ballistic Accumulator model of decision making (Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 2008, 57:153-178). This article is categorized under: Neuroscience > Computation Psychology > Reasoning and Decision Making Psychology > Theory and Methods. © 2017 Wiley Periodicals, Inc.

  11. Two novel mixed effects models for prognostics of rolling element bearings

    Science.gov (United States)

    Wang, Dong; Tsui, Kwok-Leung

    2018-01-01

    Rolling element bearings are widely used in various machines to support rotating shafts. Due to harsh working environments, the health condition of a bearing degrades over time. A typical bearing degradation process includes two phases. In Phase I, the health condition of the bearing is in normal and it exhibits a stable trend. In Phase II, the health condition of the bearing degrades exponentially. To analytically model the bearing degradation process, two novel mixed effects models are proposed in this paper. Each of the two mixed effects models is able to simultaneously model Phases I and II of the bearing degradation process. The main difference between the two mixed effects models is that different error assumptions including multiplicative normal random error and multiplicative Brownian motion error are respectively considered in the two mixed effects models. Consequently, two different closed-form distributions of bearing remaining useful life are derived from the two mixed effects models via Bayes' theorem once real-time bearing condition monitoring data are available. 25 sets of bearing degradation data collected from an experimental machine are used to illustrate how the two mixed effects models work. Comparisons are conducted to show that the mixed effects model with multiplicative Brownian motion error results in lower prediction errors than the mixed effects model with multiplicative normal random error for bearing remaining useful life prediction.

  12. External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

    Science.gov (United States)

    Guardiola, Jordi; Baliellas, Carme; Xiol, Xavier; Fernandez Esparrach, Glòria; Ginès, Pere; Ventura, Pere; Vazquez, Santiago

    2002-09-01

    Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

  13. Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models.

    Science.gov (United States)

    Thangaratinam, Shakila; Allotey, John; Marlin, Nadine; Dodds, Julie; Cheong-See, Fiona; von Dadelszen, Peter; Ganzevoort, Wessel; Akkermans, Joost; Kerry, Sally; Mol, Ben W; Moons, Karl G M; Riley, Richard D; Khan, Khalid S

    2017-03-30

    Unexpected clinical deterioration before 34 weeks gestation is an undesired course in early-onset pre-eclampsia. To safely prolong preterm gestation, accurate and timely prediction of complications is required. Women with confirmed early onset pre-eclampsia were recruited from 53 maternity units in the UK to a large prospective cohort study (PREP-946) for development of prognostic models for the overall risk of experiencing a complication using logistic regression (PREP-L), and for predicting the time to adverse maternal outcome using a survival model (PREP-S). External validation of the models were carried out in a multinational cohort (PIERS-634) and another cohort from the Netherlands (PETRA-216). Main outcome measures were C-statistics to summarise discrimination of the models and calibration plots and calibration slopes. A total of 169 mothers (18%) in the PREP dataset had adverse outcomes by 48 hours, and 633 (67%) by discharge. The C-statistics of the models for predicting complications by 48 hours and by discharge were 0.84 (95% CI, 0.81-0.87; PREP-S) and 0.82 (0.80-0.84; PREP-L), respectively. The PREP-S model included maternal age, gestation, medical history, systolic blood pressure, deep tendon reflexes, urine protein creatinine ratio, platelets, serum alanine amino transaminase, urea, creatinine, oxygen saturation and treatment with antihypertensives or magnesium sulfate. The PREP-L model included the above except deep tendon reflexes, serum alanine amino transaminase and creatinine. On validation in the external PIERS dataset, the reduced PREP-S model showed reasonable calibration (slope 0.80) and discrimination (C-statistic 0.75) for predicting adverse outcome by 48 hours. Reduced PREP-L model showed excellent calibration (slope: 0.93 PIERS, 0.90 PETRA) and discrimination (0.81 PIERS, 0.75 PETRA) for predicting risk by discharge in the two external datasets. PREP models can be used to obtain predictions of adverse maternal outcome risk, including

  14. Original approaches to test anti-breast cancer drugs in a novel set of mouse models

    NARCIS (Netherlands)

    Moiseeva, Ekaterina V.

    2005-01-01

    The limit therapeutic effect of traditional therapeutic approaches against breast cancer (BC) compels to search for new effective anti-BC therapies and to develop appropriate prognostic systems to predict BC patient outcome before therapy application. Therefore, first, we studied the prognostic

  15. Szekeres models: a covariant approach

    Science.gov (United States)

    Apostolopoulos, Pantelis S.

    2017-05-01

    We exploit the 1  +  1  +  2 formalism to covariantly describe the inhomogeneous and anisotropic Szekeres models. It is shown that an average scale length can be defined covariantly which satisfies a 2d equation of motion driven from the effective gravitational mass (EGM) contained in the dust cloud. The contributions to the EGM are encoded to the energy density of the dust fluid and the free gravitational field E ab . We show that the quasi-symmetric property of the Szekeres models is justified through the existence of 3 independent intrinsic Killing vector fields (IKVFs). In addition the notions of the apparent and absolute apparent horizons are briefly discussed and we give an alternative gauge-invariant form to define them in terms of the kinematical variables of the spacelike congruences. We argue that the proposed program can be used in order to express Sachs’ optical equations in a covariant form and analyze the confrontation of a spatially inhomogeneous irrotational overdense fluid model with the observational data.

  16. Structural Health and Prognostics Management for Offshore Wind Turbines: Sensitivity Analysis of Rotor Fault and Blade Damage with O&M Cost Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Myrent, Noah J. [Vanderbilt Univ., Nashville, TN (United States). Lab. for Systems Integrity and Reliability; Barrett, Natalie C. [Vanderbilt Univ., Nashville, TN (United States). Lab. for Systems Integrity and Reliability; Adams, Douglas E. [Vanderbilt Univ., Nashville, TN (United States). Lab. for Systems Integrity and Reliability; Griffith, Daniel Todd [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Wind Energy Technology Dept.

    2014-07-01

    Operations and maintenance costs for offshore wind plants are significantly higher than the current costs for land-based (onshore) wind plants. One way to reduce these costs would be to implement a structural health and prognostic management (SHPM) system as part of a condition based maintenance paradigm with smart load management and utilize a state-based cost model to assess the economics associated with use of the SHPM system. To facilitate the development of such a system a multi-scale modeling and simulation approach developed in prior work is used to identify how the underlying physics of the system are affected by the presence of damage and faults, and how these changes manifest themselves in the operational response of a full turbine. This methodology was used to investigate two case studies: (1) the effects of rotor imbalance due to pitch error (aerodynamic imbalance) and mass imbalance and (2) disbond of the shear web; both on a 5-MW offshore wind turbine in the present report. Sensitivity analyses were carried out for the detection strategies of rotor imbalance and shear web disbond developed in prior work by evaluating the robustness of key measurement parameters in the presence of varying wind speeds, horizontal shear, and turbulence. Detection strategies were refined for these fault mechanisms and probabilities of detection were calculated. For all three fault mechanisms, the probability of detection was 96% or higher for the optimized wind speed ranges of the laminar, 30% horizontal shear, and 60% horizontal shear wind profiles. The revised cost model provided insight into the estimated savings in operations and maintenance costs as they relate to the characteristics of the SHPM system. The integration of the health monitoring information and O&M cost versus damage/fault severity information provides the initial steps to identify processes to reduce operations and maintenance costs for an offshore wind farm while increasing turbine availability

  17. Comparison of the predictive qualities of three prognostic models of colorectal cancer

    Science.gov (United States)

    Anderson, Billie; Hardin, J. Michael; Alexander, Dominik D.; Meleth, Sreelatha; Grizzle, William E.; Manne, Upender

    2013-01-01

    Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival. ‘Data-mining’ techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal cancer (CRC) was evaluated. Predictive models (logistic regressions, ANNs, and decision trees) were compared, and the effect of techniques for variable selection on the predictive quality of these models was investigated. The Kolmogorov-Smirnoff (KS) statistic was used to compare the models. Overall, the logistic regression and ANN methods outperformed use of a decision tree. In some instances (e.g., for a model that included ‘all variables without tumor stage’ and use of a decision tree for variable selection), the ANN marginally outperformed logistic regression, although the difference between the accuracy of the KS statistic was minimal (0.80 versus 0.82). Regardless of the variable(s) and the methods for variable selection, all three predictive models identified survivors and non-survivors with the same level of statistical accuracy. PMID:20515758

  18. Modeling software behavior a craftsman's approach

    CERN Document Server

    Jorgensen, Paul C

    2009-01-01

    A common problem with most texts on requirements specifications is that they emphasize structural models to the near exclusion of behavioral models-focusing on what the software is, rather than what it does. If they do cover behavioral models, the coverage is brief and usually focused on a single model. Modeling Software Behavior: A Craftsman's Approach provides detailed treatment of various models of software behavior that support early analysis, comprehension, and model-based testing. Based on the popular and continually evolving course on requirements specification models taught by the auth

  19. Multiple Model Approaches to Modelling and Control,

    DEFF Research Database (Denmark)

    on the ease with which prior knowledge can be incorporated. It is interesting to note that researchers in Control Theory, Neural Networks,Statistics, Artificial Intelligence and Fuzzy Logic have more or less independently developed very similar modelling methods, calling them Local ModelNetworks, Operating......, and allows direct incorporation of high-level and qualitative plant knowledge into themodel. These advantages have proven to be very appealing for industrial applications, and the practical, intuitively appealing nature of the framework isdemonstrated in chapters describing applications of local methods...... to problems in the process industries, biomedical applications and autonomoussystems. The successful application of the ideas to demanding problems is already encouraging, but creative development of the basic framework isneeded to better allow the integration of human knowledge with automated learning...

  20. Prognosticating fault development rate in wind turbine generator bearings using local trend models

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Palou, Jonel; Sweeney, Christian Walsted

    2016-01-01

    Generator bearing defects, e.g. ball, inner and outer race defects, are ranked among the most frequent mechanical failures encountered in wind turbines. Diagnosis and prognosis of bearing faults can be successfully implemented using vibration based condition monitoring systems, where tracking...... of vibration trends from multi-megawatt wind turbine generators are presented, showing the effectiveness of the suggested approach on the calculation of the RUL and fault progression rate....

  1. An Approach to Modeling a Burning Cigarette

    Directory of Open Access Journals (Sweden)

    Muramatsu M

    2014-12-01

    Full Text Available The temperature and smoke components distributions inside a burning cigarette have been briefly reviewed. Then, focusing on our mathematical model to explain the natural smoldering mechanism of a cigarette and new mathematical models recently published by other authors, an approach to modeling a burning cigarette has been outlined.

  2. Model-based prognostics for batteries which estimates useful life and uses a probability density function

    Data.gov (United States)

    National Aeronautics and Space Administration — This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form...

  3. A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables

    Science.gov (United States)

    Crispin-Ortuzar, Mireia; Jeong, Jeho; Fontanella, Andrew N.; Deasy, Joseph O.

    2017-04-01

    Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model’s own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control (\\text{TC}{{\\text{D}}50} ) calculated by the model. We find that the three simulated variables correlate with the calculated \\text{TC}{{\\text{D}}50} . In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.

  4. EVALUATION OF PROGNOSTIC FACTORS IN QUALITY OF LIFE OF PATIENTS WITH ADOLESCENT IDIOPATHIC SCOLIOSIS UNDERGOING SPINAL FUSION BY THE POSTERIOR APPROACH

    Directory of Open Access Journals (Sweden)

    FELIPE DE MORAES POMAR

    Full Text Available ABSTRACT Objective: To evaluate the prognostic factors in the treatment of patients diagnosed with adolescent idiopathic scoliosis undergoing spinal fusion by the posterior approach. Methods: The study included 48 patients with idiopathic adolescent scoliosis (43 females and 5 males who underwent spinal fusion by the posterior approach, with an average age at diagnosis of 12 years, and clinical signs of Risser between 3 and 4 at the time of surgery. Clinical and radiographic measurements were performed, the participants answered the SRS-30 questionnaire, and the analysis of the medical record data was performed in two occasions during the preoperative period and at the end of two years of follow-up. Results: All satisfaction measures showed statistically significant change after the procedure (p<0.05 with respect to the radiographic characteristics, except for the lumbar apical vertebral translation (p=0.540 and Cobb L1-L5 (p=0.225. Conclusion: In general, it was found that patients who received surgical treatment were more satisfied with their appearance than those who underwent conservative treatment.

  5. Distributed simulation a model driven engineering approach

    CERN Document Server

    Topçu, Okan; Oğuztüzün, Halit; Yilmaz, Levent

    2016-01-01

    Backed by substantive case studies, the novel approach to software engineering for distributed simulation outlined in this text demonstrates the potent synergies between model-driven techniques, simulation, intelligent agents, and computer systems development.

  6. A Discrete Modeling Approach for Buck Converter

    Science.gov (United States)

    Zhaoxia, Leng; Qingfeng, Liu; Jinkun, Sun; Huamin, Wang

    In this paper, a discrete modeling approach for Buck converters based on continuous condition mode (CCM) and discontinuous condition mode (DCM) was presented. The unified coefficient matrixes of discrete model were described by building a mathematical function and the calculation methods of the parameters in coefficient matrixes were given. The working states of Buck converter on various work conditions were described adopting one discrete equation. The validity of the proposed modeling approach was proved by contrasting the output of discrete model with the operation result of Buck converter system in Simulink.

  7. Assessing the utility of a prognostication model to predict 1-year mortality in patients receiving radiation therapy for spinal metastases.

    Science.gov (United States)

    Shi, Diana D; Chen, Yu-Hui; Lam, Tai Chung; Leonard, Dana; Balboni, Tracy Anne; Schoenfeld, Andrew; Skamene, Sonia; Cagney, Daniel N; Chi, John H; Cho, Charles H; Harris, Mitchel; Ferrone, Marco L; Hertan, Lauren M

    2017-10-12

    Predicting survival outcomes after radiation therapy alone for metastatic disease of the spine is a challenging task that is important to guiding treatment decisions (e.g., determining dose fractionation and intensity). The New England Spinal Metastasis Score (NESMS) was recently introduced and validated in independent cohorts as a tool to predict 1-year survival following surgery for spinal metastases. This metric is composed of 3 factors: pre-operative albumin, ambulatory status, and modified Bauer score, with the total score ranging from 0 to 3. The purpose of this study is to assess the applicability of the NESMS model to predict 1-year survival among patients treated with radiation therapy alone for spinal metastases. This study is a retrospective analysis. This sample included 290 patients who underwent conventional radiation therapy alone for spinal metastases. Patients' NESMS scores (comprised of ambulatory status, pre-treatment serum albumin, and modified Bauer score) were assessed as well as their 1-year overall survival rates following radiation for metastatic disease of the spine. This study is a single-institution retrospective analysis of 290 patients treated with conventional radiation alone for spinal metastases from 2008 to 2013. The predictive value of the NESMS was assessed using multivariable logistic regression modeling, adjusted for potential confounding variables. This analysis indicated that patients with lower NESMS scores had higher rates of 1-year mortality. Multivariable analysis demonstrated a strong association between lower NESMS scores and lower rates of survival. The NESMS score is a simple prognostic scheme that requires clinical data that is often readily available and has been validated in independent cohorts of surgical patients. This study serves to validate the utility of the NESMS composite score to predict 1-year mortality in patients treated with radiation alone for spinal metastases. Copyright © 2017 Elsevier Inc. All

  8. A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Data.gov (United States)

    National Aeronautics and Space Administration — This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework...

  9. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  10. γ-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer

    Directory of Open Access Journals (Sweden)

    E. Chatzimichail

    2014-01-01

    Full Text Available Cancer is a leading cause of death worldwide and the prognostic evaluation of cancer patients is of great importance in medical care. The use of artificial neural networks in prediction problems is well established in human medical literature. The aim of the current study was to assess the prognostic value of a series of clinical and molecular variables with the addition of γ-H2AX—a new DNA damage response marker—for the prediction of prognosis in patients with early operable non-small cell lung cancer by comparing the γ-H2AX-based artificial network prediction model with the corresponding LR one. Two prognostic models of 96 patients with 27 input variables were constructed by using the parameter-increasing method in order to compare the predictive accuracy of neural network and logistic regression models. The quality of the models was evaluated by an independent validation data set of 11 patients. Neural networks outperformed logistic regression in predicting the patient’s outcome according to the experimental results. To assess the importance of the two factors p53 and γ-H2AX, models without these two variables were also constructed. JR and accuracy of these models were lower than those of the models using all input variables, suggesting that these biological markers are very important for optimal performance of the models. This study indicates that neural networks may represent a potentially more useful decision support tool than conventional statistical methods for predicting the outcome of patients with non-small cell lung cancer and that some molecular markers, such as γ-H2AX, enhance their predictive ability.

  11. Chronic lymphocytic leukemia: A prognostic model comprising only two biomarkers (IGHV mutational status and FISH cytogenetics) separates patients with different outcome and simplifies the CLL-IPI.

    Science.gov (United States)

    Delgado, Julio; Doubek, Michael; Baumann, Tycho; Kotaskova, Jana; Molica, Stefano; Mozas, Pablo; Rivas-Delgado, Alfredo; Morabito, Fortunato; Pospisilova, Sarka; Montserrat, Emili

    2017-04-01

    Rai and Binet staging systems are important to predict the outcome of patients with chronic lymphocytic leukemia (CLL) but do not reflect the biologic diversity of the disease nor predict response to therapy, which ultimately shape patients' outcome. We devised a biomarkers-only CLL prognostic system based on the two most important prognostic parameters in CLL (i.e., IGHV mutational status and fluorescence in situ hybridization [FISH] cytogenetics), separating three different risk groups: (1) low-risk (mutated IGHV + no adverse FISH cytogenetics [del(17p), del(11q)]); (2) intermediate-risk (either unmutated IGHV or adverse FISH cytogenetics) and (3) high-risk (unmutated IGHV + adverse FISH cytogenetics). In 524 unselected subjects with CLL, the 10-year overall survival was 82% (95% CI 76%-88%), 52% (45%-62%), and 27% (17%-42%) for the low-, intermediate-, and high-risk groups, respectively. Patients with low-risk comprised around 50% of the series and had a life expectancy comparable to the general population. The prognostic model was fully validated in two independent cohorts, including 417 patients representative of general CLL population and 337 patients with Binet stage A CLL. The model had a similar discriminatory value as the CLL-IPI. Moreover, it applied to all patients with CLL independently of age, and separated patients with different risk within Rai or Binet clinical stages. The biomarkers-only CLL prognostic system presented here simplifies the CLL-IPI and could be useful in daily practice and to stratify patients in clinical trials. © 2017 Wiley Periodicals, Inc.

  12. Treatment outcome and prognostic factor analysis in transplant-eligible Chinese myeloma patients receiving bortezomib-based induction regimens including the staged approach, PAD or VTD

    Directory of Open Access Journals (Sweden)

    Chim Chor

    2012-06-01

    Full Text Available Abstract Background We have reported promising outcomes using a staged approach, in which bortezomib/thalidomide/dexamethasone was used only in 14 patients with suboptimal response to VAD (vincristine/adriamycin/dexamethasone before autologous stem cell transplantation (ASCT. Here we compared the outcomes of the staged approach with frontline PAD (bortezomib/doxorubicin/dexamethasone or VTD (bortezomib/thalidomide/dexamethasone induction, and analysed prognostic factors for outcome. Patients and methods Ninety-one transplant-eligible Chinese patients received three induction regimens prior to ASCT [staged approach (N = 25, PAD (N = 31, VTD (N = 35]. and received thalidomide maintenance for 2 years post-ASCT. Results 43 (47.3% patients had International Staging System (ISS III disease. By an intention-to-treat analysis, the overall CR/nCR rate were 37.4% post-induction, and 62.6% post-ASCT. Five-year overall (OS and event-free (EFS survivals were 66% and 45.1%. There was no difference of the post-induction CR/nCR rate, EFS or OS between patients induced by these three regimens. Moreover, ISS III disease did not affect CR/nCR rates. Multivariate analysis showed that ISS and post-ASCT CR/nCR impacted OS while ISS and post-induction CR/nCR impacted EFS. Conclusions These three induction regimens produced comparable and favorable outcomes in myeloma. The unfavorable outcome of ISS stage III persisted despite upfront/early use of bortezomib. CR/nCR predicted favorable survivals.

  13. A Language Modeling Approach to TREC

    NARCIS (Netherlands)

    Hiemstra, Djoerd; Kraaij, W.; Voorhees, E.M; Harman, D.

    In this paper we present the language modeling approach to information retrieval as a toolbox to systematically combine information from dierent sources. Four TREC subtasks (Ad Hoc, Entry Page, Adaptive Filtering and Cross-language) are used to illustrate the application of language models to

  14. A two-dimensional volatility basis set – Part 3: Prognostic modeling and NOx dependence

    Directory of Open Access Journals (Sweden)

    W. K. Chuang

    2016-01-01

    Full Text Available When NOx is introduced to organic emissions, aerosol production is sometimes, but not always, reduced. Under certain conditions, these interactions will instead increase aerosol concentrations. We expanded the two-dimensional volatility basis set (2D-VBS to include the effects of NOx on aerosol formation. This includes the formation of organonitrates, where the addition of a nitrate group contributes to a decrease of 2.5 orders of magnitude in volatility. With this refinement, we model outputs from experimental results, such as the atomic N : C ratio, organonitrate mass, and nitrate fragments in Aerosol Mass Spectrometer (AMS measurements. We also discuss the mathematical methods underlying the implementation of the 2D-VBS and provide the complete code in the Supplement. A developer version is available on Bitbucket, an online community repository.

  15. A two-dimensional volatility basis set - Part 3: Prognostic modeling and NOx dependence

    Science.gov (United States)

    Chuang, W. K.; Donahue, N. M.

    2016-01-01

    When NOx is introduced to organic emissions, aerosol production is sometimes, but not always, reduced. Under certain conditions, these interactions will instead increase aerosol concentrations. We expanded the two-dimensional volatility basis set (2D-VBS) to include the effects of NOx on aerosol formation. This includes the formation of organonitrates, where the addition of a nitrate group contributes to a decrease of 2.5 orders of magnitude in volatility. With this refinement, we model outputs from experimental results, such as the atomic N : C ratio, organonitrate mass, and nitrate fragments in Aerosol Mass Spectrometer (AMS) measurements. We also discuss the mathematical methods underlying the implementation of the 2D-VBS and provide the complete code in the Supplement. A developer version is available on Bitbucket, an online community repository.

  16. Basic critical care echocardiography by pulmonary fellows: learning trajectory and prognostic impact using a minimally resourced training model*.

    Science.gov (United States)

    See, Kay Choong; Ong, Venetia; Ng, Jeffrey; Tan, Rou An; Phua, Jason

    2014-10-01

    The spread of basic critical care echocardiography may be limited by training resources. Another barrier is the lack of information about the learning trajectory and prognostic impact of individual basic critical care echocardiography domains like acute cor pulmonale determination and left ventricular function estimation. We thus developed a minimally resourced training model and studied the latter outcomes. Prospective observational study. Twenty-bed medical ICU. Echocardiography-naive trainees enrolled in the first year of our Pulmonary Medicine Fellowship Program from September 2012 to September 2013. We described the learning trajectory in six basic critical care echocardiography domains (adequate views, pericardial effusion, acute cor pulmonale, left ventricular ejection fraction, mitral regurgitation, and inferior vena cava variability) and correlated abnormalities in selected basic critical care echocardiography domains with clinical outcomes (mortality and length of stay). Three-hundred forty-three basic critical care echocardiography scans were done for 318 patients by seven fellows (median of 40 scans per fellow; range, 34-105). Only one-third patients had normal basic critical care echocardiography studies. Accuracy in various basic critical care echocardiography domains was high (> 90%), especially beyond the first 30 examinations. Acute cor pulmonale was associated with ICU mortality when adjusted for Acute Physiology and Chronic Health Evaluation II score and presence of sepsis, whereas mitral regurgitation was associated with longer hospitalization only on univariate analysis. Basic critical care echocardiography training using minimal resources is feasible. New trainees can achieve reasonable competency in most basic critical care echocardiography domains after performing about 30 examinations within the first year. The relatively high prevalence of abnormalities and the significant association of acute cor pulmonale with ICU mortality support the

  17. Prognostic and symptomatic aspects of rapid eye movement sleep in a mouse model of posttraumatic stress disorder

    Directory of Open Access Journals (Sweden)

    Stephanie A. Polta

    2013-05-01

    Full Text Available Not every individual develops Posttraumatic Stress Disorder (PTSD after the exposure to a potentially traumatic event. Therefore, the identification of pre-existing risk factors and early diagnostic biomarkers is of high medical relevance. However, no objective biomarker has yet progressed into clinical practice. Sleep disturbances represent commonly reported complaints in PTSD patients. In particular, changes in rapid eye movement sleep (REMS properties are frequently observed in PTSD patients. Here, we examined in a mouse model of PTSD whether (1 mice developed REMS alterations after trauma and (2 whether REMS architecture before and/or shortly after trauma predicted the development of PTSD-like symptoms. We monitored sleep-wake behavior via combined EEG/EMG recordings immediately before (24 h pre, immediately after (0-48 h post and two months after exposure to an electric foot shock in male C57BL/6N mice (n=15. PTSD-like symptoms, including hyperarousal, contextual and generalized fear, were assessed one month post-trauma.Shocked mice showed early-onset and sustained elevation of REMS compared to non-shocked controls. In addition, REMS architecture before trauma was correlated with the intensity of acoustic startle responses, but not contextual fear, one month after trauma.Our data suggest REMS as prognostic (pre-trauma and symptomatic (post-trauma marker of PTSD-like symptoms in mice. Translated to the situation in humans, REMS may constitute a viable, objective and non-invasive biomarker in PTSD and other trauma-related psychiatric disorders, which could guide pharmacological interventions in humans at high risk.

  18. A Prognostic Model of Surgical Site Infection Using Daily Clinical Wound Assessment.

    Science.gov (United States)

    Sanger, Patrick C; van Ramshorst, Gabrielle H; Mercan, Ezgi; Huang, Shuai; Hartzler, Andrea L; Armstrong, Cheryl A L; Lordon, Ross J; Lober, William B; Evans, Heather L

    2016-08-01

    Surgical site infection (SSI) remains a common, costly, and morbid health care-associated infection. Early detection can improve outcomes, yet previous risk models consider only baseline risk factors (BF) not incorporating a proximate and timely data source-the wound itself. We hypothesize that incorporation of daily wound assessment improves the accuracy of SSI identification compared with traditional BF alone. A prospective cohort of 1,000 post open abdominal surgery patients at an academic teaching hospital were examined daily for serial features (SF), for example, wound characteristics and vital signs, in addition to standard BF, for example, wound class. Using supervised machine learning, we trained 3 Naïve Bayes classifiers (BF, SF, and BF+SF) using patient data from 1 to 5 days before diagnosis to classify SSI on the following day. For comparison, we also created a simplified SF model that used logistic regression. Control patients without SSI were matched on 5 similar consecutive postoperative days to avoid confounding by length of stay. Accuracy, sensitivity/specificity, and area under the receiver operating characteristic curve were calculated on a training and hold-out testing set. Of 851 patients, 19.4% had inpatient SSIs. Univariate analysis showed differences in C-reactive protein, surgery duration, and contamination, but no differences in American Society of Anesthesiologists scores, diabetes, or emergency surgery. The BF, SF, and BF+SF classifiers had area under the receiver operating characteristic curves of 0.67, 0.76, and 0.76, respectively. The best-performing classifier (SF) had optimal sensitivity of 0.80, specificity of 0.64, positive predictive value of 0.35, and negative predictive value of 0.93. Features most associated with subsequent SSI diagnosis were granulation degree, exudate amount, nasogastric tube presence, and heart rate. Serial features provided moderate positive predictive value and high negative predictive value for early

  19. A Combined Model-Based and Data-Driven Prognostic Approach for Aircraft System Life Management

    Data.gov (United States)

    National Aeronautics and Space Administration — Failure prognosis - as a natural extension to the fault detection and isolation (FDI) problem - has become a key issue in a world where the economic impact of system...

  20. Risk Modelling for Passages in Approach Channel

    Directory of Open Access Journals (Sweden)

    Leszek Smolarek

    2013-01-01

    Full Text Available Methods of multivariate statistics, stochastic processes, and simulation methods are used to identify and assess the risk measures. This paper presents the use of generalized linear models and Markov models to study risks to ships along the approach channel. These models combined with simulation testing are used to determine the time required for continuous monitoring of endangered objects or period at which the level of risk should be verified.

  1. Tackling V&V for Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — We believe our approach to gathering and organizing prognostics V the descriptive text recorded proved on occasion to be insufficient to serve as a standalone...

  2. Precursor Parameter Identification for IGBT Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Precursor parameters have been identified to enable development of a prognostic approach for insulated gate bipolar transistors (IGBT). The IGBT were subjected to...

  3. The sequential organ failure assessment (SOFA) score is prognostically superior to the model for end-stage liver disease (MELD) and MELD variants following paracetamol (acetaminophen) overdose.

    Science.gov (United States)

    Craig, D G N; Reid, T W D J; Wright, E C; Martin, K G; Davidson, J S; Hayes, P C; Simpson, K J

    2012-03-01

    The prognostic value of the model for end-stage liver disease (MELD) and sodium-based MELD variants in predicting survival following paracetamol overdose remains unclear. To examine the prognostic accuracy of sodium-based MELD variants in paracetamol-induced acute liver injury compared with the sequential organ failure assessment (SOFA) score. Retrospective analysis of 138 single time point paracetamol overdoses admitted to a tertiary liver centre. Individual laboratory samples were correlated with the corresponding clinical parameters in relation to time post-overdose, and the daily MELD, MELD-Na, MELDNa, MESO, iMELD, UKELD, updated MELD and SOFA scores were calculated. Sixty-six (47.8%) patients developed hepatic encephalopathy, of whom 7 were transplanted and 21 died without liver transplantation. SOFA had a significantly greater area under the receiver operator characteristic for the prediction of spontaneous survival compared with MELD at both 72 (P = 0.024) and 96 (P = 0.017) h post-overdose. None of the sodium-based MELD variants improved the prognostic accuracy of MELD. A SOFA score >6 by 72 h or >7 by 96 h, post-overdose predicted death/transplantation with a negative predictive value of 96.9 (95% CI 90.2-99.4) and 98.8 (95% CI 93.6-99.9) respectively. SOFA and MELD had similar accuracy for predicting the development of hepatic encephalopathy (P = 0.493). The SOFA score is superior to MELD in predicting spontaneous survival following paracetamol-induced acute liver injury. Modification of the MELD score to include serum sodium does not improve prognostic accuracy in this setting. SOFA may have potential as a quantitative triage marker following paracetamol overdose. © 2012 Blackwell Publishing Ltd.

  4. A Multivariate Approach to Functional Neuro Modeling

    DEFF Research Database (Denmark)

    Mørch, Niels J.S.

    1998-01-01

    exists. - Model visualization and interpretation techniques. The simplicity of this task for linear models contrasts the difficulties involved when dealing with nonlinear models. Finally, a visualization technique for nonlinear models is proposed. A single observation emerges from the thesis......This Ph.D. thesis, A Multivariate Approach to Functional Neuro Modeling, deals with the analysis and modeling of data from functional neuro imaging experiments. A multivariate dataset description is provided which facilitates efficient representation of typical datasets and, more importantly......, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: - An introduction of the representation of functional datasets by pairs of neuronal activity patterns...

  5. Comparison of prognostic scores and surgical approaches to treat spinal metastatic tumors: A review of 57 cases

    Directory of Open Access Journals (Sweden)

    Bekar Ahmet

    2008-08-01

    Full Text Available Abstract Surgical treatment of metastatic spinal cord compression with or without neural deficit is controversial. Karnofsky and Tokuhashi scores have been proposed for prognosis of spinal metastasis. Here, we conducted a retrospective analysis of Karnofsky and modified Tokuhashi scores in 57 consecutive patients undergoing surgery for secondary spinal metastases to evaluate the value of these scores in aiding decision making for surgery. Comparison of preoperative Karnofsky and modified Tokuhashi scores with the type of the surgical approach for each patient revealed that both scores not only reliably estimate life expectancy, but also objectively improved surgical decisions. When the general status of the patient is poor (i.e., Karnofsky score less than 40% or modified Tokuhashi score of 5 or greater, palliative treatments and radiotherapy, rather than surgery, should be considered.

  6. Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

    Energy Technology Data Exchange (ETDEWEB)

    Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts (United States); Haasbeek, Cornelis J.A. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Mokhles, Sahar [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Rodrigues, George B. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Stephans, Kevin L. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Lagerwaard, Frank J. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Palma, David A. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Warner, Andrew [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Takkenberg, Johanna J.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Reddy, Chandana A. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Maat, Alex P.W.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Woody, Neil M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Slotman, Ben J.; Senan, Suresh [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands)

    2015-09-01

    Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.

  7. Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model.

    Science.gov (United States)

    Louie, Alexander V; Haasbeek, Cornelis J A; Mokhles, Sahar; Rodrigues, George B; Stephans, Kevin L; Lagerwaard, Frank J; Palma, David A; Videtic, Gregory M M; Warner, Andrew; Takkenberg, Johanna J M; Reddy, Chandana A; Maat, Alex P W M; Woody, Neil M; Slotman, Ben J; Senan, Suresh

    2015-09-01

    A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n = 193) and SABR (n = 543) datasets. RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r(2) = 0.97) and external SABR (r(2) = 0.79) and surgical cohorts (r(2) = 0.91). The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Prognostic molecular markers in cancer - quo vadis?

    Science.gov (United States)

    Søland, Tine M; Brusevold, Ingvild J

    2013-09-01

    Despite the tremendous number of studies of prognostic molecular markers in cancer, only a few such markers have entered clinical practise. The lack of clinical prognostic markers clearly reflects limitations in or an inappropriate approach to prognostic studies. This situation should be of great concern for the research community, clinicians and patients. In the present review, we evaluate immunohistochemical prognostic marker studies in oral squamous cell carcinomas (OSCC) from 2006 to 2012. We comment upon general issues such as study design, assay methods and statistical methods, applicable to prognostic marker studies irrespective of cancer type. The three most frequently studied markers in OSCC are reviewed. Our analysis revealed that most new molecular markers are reported only once. To draw conclusions of clinical relevance based on the few markers that appeared in more than one study was problematic due to between-study heterogeneity. Currently, much valuable tissue material, time and money are wasted on irrelevant studies. © 2013 John Wiley & Sons Ltd.

  9. A Community Approach to Earth Systems Modeling

    Science.gov (United States)

    Voinov, Alexey A.; DeLuca, Cecelia; Hood, Raleigh R.; Peckham, Scott; Sherwood, Christopher R.; Syvitski, James P. M.

    2010-03-01

    Earth science often deals with complex systems spanning multiple disciplines. These systems are best described by integrated models built with contributions from specialists of many backgrounds. But building integrated models can be difficult; modular and hierarchical approaches help to manage the increasing complexity of these modeling systems, but there is a need for framework and integration methods and standards to support modularity. Complex models require many data and generate lots of output, so software and standards are required for data handling, model output, data distribution services, and user interfaces. Complex modeling systems must be efficient to be useful, so they require contributions by software engineers to ensure efficient architectures, accurate numerics, and implementation on fast computers. Further, integrated model systems can be difficult to learn and use unless adequate documentation, training, and support are provided.

  10. Behavioural queuing: an agent based modelling approach

    OpenAIRE

    Sankaranarayanan Karthik; Arturo Delgado-Alvarez Carlos; R Larsen Erik; van Ackere Ann

    2012-01-01

    Queueing research has a plethora of applications and has been an area of study spanning from telecommunications to economics. Traditionally studies on queueing has mainly concentrated on design performance and running of the service facility with customers arriving following a stochastic process. In this paper we take an agent based modeling approach to develop a behavioral model of a queueing system using Cellular Automata (CA). We study how adaptive expectation along with a simple informati...

  11. Imputation approaches for animal movement modeling

    Science.gov (United States)

    Scharf, Henry; Hooten, Mevin B.; Johnson, Devin S.

    2017-01-01

    The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.

  12. A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon.

    Directory of Open Access Journals (Sweden)

    Ena Wang

    Full Text Available PURPOSE: IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival. EXPERIMENTAL DESIGN: We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy. RESULTS: Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021, HLA-C15 (p = 0.025, HLA-C3 (p = 0.014, DRB1*15 (p = 0.005 and CT60*G/G (0.081 were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013-0.709, 0.387 (95% CI, 0.169-0.889, 0.449 (95% CI, 0.237-0.851, 1.948 (95% CI, 1.221-3.109 and 1.484 (95% IC, 0.953-2.312 respectively. CONCLUSION: These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed.

  13. External validation of a proposed prognostic model for the prediction of 1-year postoperative eGFR after living donor nephrectomy.

    Science.gov (United States)

    Kulik, Ulf; Gwiasda, Jill; Oldhafer, Felix; Kaltenborn, Alexander; Arelin, Viktor; Gueler, Faikah; Richter, Nicolas; Klempnauer, Juergen; Schrem, Harald

    2017-11-01

    The goal of this study was to externally validate the recently proposed prognostic model for the prediction of estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m(2) 1 year after living donor nephrectomy. 130 living kidney donors (median age at donation 52.3 years, range 24.7-75.6 years) were investigated before and after donation between March 2000 and April 2016. Preoperative eGFR values varied between 61.7 and 148.4 ml/min (mean: 89, median: 88). Observed eGFR 1 year after transplantation (±45 days) ranged between 36.3 and 97.1 ml/min (mean: 55, median: 53). 70.8% of donors displayed eGFR values < 60 ml/min 1 year after donation. Predicted eGFR 1 year after donation was determined using the prognostic model proposed by Benoit et al. (Int Urol Nephrol 49(5):793-801. doi: 10.1007/s11255-017-1559-1 , 2017): postoperative eGFR ml/min/1.73 m(2) = 31.71 + (0.521 × eGFR in ml/min prior to donation -0.314 × Age in years at donation). Pearson correlation and receiver operating characteristics curve (ROC-curve) were used to assess external validity of the proposed prognostic model to predict postoperative eGFR in ml/min and eGFR < 60 ml/min. The correlation between predicted and observed eGFR 1 year after donation was significant (p < 0.001; R (2) = 0.594). The area under the ROC-curve (AUROC) demonstrated a high sensitivity and specificity for predicted eGFR values < 60 ml/min (AUROC = 0.866). The proposed prognostic model for the prediction of postoperative eGFR was successfully validated in our cohort. We therefore consider the model as generally applicable.

  14. A Transfer Learning Approach for Network Modeling

    Science.gov (United States)

    Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li

    2012-01-01

    Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804

  15. A Conceptual Modeling Approach for OLAP Personalization

    Science.gov (United States)

    Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan

    Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

  16. Neural network approaches for noisy language modeling.

    Science.gov (United States)

    Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid

    2013-11-01

    Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.

  17. Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study.

    Science.gov (United States)

    Serpa Neto, Ary; Assunção, Murillo Santucci Cesar de; Pardini, Andréia; Silva, Eliézer

    2015-01-01

    Prognostic models reflect the population characteristics of the countries from which they originate. Predictive models should be customized to fit the general population where they will be used. The aim here was to perform external validation on two predictive models and compare their performance in a mixed population of critically ill patients in Brazil. Retrospective study in a Brazilian general intensive care unit (ICU). This was a retrospective review of all patients admitted to a 41-bed mixed ICU from August 2011 to September 2012. Calibration (assessed using the Hosmer-Lemeshow goodness-of-fit test) and discrimination (assessed using area under the curve) of APACHE II and SAPS III were compared. The standardized mortality ratio (SMR) was calculated by dividing the number of observed deaths by the number of expected deaths. A total of 3,333 ICU patients were enrolled. The Hosmer-Lemeshow goodness-of-fit test showed good calibration for all models in relation to hospital mortality. For in-hospital mortality there was a worse fit for APACHE II in clinical patients. Discrimination was better for SAPS III for in-ICU and in-hospital mortality (P = 0.042). The SMRs for the whole population were 0.27 (confidence interval [CI]: 0.23 - 0.33) for APACHE II and 0.28 (CI: 0.22 - 0.36) for SAPS III. In this group of critically ill patients, SAPS III was a better prognostic score, with higher discrimination and calibration power.

  18. Predicting intracranial hemorrhage after traumatic brain injury in low and middle-income countries: A prognostic model based on a large, multi-center, international cohort

    Directory of Open Access Journals (Sweden)

    Subaiya Saleena

    2012-11-01

    Full Text Available Abstract Background Traumatic brain injury (TBI affects approximately 10 million people annually, of which intracranial hemorrhage is a devastating sequelae, occurring in one-third to half of cases. Patients in low and middle-income countries (LMIC are twice as likely to die following TBI as compared to those in high-income countries. Diagnostic capabilities and treatment options for intracranial hemorrhage are limited in LMIC as there are fewer computed tomography (CT scanners and neurosurgeons per patient as in high-income countries. Methods The Medical Research Council CRASH-1 trial was utilized to build this model. The study cohort included all patients from LMIC who received a CT scan of the brain (n = 5669. Prognostic variables investigated included age, sex, time from injury to randomization, pupil reactivity, cause of injury, seizure and the presence of major extracranial injury. Results There were five predictors that were included in the final model; age, Glasgow Coma Scale, pupil reactivity, the presence of a major extracranial injury and time from injury to presentation. The model demonstrated good discrimination and excellent calibration (c-statistic 0.71. A simplified risk score was created for clinical settings to estimate the percentage risk of intracranial hemorrhage among TBI patients. Conclusion Simple prognostic models can be used in LMIC to estimate the risk of intracranial hemorrhage among TBI patients. Combined with clinical judgment this may facilitate risk stratification, rapid transfer to higher levels of care and treatment in resource-poor settings.

  19. A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment).

    Science.gov (United States)

    Passamonti, Francesco; Cervantes, Francisco; Vannucchi, Alessandro Maria; Morra, Enrica; Rumi, Elisa; Pereira, Arturo; Guglielmelli, Paola; Pungolino, Ester; Caramella, Marianna; Maffioli, Margherita; Pascutto, Cristiana; Lazzarino, Mario; Cazzola, Mario; Tefferi, Ayalew

    2010-03-04

    Age older than 65 years, hemoglobin level lower than 100 g/L (10 g/dL), white blood cell count greater than 25 x 10(9)/L, peripheral blood blasts 1% or higher, and constitutional symptoms have been shown to predict poor survival in primary myelofibrosis (PMF) at diagnosis. To investigate whether the acquisition of these factors during follow-up predicts survival, we studied 525 PMF patients regularly followed. All 5 variables had a significant impact on survival when analyzed as time-dependent covariates in a multivariate Cox proportional hazard model and were included in 2 separate models, 1 for all patients (Dynamic International Prognostic Scoring System [DIPSS]) and 1 for patients younger than 65 years (age-adjusted DIPSS). Risk factors were assigned score values based on hazard ratios (HRs). Risk categories were low, intermediate-1, intermediate-2, and high in both models. Survival was estimated by the HR. When shifting to the next risk category, the HR was 4.13 for low risk, 4.61 for intermediate-1, and 2.54 for intermediate-2 according to DIPSS; 3.97 for low risk, 2.84 for intermediate-1, and 1.81 for intermediate-2 according to the age-adjusted DIPSS. The novelty of these models is the prognostic assessment of patients with PMF anytime during their clinical course, which may be useful for treatment decision-making.

  20. Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation

    Energy Technology Data Exchange (ETDEWEB)

    Hines, J. Wesley [Univ. of Tennessee, Knoxville, TN (United States); Upadhyaya, Belle [Univ. of Tennessee, Knoxville, TN (United States); Sharp, Michael [Univ. of Tennessee, Knoxville, TN (United States); Ramuhalli, Pradeep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Jeffries, Brien [Univ. of Tennessee, Knoxville, TN (United States); Nam, Alan [Univ. of Tennessee, Knoxville, TN (United States); Strong, Eric [Univ. of Tennessee, Knoxville, TN (United States); Tong, Matthew [Univ. of Tennessee, Knoxville, TN (United States); Welz, Zachary [Univ. of Tennessee, Knoxville, TN (United States); Barbieri, Federico [Univ. of Tennessee, Knoxville, TN (United States); Langford, Seth [Univ. of Tennessee, Knoxville, TN (United States); Meinweiser, Gregory [Univ. of Tennessee, Knoxville, TN (United States); Weeks, Matthew [Univ. of Tennessee, Knoxville, TN (United States)

    2014-11-06

    RUL predictions, with as little uncertainty as possible. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. Bayesian methods are best used when limited data are available. The use of a prior also means that information is conserved when new data are available. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. The general approach to applying the

  1. Heat transfer modeling an inductive approach

    CERN Document Server

    Sidebotham, George

    2015-01-01

    This innovative text emphasizes a "less-is-more" approach to modeling complicated systems such as heat transfer by treating them first as "1-node lumped models" that yield simple closed-form solutions. The author develops numerical techniques for students to obtain more detail, but also trains them to use the techniques only when simpler approaches fail. Covering all essential methods offered in traditional texts, but with a different order, Professor Sidebotham stresses inductive thinking and problem solving as well as a constructive understanding of modern, computer-based practice. Readers learn to develop their own code in the context of the material, rather than just how to use packaged software, offering a deeper, intrinsic grasp behind models of heat transfer. Developed from over twenty-five years of lecture notes to teach students of mechanical and chemical engineering at The Cooper Union for the Advancement of Science and Art, the book is ideal for students and practitioners across engineering discipl...

  2. A multiscale modeling approach for biomolecular systems

    Energy Technology Data Exchange (ETDEWEB)

    Bowling, Alan, E-mail: bowling@uta.edu; Haghshenas-Jaryani, Mahdi, E-mail: mahdi.haghshenasjaryani@mavs.uta.edu [The University of Texas at Arlington, Department of Mechanical and Aerospace Engineering (United States)

    2015-04-15

    This paper presents a new multiscale molecular dynamic model for investigating the effects of external interactions, such as contact and impact, during stepping and docking of motor proteins and other biomolecular systems. The model retains the mass properties ensuring that the result satisfies Newton’s second law. This idea is presented using a simple particle model to facilitate discussion of the rigid body model; however, the particle model does provide insights into particle dynamics at the nanoscale. The resulting three-dimensional model predicts a significant decrease in the effect of the random forces associated with Brownian motion. This conclusion runs contrary to the widely accepted notion that the motor protein’s movements are primarily the result of thermal effects. This work focuses on the mechanical aspects of protein locomotion; the effect ATP hydrolysis is estimated as internal forces acting on the mechanical model. In addition, the proposed model can be numerically integrated in a reasonable amount of time. Herein, the differences between the motion predicted by the old and new modeling approaches are compared using a simplified model of myosin V.

  3. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

    Science.gov (United States)

    Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

    2017-01-01

    Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a

  4. Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

    Directory of Open Access Journals (Sweden)

    Liu Junqiang

    2014-10-01

    Full Text Available Remaining useful life (RUL prognostics is a fundamental premise to perform condition-based maintenance (CBM for a system subject to performance degradation. Over the past decades, research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve, prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.

  5. A hybrid approach to empirical magnetosphere modeling

    Science.gov (United States)

    Tsyganenko, N. A.; Andreeva, V. A.

    2017-08-01

    A new approach has been devised and explored to reconstruct magnetospheric configurations, based on spacecraft data and a synthesis of two methods of modeling the magnetic field of extraterrestrial currents. The main idea is to combine within a single framework (1) a modular structure explicitly representing separate contributions to the total field from the magnetopause, ring, tail, and field-aligned currents, and (2) a system of densely distributed field sources, modeled by the radial basis functions (RBF). In such an arrangement, the modular part takes on a role of the principal component representing the gross large-scale structure of the magnetosphere, whereas the RBF part serves as a higher-order correction that compensates for the lack of flexibility of the modular component. The approach has been tested on four subsets of spacecraft data, corresponding to four phases of a geomagnetic storm, and was shown to tangibly improve the model's performance. In particular, it allows proper representation of magnetic effects of the field-aligned currents both at low altitudes and in the distant magnetosphere, as well as inclusion of extensive high-latitude field depressions associated with diamagnetism of the polar cusp plasma, missing in earlier empirical models. It also helps to more accurately model the nightside magnetosphere, so that most of the large-scale magnetotail field is compactly described by a dedicated module inherited from an earlier empirical model, while the RBF component's task is to resolve finer details in the inner magnetosphere.

  6. Multiplex polymerase chain reaction-based prognostic models in diffuse large B-cell lymphoma patients treated with R-CHOP

    DEFF Research Database (Denmark)

    Green, Tina M.; Jensen, Andreas K.; Holst, René

    2016-01-01

    We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B-cell lymphoma (DLBCL). Real-time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de...... models. The best model was validated in data from an online available R-CHOP treated cohort. With progression-free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAMxMIB1, GCSAMxCTGF and FOXP1xPDE4B....... 82% for low risk group (P new drug trials....

  7. Fidelity approach to the Hubbard model

    Science.gov (United States)

    Campos Venuti, L.; Cozzini, M.; Buonsante, P.; Massel, F.; Bray-Ali, N.; Zanardi, P.

    2008-09-01

    We use the fidelity approach to quantum critical points to study the zero-temperature phase diagram of the one-dimensional Hubbard model. Using a variety of analytical and numerical techniques, we analyze the fidelity metric in various regions of the phase diagram with particular care to the critical points. Specifically we show that close to the Mott transition, taking place at on-site repulsion U=0 and electron density n=1 , the fidelity metric satisfies an hyperscaling form which we calculate. This implies that in general, as one approaches the critical point U=0 , n=1 , the fidelity metric tends to a limit which depends on the path of approach. At half-filling, the fidelity metric is expected to diverge as U-4 when U is sent to zero.

  8. Critical Assessment of Clinical Prognostic Tools in Melanoma.

    Science.gov (United States)

    Mahar, Alyson L; Compton, Carolyn; Halabi, Susan; Hess, Kenneth R; Gershenwald, Jeffrey E; Scolyer, Richard A; Groome, Patti A

    2016-09-01

    The 7th edition American Joint Committee on Cancer (AJCC) melanoma staging system classifies patients according to prognosis. Significant within-stage heterogeneity remains and the inclusion of additional clinicopathologic and other host- and tumor-based prognostic factors have been proposed. Clinical prognostic tools have been developed for use in clinical practice to refine survival estimates. Little is known about the comparative features of tools in melanoma. We performed a systematic search of the scientific published literature for clinical prognostic tools in melanoma and web-based resources. A priori criteria were used to evaluate their quality and clinical relevance, and included intended clinical use, model development approaches, validation strategies, and performance metrics. We identified 17 clinical prognostic tools for primary cutaneous melanoma. Patients with stages I-III and T1 or thin melanoma were the most frequently considered populations. Seventy-five percent of tools were developed using data collected from patients diagnosed in 2006 or earlier, and the well-established factors of tumor thickness, ulceration, and age were included in 70 % of tools. Internal validity using cross-validation or bootstrapping techniques was performed for two tools only. Fewer than half were evaluated for external validity; however, when done, the appropriate statistical methodology was applied and results indicated good generalizability. Several clinical prognostic tools have the potential to refine survival estimates for individual melanoma patients; however, there is a great opportunity to improve these tools and to foster the development of new, validated tools by the inclusion of contemporary clinicopathological covariates and by using improved statistical and methodological approaches.

  9. Prognostic model for advanced breast carcinoma with luminal subtype and impact of hormonal maintenance: Implications for post-progression and conditional survival.

    Science.gov (United States)

    Carbognin, Luisa; Sperduti, Isabella; Ciccarese, Mariangela; Fabi, Alessandra; Petrucelli, Luciana; Vari, Sabrina; Forcignanò, Rosa Chiara; Nortilli, Rolando; Vicentini, Cecilia; Pilotto, Sara; Merler, Sara; Zampiva, Ilaria; Brunelli, Matteo; Manfrin, Erminia; Giannarelli, Diana; Tortora, Giampaolo; Bria, Emilio

    2016-10-01

    The aim of this analysis was to develop and validate a prognostic model for advanced breast cancer (ABC) with luminal subtype based on the combination of clinical, pathological and therapeutic predictors to provide a practical tool to evaluate patients' prognosis. Clinical and pathological data were retrospectively correlated to progression-free and overall survival (PFS/OS) using a Cox model. Significant treatment variables were adjusted with the propensity score analysis. A continuous score to identify risk classes was derived according to model ratios. The performance of the risk-class model was tested for post-progression survival (PPS) and conditional survival (CS) as well. Data from 335 patients (3 institutions) were gathered (median follow-up 58 months). At multivariate analysis Ki67, Performance Status (PS) and number of metastatic sites were significant predictors for PFS, whereas Ki67, PS, brain metastases, PFS after 1st-line therapy, number of chemotherapy lines, hormonal therapy and maintenance were significant predictors for OS. The hormonal maintenance resulted to be prognostic after adjustment with propensity score analysis. A two-class model significantly differentiated low-risk and high-risk patients for 2-year PFS (31.5% and 11.0%, p model separated low risk, intermediate-risk, and high-risk patients for 2-year PFS (40.8%, 24.4%, and 11.0%, p models equally discriminate the luminal ABC prognosis in terms of PPS and CS. A risk stratification model including 'easy-to-obtain' clinical, pathological and therapeutic parameters accurately separates luminal ABC patients into different risk classes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Interfacial Fluid Mechanics A Mathematical Modeling Approach

    CERN Document Server

    Ajaev, Vladimir S

    2012-01-01

    Interfacial Fluid Mechanics: A Mathematical Modeling Approach provides an introduction to mathematical models of viscous flow used in rapidly developing fields of microfluidics and microscale heat transfer. The basic physical effects are first introduced in the context of simple configurations and their relative importance in typical microscale applications is discussed. Then,several configurations of importance to microfluidics, most notably thin films/droplets on substrates and confined bubbles, are discussed in detail.  Topics from current research on electrokinetic phenomena, liquid flow near structured solid surfaces, evaporation/condensation, and surfactant phenomena are discussed in the later chapters. This book also:  Discusses mathematical models in the context of actual applications such as electrowetting Includes unique material on fluid flow near structured surfaces and phase change phenomena Shows readers how to solve modeling problems related to microscale multiphase flows Interfacial Fluid Me...

  11. MODELS OF TECHNOLOGY ADOPTION: AN INTEGRATIVE APPROACH

    Directory of Open Access Journals (Sweden)

    Andrei OGREZEANU

    2015-06-01

    Full Text Available The interdisciplinary study of information technology adoption has developed rapidly over the last 30 years. Various theoretical models have been developed and applied such as: the Technology Acceptance Model (TAM, Innovation Diffusion Theory (IDT, Theory of Planned Behavior (TPB, etc. The result of these many years of research is thousands of contributions to the field, which, however, remain highly fragmented. This paper develops a theoretical model of technology adoption by integrating major theories in the field: primarily IDT, TAM, and TPB. To do so while avoiding mess, an approach that goes back to basics in independent variable type’s development is proposed; emphasizing: 1 the logic of classification, and 2 psychological mechanisms behind variable types. Once developed these types are then populated with variables originating in empirical research. Conclusions are developed on which types are underpopulated and present potential for future research. I end with a set of methodological recommendations for future application of the model.

  12. Continuum modeling an approach through practical examples

    CERN Document Server

    Muntean, Adrian

    2015-01-01

    This book develops continuum modeling skills and approaches the topic from three sides: (1) derivation of global integral laws together with the associated local differential equations, (2) design of constitutive laws and (3) modeling boundary processes. The focus of this presentation lies on many practical examples covering aspects such as coupled flow, diffusion and reaction in porous media or microwave heating of a pizza, as well as traffic issues in bacterial colonies and energy harvesting from geothermal wells. The target audience comprises primarily graduate students in pure and applied mathematics as well as working practitioners in engineering who are faced by nonstandard rheological topics like those typically arising in the food industry.

  13. Pedagogic process modeling: Humanistic-integrative approach

    OpenAIRE

    Boritko Nikolaj M.

    2007-01-01

    The paper deals with some current problems of modeling the dynamics of the subject-features development of the individual. The term "process" is considered in the context of the humanistic-integrative approach, in which the principles of self education are regarded as criteria for efficient pedagogic activity. Four basic characteristics of the pedagogic process are pointed out: intentionality reflects logicality and regularity of the development of the process; discreteness (stageability) in ...

  14. Systems Engineering Interfaces: A Model Based Approach

    Science.gov (United States)

    Fosse, Elyse; Delp, Christopher

    2013-01-01

    Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.

  15. Algebraic operator approach to gas kinetic models

    Science.gov (United States)

    Il'ichov, L. V.

    1997-02-01

    Some general properties of the linear Boltzmann kinetic equation are used to present it in the form ∂ tϕ = - †Âϕ with the operators Âand† possessing some nontrivial algebraic properties. When applied to the Keilson-Storer kinetic model, this method gives an example of quantum ( q-deformed) Lie algebra. This approach provides also a natural generalization of the “kangaroo model”.

  16. Cytogenetic Prognostication Within Medulloblastoma Subgroups

    Science.gov (United States)

    Shih, David J.H.; Northcott, Paul A.; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M.; Garzia, Livia; Peacock, John; Mack, Stephen C.; Wu, Xiaochong; Rolider, Adi; Morrissy, A. Sorana; Cavalli, Florence M.G.; Jones, David T.W.; Zitterbart, Karel; Faria, Claudia C.; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A.; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G.; Liau, Linda M.; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K.; Thompson, Reid C.; Bailey, Simon; Lindsey, Janet C.; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M.C.; Scherer, Stephen W.; Phillips, Joanna J.; Gupta, Nalin; Fan, Xing; Muraszko, Karin M.; Vibhakar, Rajeev; Eberhart, Charles G.; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J.; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F.; Weiss, William A.; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R.; Rubin, Joshua B.; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M.; Gajjar, Amar; Packer, Roger J.; Rutkowski, Stefan; Pomeroy, Scott L.; French, Pim J.; Kloosterhof, Nanne K.; Kros, Johan M.; Van Meir, Erwin G.; Clifford, Steven C.; Bourdeaut, Franck; Delattre, Olivier; Doz, François F.; Hawkins, Cynthia E.; Malkin, David; Grajkowska, Wieslawa A.; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T.; Pfister, Stefan M.; Taylor, Michael D.

    2014-01-01

    Purpose Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Patients and Methods Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Results Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Conclusion Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials. PMID

  17. Preoperative multivariable prognostic models for prediction of survival and major complications following surgical resection of renal cell carcinoma with suprahepatic caval tumor thrombus.

    Science.gov (United States)

    Haddad, Ahmed Q; Leibovich, Bradley C; Abel, Edwin Jason; Luo, Jun-Hang; Krabbe, Laura-Maria; Thompson, Robert Houston; Heckman, Jennifer E; Merrill, Megan M; Gayed, Bishoy A; Sagalowsky, Arthur I; Boorjian, Stephen A; Wood, Christopher G; Margulis, Vitaly

    2015-09-01

    Surgical resection for renal cell carcinoma (RCC) with suprahepatic inferior vena cava tumor thrombus is associated with significant morbidity, yet there are currently no tools for preoperative prognostic evaluation. Our goal was to develop a preoperative multivariable model for prediction of survival and risk of major complications in patients with suprahepatic thrombi. We identified patients who underwent surgery for RCC with suprahepatic tumor thrombus extension from 2000 to 2013 at 4 tertiary centers. A Cox proportional hazard model was used for analysis of overall survival (OS) and logistic regression was used for major complications within 90 days of surgery (Clavien ≥ 3A). Nomograms were internally calibrated by bootstrap resampling method. A total of 49 patients with level III thrombus and 83 patients with level IV thrombus were identified. During median follow-up of 24.5 months, 80 patients (60.6%) died and 46 patients (34.8%) experienced major complication. Independent prognostic factors for OS included distant metastases at presentation (hazard ratio = 2.52, P = 0.002) and Eastern Cooperative Oncology Group (ECOG) performance status (hazard ratio = 1.84, Pmodels for the prediction of survival and major complications in patients with RCC who have a suprahepatic inferior vena cava thrombus. If externally validated, these tools may aid in patient selection for surgical intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Distributed Prognostic Health Management with Gaussian Process Regression

    Science.gov (United States)

    Saha, Sankalita; Saha, Bhaskar; Saxena, Abhinav; Goebel, Kai Frank

    2010-01-01

    Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

  19. Prognostic factors for survival in metastatic renal cell carcinoma: update 2008.

    Science.gov (United States)

    Bukowski, Ronald M

    2009-05-15

    A variety of prognostic factor models to predict survival in patients with metastatic renal cell carcinoma have been developed. Diverse populations of patients with variable treatments have been used for these analyses. A variety of clinical, pathologic, and molecular factors have been studied, but current models use predominantly easily obtained clinical factors. These approaches are reviewed, and current approaches to further refine and develop these techniques are reviewed. (c) 2009 American Cancer Society.

  20. Prognostic Performance Metrics

    Data.gov (United States)

    National Aeronautics and Space Administration — This chapter presents several performance metrics for offline evaluation of prognostics algorithms. A brief overview of different methods employed for performance...

  1. Prognostic model to identify patients with myelofibrosis at the highest risk of transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Kantarjian, Hagop; Pierce, Sherry; Cortes, Jorge; Verstovsek, Srdan

    2013-06-01

    Some patients with myelofibrosis (MF) progress to acute myeloid leukemia (AML). Current prognostic tools were not devised to assess risk of AML transformation. Multivariate analysis in 649 patients followed for a median of 19 months (range, 1-180 months). We identified age > 60 (P = .004; hazard ratio [HR], 1.63), platelets HR, 1.62), bone marrow blast > 10% (P = .002; HR, 2.18), high-risk karyotype (P HR, 2.44), transfusion dependency (P HR, 2.64), performance status > 1 (P = .003; HR, 1.47), lactate dehydrogenase > 2000 U/L (P HR, 1.62), previous hydroxyurea (P HR, 1.69), and male sex (P = .005; HR, 1.41) as independent poor prognostic factors for survival. Using the same baseline variables we identified bone marrow blasts >10% and worst karyotype as independent risk factors for AML transformation. Patients with 1 or both of these risk factors (n = 80; 12%) had a median survival of 10 months and a 1-year AML transformation rate of 13% (2% if none of those factors, P = .001). We have identified risk factors that predict high risk of transformation from MF to AML. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES

    Directory of Open Access Journals (Sweden)

    H. Sadeq

    2016-06-01

    Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

  3. Prognostic Factors and a New Prognostic Index Model for Children and Adolescents with Hodgkin’s Lymphoma Who Underwent Autologous Hematopoietic Stem Cell Transplantation: A Multicenter Study of the Turkish Pediatric Bone Marrow Transplantation Study

    Directory of Open Access Journals (Sweden)

    Vural Kesik

    2016-12-01

    Full Text Available Objective: The prognostic factors and a new childhood prognostic index after autologous hematopoietic stem cell transplantation (AHSCT in patients with relapsed/refractory Hodgkin’s lymphoma (HL were evaluated. Materials and Methods: The prognostic factors of 61 patients who underwent AHSCT between January 1990 and December 2014 were evaluated. In addition, the Age-Adjusted International Prognostic Index and the Childhood International Prognostic Index (CIPI were evaluated for their impact on prognosis. Results: The median age of the 61 patients was 14.8 years (minimummaximum: 5-20 years at the time of AHSCT. There were single relapses in 28 patients, ≥2 relapses in eight patients, and refractory disease in 25 patients. The chemosensitivity/chemorefractory ratio was 36/25. No pretransplant radiotherapy, no remission at the time of transplantation, posttransplant white blood cell count over 10x103/ μL, posttransplant positron emission tomography positivity at day 100, and serum albumin of <2.5 g/dL at diagnosis were correlated with progression-free survival. No remission at the time of transplantation, bone marrow positivity at diagnosis, and relapse after AHSCT were significant parameters for overall survival. Conclusion: The major factors affecting the progression-free and overall survival were clearly demonstrated. A CIPI that uses a lactate dehydrogenase level of 500 IU/L worked well for estimating the prognosis. We recommend AHSCT at first complete remission for relapsed cases, and it should also be taken into consideration for patients with high prognostic scores at diagnosis.

  4. Modeling for fairness: A Rawlsian approach.

    Science.gov (United States)

    Diekmann, Sven; Zwart, Sjoerd D

    2014-06-01

    In this paper we introduce the overlapping design consensus for the construction of models in design and the related value judgments. The overlapping design consensus is inspired by Rawls' overlapping consensus. The overlapping design consensus is a well-informed, mutual agreement among all stakeholders based on fairness. Fairness is respected if all stakeholders' interests are given due and equal attention. For reaching such fair agreement, we apply Rawls' original position and reflective equilibrium to modeling. We argue that by striving for the original position, stakeholders expel invalid arguments, hierarchies, unwarranted beliefs, and bargaining effects from influencing the consensus. The reflective equilibrium requires that stakeholders' beliefs cohere with the final agreement and its justification. Therefore, the overlapping design consensus is not only an agreement to decisions, as most other stakeholder approaches, it is also an agreement to their justification and that this justification is consistent with each stakeholders' beliefs. For supporting fairness, we argue that fairness qualifies as a maxim in modeling. We furthermore distinguish values embedded in a model from values that are implied by its context of application. Finally, we conclude that for reaching an overlapping design consensus communication about properties of and values related to a model is required.

  5. Modeling in transport phenomena a conceptual approach

    CERN Document Server

    Tosun, Ismail

    2007-01-01

    Modeling in Transport Phenomena, Second Edition presents and clearly explains with example problems the basic concepts and their applications to fluid flow, heat transfer, mass transfer, chemical reaction engineering and thermodynamics. A balanced approach is presented between analysis and synthesis, students will understand how to use the solution in engineering analysis. Systematic derivations of the equations and the physical significance of each term are given in detail, for students to easily understand and follow up the material. There is a strong incentive in science and engineering to

  6. Model approach brings multi-level success.

    Science.gov (United States)

    Howell, Mark

    2012-08-01

    n an article that first appeared in US magazine, Medical Construction & Design, Mark Howell, senior vice-president of Skanska USA Building, based in Seattle, describes the design and construction of a new nine-storey, 350,000 ft2 extension to the Good Samaritan Hospital in Puyallup, Washington state. He explains how the use of an Integrated Project Delivery (IPD) approach by the key players, and extensive use of building information modelling (BIM), combined to deliver a healthcare facility that he believes should meet the needs of patients, families, and the clinical care team, 'well into the future'.

  7. Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

    Science.gov (United States)

    Saha, Bhaskar; Goebel, kai

    2007-01-01

    Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

  8. Pedagogic process modeling: Humanistic-integrative approach

    Directory of Open Access Journals (Sweden)

    Boritko Nikolaj M.

    2007-01-01

    Full Text Available The paper deals with some current problems of modeling the dynamics of the subject-features development of the individual. The term "process" is considered in the context of the humanistic-integrative approach, in which the principles of self education are regarded as criteria for efficient pedagogic activity. Four basic characteristics of the pedagogic process are pointed out: intentionality reflects logicality and regularity of the development of the process; discreteness (stageability in dicates qualitative stages through which the pedagogic phenomenon passes; nonlinearity explains the crisis character of pedagogic processes and reveals inner factors of self-development; situationality requires a selection of pedagogic conditions in accordance with the inner factors, which would enable steering the pedagogic process. Offered are two steps for singling out a particular stage and the algorithm for developing an integrative model for it. The suggested conclusions might be of use for further theoretic research, analyses of educational practices and for realistic predicting of pedagogical phenomena. .

  9. Analytic prognostic for petrochemical pipelines

    CERN Document Server

    Jaoude, Abdo Abou; El-Tawil, Khaled; Noura, Hassan; Ouladsine, Mustapha

    2012-01-01

    Pipelines tubes are part of vital mechanical systems largely used in petrochemical industries. They serve to transport natural gases or liquids. They are cylindrical tubes and are submitted to the risks of corrosion due to high PH concentrations of the transported liquids in addition to fatigue cracks due to the alternation of pressure-depression of gas along the time, initiating therefore in the tubes body micro-cracks that can propagate abruptly to lead to failure. The development of the prognostic process for such systems increases largely their performance and their availability, as well decreases the global cost of their missions. Therefore, this paper deals with a new prognostic approach to improve the performance of these pipelines. Only the first mode of crack, that is, the opening mode, is considered.

  10. An Aircraft Lifecycle Approach for the Cost-Benefit Analysis of Prognostics and Condition-Based Maintenance-Based on Discrete-Event Simulation

    Science.gov (United States)

    2014-10-02

    PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 2 expected benefits in this case are substitutions of preventive inspection tasks and reductions of...goal is to derive maximum acceptable investment costs for PHM systems from the analysis results. Therefore, no additional fix costs for an airplane ...September 23-28, Brisbane, Australia . Keller, K., & Poblete, J. (2011). The Business Case for SHM, System Health Management: with aerospace

  11. Hematological changes as prognostic indicators of survival: similarities between Gottingen minipigs, humans, and other large animal models.

    Directory of Open Access Journals (Sweden)

    Maria Moroni

    Full Text Available The animal efficacy rule addressing development of drugs for selected disease categories has pointed out the need to develop alternative large animal models. Based on this rule, the pathophysiology of the disease in the animal model must be well characterized and must reflect that in humans. So far, manifestations of the acute radiation syndrome (ARS have been extensively studied only in two large animal models, the non-human primate (NHP and the canine. We are evaluating the suitability of the minipig as an additional large animal model for development of radiation countermeasures. We have previously shown that the Gottingen minipig manifests hematopoietic ARS phases and symptoms similar to those observed in canines, NHPs, and humans.We establish here the LD50/30 dose (radiation dose at which 50% of the animals succumb within 30 days, and show that at this dose the time of nadir and the duration of cytopenia resemble those observed for NHP and canines, and mimic closely the kinetics of blood cell depletion and recovery in human patients with reversible hematopoietic damage (H3 category, METREPOL approach. No signs of GI damage in terms of diarrhea or shortening of villi were observed at doses up to 1.9 Gy. Platelet counts at days 10 and 14, number of days to reach critical platelet values, duration of thrombocytopenia, neutrophil stress response at 3 hours and count at 14 days, and CRP-to-platelet ratio were correlated with survival. The ratios between neutrophils, lymphocytes and platelets were significantly correlated with exposure to irradiation at different time intervals.As a non-rodent animal model, the minipig offers a useful alternative to NHP and canines, with attractive features including ARS resembling human ARS, cost, and regulatory acceptability. Use of the minipig may allow accelerated development of radiation countermeasures.

  12. A novel approach to pipeline tensioner modeling

    Energy Technology Data Exchange (ETDEWEB)

    O' Grady, Robert; Ilie, Daniel; Lane, Michael [MCS Software Division, Galway (Ireland)

    2009-07-01

    As subsea pipeline developments continue to move into deep and ultra-deep water locations, there is an increasing need for the accurate prediction of expected pipeline fatigue life. A significant factor that must be considered as part of this process is the fatigue damage sustained by the pipeline during installation. The magnitude of this installation-related damage is governed by a number of different agents, one of which is the dynamic behavior of the tensioner systems during pipe-laying operations. There are a variety of traditional finite element methods for representing dynamic tensioner behavior. These existing methods, while basic in nature, have been proven to provide adequate forecasts in terms of the dynamic variation in typical installation parameters such as top tension and sagbend/overbend strain. However due to the simplicity of these current approaches, some of them tend to over-estimate the frequency of tensioner pay out/in under dynamic loading. This excessive level of pay out/in motion results in the prediction of additional stress cycles at certain roller beds, which in turn leads to the prediction of unrealistic fatigue damage to the pipeline. This unwarranted fatigue damage then equates to an over-conservative value for the accumulated damage experienced by a pipeline weld during installation, and so leads to a reduction in the estimated fatigue life for the pipeline. This paper describes a novel approach to tensioner modeling which allows for greater control over the velocity of dynamic tensioner pay out/in and so provides a more accurate estimation of fatigue damage experienced by the pipeline during installation. The paper reports on a case study, as outlined in the proceeding section, in which a comparison is made between results from this new tensioner model and from a more conventional approach. The comparison considers typical installation parameters as well as an in-depth look at the predicted fatigue damage for the two methods

  13. Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine; Hissel, Daniel

    2016-08-01

    Proton Exchange Membrane Fuel Cell (PEMFC) is considered the most versatile among available fuel cell technologies, which qualify for diverse applications. However, the large-scale industrial deployment of PEMFCs is limited due to their short life span and high exploitation costs. Therefore, ensuring fuel cell service for a long duration is of vital importance, which has led to Prognostics and Health Management of fuel cells. More precisely, prognostics of PEMFC is major area of focus nowadays, which aims at identifying degradation of PEMFC stack at early stages and estimating its Remaining Useful Life (RUL) for life cycle management. This paper presents a data-driven approach for prognostics of PEMFC stack using an ensemble of constraint based Summation Wavelet- Extreme Learning Machine (SW-ELM) models. This development aim at improving the robustness and applicability of prognostics of PEMFC for an online application, with limited learning data. The proposed approach is applied to real data from two different PEMFC stacks and compared with ensembles of well known connectionist algorithms. The results comparison on long-term prognostics of both PEMFC stacks validates our proposition.

  14. 心力衰竭预后评估模型与评价%Different prognostic models of heart failure and their evaluation

    Institute of Scientific and Technical Information of China (English)

    黄樱硕; 孙颖

    2013-01-01

    Heart failure is the end-stage of various cardiomyopathy, and is of characteristics with difficult therapeutic options, poor prognosis and very high mortality. Many studies reported a wide variety of risk factors associated to heart failure. So, clinical physicians have to face some important decisions, such as, optimal therapeutic option, left ventricular assisted devices, and priority groups for heart transplantation, and prognosis and death risk. Though prognostic models of heart failure are helpful to assess prognosis, different models have their own characteristics, and are suitable for different population. This article introduced several prognostic models of heart failure.%心力衰竭是各种器质性心脏病的终末阶段,其治疗难度大、预后差、死亡率高。已有很多研究分析了心力衰竭的危险因素。选择正确的治疗措施,识别左室辅助装置及心脏移植的优先人群,正确判断预后及死亡风险,是临床医师面临的重要问题。心力衰竭的风险预后模型有助于评估预后,但现有模型各有特点,适用于不同人群。本文分别介绍心力衰竭的几种预后评估模型。

  15. Stochastic model updating utilizing Bayesian approach and Gaussian process model

    Science.gov (United States)

    Wan, Hua-Ping; Ren, Wei-Xin

    2016-03-01

    Stochastic model updating (SMU) has been increasingly applied in quantifying structural parameter uncertainty from responses variability. SMU for parameter uncertainty quantification refers to the problem of inverse uncertainty quantification (IUQ), which is a nontrivial task. Inverse problem solved with optimization usually brings about the issues of gradient computation, ill-conditionedness, and non-uniqueness. Moreover, the uncertainty present in response makes the inverse problem more complicated. In this study, Bayesian approach is adopted in SMU for parameter uncertainty quantification. The prominent strength of Bayesian approach for IUQ problem is that it solves IUQ problem in a straightforward manner, which enables it to avoid the previous issues. However, when applied to engineering structures that are modeled with a high-resolution finite element model (FEM), Bayesian approach is still computationally expensive since the commonly used Markov chain Monte Carlo (MCMC) method for Bayesian inference requires a large number of model runs to guarantee the convergence. Herein we reduce computational cost in two aspects. On the one hand, the fast-running Gaussian process model (GPM) is utilized to approximate the time-consuming high-resolution FEM. On the other hand, the advanced MCMC method using delayed rejection adaptive Metropolis (DRAM) algorithm that incorporates local adaptive strategy with global adaptive strategy is employed for Bayesian inference. In addition, we propose the use of the powerful variance-based global sensitivity analysis (GSA) in parameter selection to exclude non-influential parameters from calibration parameters, which yields a reduced-order model and thus further alleviates the computational burden. A simulated aluminum plate and a real-world complex cable-stayed pedestrian bridge are presented to illustrate the proposed framework and verify its feasibility.

  16. A new approach to modeling aviation accidents

    Science.gov (United States)

    Rao, Arjun Harsha

    views aviation accidents as a set of hazardous states of a system (pilot and aircraft), and triggers that cause the system to move between hazardous states. I used the NTSB's accident coding manual (that contains nearly 4000 different codes) to develop a "dictionary" of hazardous states, triggers, and information codes. Then, I created the "grammar", or a set of rules, that: (1) orders the hazardous states in each accident; and, (2) links the hazardous states using the appropriate triggers. This approach: (1) provides a more correct count of the causes for accidents in the NTSB database; and, (2) checks for gaps or omissions in NTSB accident data, and fills in some of these gaps using logic-based rules. These rules also help identify and count causes for accidents that were not discernable from previous analyses of historical accident data. I apply the model to 6200 helicopter accidents that occurred in the US between 1982 and 2015. First, I identify the states and triggers that are most likely to be associated with fatal and non-fatal accidents. The results suggest that non-fatal accidents, which account for approximately 84% of the accidents, provide valuable opportunities to learn about the causes for accidents. Next, I investigate the causes of inflight loss of control using both a conventional approach and using the state-based approach. The conventional analysis provides little insight into the causal mechanism for LOC. For instance, the top cause of LOC is "aircraft control/directional control not maintained", which does not provide any insight. In contrast, the state-based analysis showed that pilots' tendency to clip objects frequently triggered LOC (16.7% of LOC accidents)--this finding was not directly discernable from conventional analyses. Finally, I investigate the causes for improper autorotations using both a conventional approach and the state-based approach. The conventional approach uses modifiers (e.g., "improper", "misjudged") associated with "24520

  17. Transport modeling: An artificial immune system approach

    Directory of Open Access Journals (Sweden)

    Teodorović Dušan

    2006-01-01

    Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

  18. Approaches and models of intercultural education

    Directory of Open Access Journals (Sweden)

    Iván Manuel Sánchez Fontalvo

    2013-10-01

    Full Text Available Needed to be aware of the need to build an intercultural society, awareness must be assumed in all social spheres, where stands the role play education. A role of transcendental, since it must promote educational spaces to form people with virtues and powers that allow them to live together / as in multicultural contexts and social diversities (sometimes uneven in an increasingly globalized and interconnected world, and foster the development of feelings of civic belonging shared before the neighborhood, city, region and country, allowing them concern and critical judgement to marginalization, poverty, misery and inequitable distribution of wealth, causes of structural violence, but at the same time, wanting to work for the welfare and transformation of these scenarios. Since these budgets, it is important to know the approaches and models of intercultural education that have been developed so far, analysing their impact on the contexts educational where apply.   

  19. Prediction of outcome after moderate and severe traumatic brain injury: External validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation after Significant Head injury (CRASH) prognostic models

    NARCIS (Netherlands)

    B. Roozenbeek (Bob); H.F. Lingsma (Hester); F.E. Lecky (Fiona); J. Lu (Juan); J. Weir (James); I. Butcher (Isabella); G.S. McHugh (Gillian); G.D. Murray (Gordon); P. Perel (Pablo); A.I.R. Maas (Andrew); E.W. Steyerberg (Ewout)

    2012-01-01

    textabstractObjective: The International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation After Significant Head injury prognostic models predict outcome after traumatic brain injury but have not been compared in large datasets. The objective of this is study is to

  20. Validation and Extension of the Prolonged Mechanical Ventilation Prognostic Model (ProVent) Score for Predicting 1-Year Mortality after Prolonged Mechanical Ventilation.

    Science.gov (United States)

    Udeh, Chiedozie I; Hadder, Brent; Udeh, Belinda L

    2015-12-01

    Prognostic models can inform management decisions for patients requiring prolonged mechanical ventilation. The Prolonged Mechanical Ventilation Prognostic model (ProVent) score was developed to predict 1-year mortality in these patients. External evaluation of such models is needed before they are adopted for routine use. The goal was to perform an independent external validation of the modified ProVent score and assess for spectrum extension at 14 days of mechanical ventilation. This was a retrospective cohort analysis of patients who received prolonged mechanical ventilation at the University of Iowa Hospitals. Patients who received 14 or more days of mechanical ventilation were identified from a database. Manual review of their medical records was performed to abstract relevant data including the four model variables at Days 14 and 21 of mechanical ventilation. Vital status at 1 year was checked in the medical records or the social security death index. Logistic regressions examined the associations between the different variables and mortality. Model performance at 14 to 20 days and 21+ days was assessed for discrimination by calculating the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. A total of 180 patients (21+ d) and 218 patients (14-20 d) were included. Overall, 75% were surgical patients. One-year mortality was 51% for 21+ days and 32% for 14 to 20 days of mechanical ventilation. Age greater than 65 years was the strongest predictor of mortality at 1 year in all cohorts. There was no significant difference between predicted and observed mortality rates for patients stratified by ProVent score. There was near-perfect specificity for mortality in the groups with higher ProVent scores. Areas under the curve were 0.69 and 0.75 for the 21+ days and the 14 to 20 days cohorts respectively. P values for the Hosmer-Lemeshow statistics were 0.24 for 21+ days and 0.22 for 14 to

  1. Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics

    Science.gov (United States)

    Kulkarni, Chetan S.; Celaya, Jose Ramon; Biswas, Gautam; Goebel, Kai

    2012-01-01

    This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.

  2. Modelling Approach In Islamic Architectural Designs

    Directory of Open Access Journals (Sweden)

    Suhaimi Salleh

    2014-06-01

    Full Text Available Architectural designs contribute as one of the main factors that should be considered in minimizing negative impacts in planning and structural development in buildings such as in mosques. In this paper, the ergonomics perspective is revisited which hence focuses on the conditional factors involving organisational, psychological, social and population as a whole. This paper tries to highlight the functional and architectural integration with ecstatic elements in the form of decorative and ornamental outlay as well as incorporating the building structure such as wall, domes and gates. This paper further focuses the mathematical aspects of the architectural designs such as polar equations and the golden ratio. These designs are modelled into mathematical equations of various forms, while the golden ratio in mosque is verified using two techniques namely, the geometric construction and the numerical method. The exemplary designs are taken from theSabah Bandaraya Mosque in Likas, Kota Kinabalu and the Sarawak State Mosque in Kuching,while the Universiti Malaysia Sabah Mosque is used for the Golden Ratio. Results show thatIslamic architectural buildings and designs have long had mathematical concepts and techniques underlying its foundation, hence, a modelling approach is needed to rejuvenate these Islamic designs.

  3. Prognostics of Power MOSFET

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and...

  4. A prognostic model for platinum-doublet as second-line chemotherapy in advanced non-small-cell lung cancer patients.

    Science.gov (United States)

    Mo, Hongnan; Hao, Xuezhi; Liu, Yutao; Wang, Lin; Hu, Xingsheng; Xu, Jianping; Yang, Sheng; Xing, Puyuan; Shi, Youwu; Jia, Bo; Wang, Yan; Li, Junling; Wang, Hongyu; Wang, Ziping; Sun, Yan; Shi, Yuankai

    2016-06-01

    Poor prognosis of advanced non-small-cell lung cancer (NSCLC) patients and the promising therapeutic effect of platinum urge the oncologists to evaluate the role of platinum doublet as second-line chemotherapy and establish the definition of platinum sensitivity in NSCLC. We retrospectively analyzed 364 advanced NSCLC patients who received platinum-doublet regimens as second-line chemotherapy after platinum-based first-line treatment. Patients were divided into four groups by their time-to-progression (TTP) after first-line chemotherapy: 0-3, 4-6, 7-12, and >12-month group, respectively. Treatment efficacy of patients' overall survival (OS), progression-free survival (PFS), and response rate (RR), as well as treatment-related toxicity, were compared among the four groups. A prognosis score system and a nomogram were established by Cox proportional hazard model, and validated by concordance index (c-index). Median OS was 14.0, 16.0, 20.0, 25.0 months for patients in the 0-3, 4-6, 7-12, >12-month group, respectively. Age ≤60 years (P = 0.002), female (P = 0.019), and TTP>12 months (P = 0.003) were independent prognostic factors. Prognostic score was calculated by adding 1 point each for any of the above three indicators, with a c-index of 0.590 (95% confidential interval [CI], 0.552-0.627). Median OS were equal to 25.0, 16.0, and 11.0 months for best (2-3 points), intermediate (1 point) and worst (0 point) category, respectively (P chemotherapy in advanced NSCLCpatients. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  5. Palliative medicine review: prognostication.

    Science.gov (United States)

    Glare, Paul A; Sinclair, Christian T

    2008-01-01

    Prognostication, along with diagnosis and treatment, is a traditional core clinical skill of the physician. Many patients and families receiving palliative care want information about life expectancy to help plan realistically for their futures. Although underappreciated, prognosis is, or at least should be, part of every clinical decision. Despite this crucial role, expertise in the art and science of prognostication diminished during the twentieth century, due largely to the ascendancy of accurate diagnostic tests and effective therapies. Consequently, "Doctor, how long do I have?" is a question most physicians find unprepared to answer effectively. As we focus on palliative care in the twenty-first century, prognostication will need to be restored as a core clinical proficiency. The discipline of palliative medicine can provide leadership in this direction. This paper begins by discussing a framework for understanding prognosis and how its different domains might be applied to all patients with life limiting illness, although the main focus of the paper is predicting survival in patients with cancer. Examples of prognostic tools are provided, although the subjective assessment of prognosis remains important in the terminally ill. Other issues addressed include: the importance of prognostication in terms of clinical decision-making, discharge planning, and care planning; the impact of prognosis on hospice referrals and patient/family satisfaction; and physicians' willingness to prognosticate.

  6. Prognostic Model for Resected Squamous-Cell Lung Cancer: External Multicenter Validation and Propensity Score Analysis exploring the Impact of Adjuvant and Neoadjuvant Treatment.

    Science.gov (United States)

    Pilotto, Sara; Sperduti, Isabella; Leuzzi, Giovanni; Chiappetta, Marco; Mucilli, Felice; Ratto, Giovanni Battista; Lococo, Filippo; Filosso, Pierluigi; Spaggiari, Lorenzo; Novello, Silvia; Milella, Michele; Santo, Antonio; Scarpa, Aldo; Infante, Maurizio; Tortora, Giampaolo; Facciolo, Francesco; Bria, Emilio

    2017-12-18

    We developed one of the first clinicopathological prognostic nomograms for resected squamous cell lung cancer (SQLC). Herein, we validate the model in a larger multicenter cohort and we explore the impact of adjuvant/neoadjuvant treatment (ANT). Resected SQLC patients from January 2002 to December 2012 in six institutions were eligible. To each patient was assigned a prognostic score based on those clinicopathological factors included in the model (age, T-descriptor according to TNM 7th edition, lymph nodes, grading). Kaplan-Meier analysis for disease-free/cancer-specific/overall survival (DFS/CSS/OS) was performed according to three-class risk model. Harrell's C-statistics were adopted for model validation. The effect of ANT was adjusted with propensity score (PS). Data from 1,375 patients was gathered (median age: 68 years; male: 86.8%; T-descriptor 1-2/3-4: 71.7%/24.9%; nodes negative/positive: 53.4%/46.6%; grading 1-2/3: 35.0%/41.1%). Data for survival analysis was available for 1,097 patients. With a median follow-up of 55 months, patients at low risk had a significantly longer DFS versus intermediate (HR 1.67, 95% CI 1.40-2.01) and high risk (HR 2.46, 95% CI 1.90-3.19), as well as for CSS (HR 2.46, 95% CI 1.80-3.36; HR 4.30, 95% CI 2.92-6.33) and OS (HR 1.79, 95% CI 1.48-2.17; HR 2.33, 95% CI 1.76-3.07). A trend in favor of ANT was observed for intermediate/high risk patients, particularly for CSS (p=0.06; 5-year CSS 72.7% versus 60.8%). A model based on a combination of easily available clinicopathological factors effectively stratifies resected SQLC patients in three-risk classes. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  7. Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

    Science.gov (United States)

    Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

    2013-01-01

    This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

  8. Risk communication: a mental models approach

    National Research Council Canada - National Science Library

    Morgan, M. Granger (Millett Granger)

    2002-01-01

    ... information about risks. The procedure uses approaches from risk and decision analysis to identify the most relevant information; it also uses approaches from psychology and communication theory to ensure that its message is understood. This book is written in nontechnical terms, designed to make the approach feasible for anyone willing to try it. It is illustrat...

  9. Implementing targeted expectant management in fertility care using prognostic modelling: a cluster randomized trial with a multifaceted strategy.

    Science.gov (United States)

    Kersten, F A M; Nelen, W L D M; van den Boogaard, N M; van Rumste, M M; Koks, C A; IntHout, J; Verhoeve, H R; Pelinck, M J; Boks, D E S; Gianotten, J; Broekmans, F J M; Goddijn, M; Braat, D D M; Mol, B W J; Hermens, R P G M

    2017-08-01

    What is the effectiveness of a multifaceted implementation strategy compared to usual care on improving the adherence to guideline recommendations on expectant management for couples with unexplained infertility? The multifaceted implementation strategy did not significantly increase adherence to guideline recommendations on expectant management compared to care as usual. Intrauterine insemination (IUI) with or without ovarian hyperstimulation has no beneficial effect compared to no treatment for 6 months after the fertility work-up for couples with unexplained infertility and a good prognosis of natural conception. Therefore, various professionals and policy makers have advocated the use of prognostic profiles and expectant management in guideline recommendations. A cluster randomized controlled trial in 25 clinics in the Netherlands was conducted between March 2013 and May 2014. Clinics were randomized between the implementation strategy (intervention, n = 13) and care as usual (control, n = 12). The effect of the implementation strategy was evaluated by comparing baseline and effect measurement data. Data collection was retrospective and obtained from medical record research and a patient questionnaire. A total of 544 couples were included at baseline and 485 at the effect measurement (247 intervention group/238 control group). Guideline adherence increased from 49 to 69% (OR 2.66; 95% CI 1.45-4.89) in the intervention group, and from 49 to 61% (OR 2.03; 95% CI 1.38-3.00) in the control group. Multilevel analysis with case-mix adjustment showed that the difference of 8% was not statistically significant (OR 1.31; 95% CI 0.67-2.59). The ongoing pregnancy rate within six months after fertility work-up did not significantly differ between intervention and control group (25% versus 27%: OR 0.72; 95% CI 0.40-1.27). There is a possible selection bias, couples included in the study had a higher socio-economic status than non-responders. How this affects guideline

  10. Genome-scale modeling of human metabolism - a systems biology approach.

    Science.gov (United States)

    Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

    2013-09-01

    Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models - one of the fundamental aspects of systems biology - have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer.

    Science.gov (United States)

    Saha, Monjoy; Chakraborty, Chandan; Arun, Indu; Ahmed, Rosina; Chatterjee, Sanjoy

    2017-06-12

    Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists' manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.

  12. Prognostic value of blood-biomarkers related to hypoxia, inflammation, immune response and tumour load in non-small cell lung cancer - A survival model with external validation.

    Science.gov (United States)

    Carvalho, Sara; Troost, Esther G C; Bons, Judith; Menheere, Paul; Lambin, Philippe; Oberije, Cary

    2016-06-01

    Improve the prognostic prediction of clinical variables for non-small cell lung cancer (NSCLC), by selecting from blood-biomarkers, non-invasively describing hypoxia, inflammation and tumour load. Model development and validation included 182 and 181 inoperable stage I-IIIB NSCLC patients treated radically with radiotherapy (55.2%) or chemo-radiotherapy (44.8%). Least absolute shrinkage and selection operator (LASSO), selected from blood-biomarkers related to hypoxia [osteopontin (OPN) and carbonic anhydrase IX (CA-IX)], inflammation [interleukin-6 (IL-6), IL-8, and C-reactive protein (CRP)], and tumour load [carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1 (Cyfra 21-1)]. Sequent model extension selected from alpha-2-macroglobulin (α2M), serum interleukin-2 receptor (sIL2r), toll-like receptor 4 (TLR4), and vascular endothelial growth factor (VEGF). Discrimination was reported by concordance-index. OPN and Cyfra 21-1 (hazard ratios of 3.3 and 1.7) significantly improved a clinical model comprising gender, World Health Organization performance-status, forced expiratory volume in 1s, number of positive lymph node stations, and gross tumour volume, from a concordance-index of 0.66 to 0.70 (validation=0.62 and 0.66). Extension of the validated model yielded a concordance-index of 0.67, including α2M, sIL2r and VEGF (hazard ratios of 4.6, 3.1, and 1.4). Improvement of a clinical model including hypoxia and tumour load blood-biomarkers was validated. New immunological markers were associated with overall survival. Data and models can be found at www.cancerdata.org (http://dx.doi.org/10.17195/candat.2016.04.1) and www.predictcancer.org. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  13. Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies

    Data.gov (United States)

    National Aeronautics and Space Administration — An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor...

  14. Prognostics Of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven And Model-Based Methodologies

    Data.gov (United States)

    National Aeronautics and Space Administration — An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor...

  15. Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

    Data.gov (United States)

    National Aeronautics and Space Administration — A remaining useful life prediction methodology for elec- trolytic capacitors is presented. This methodology adopts a Kalman filter approach in conjunction with an...

  16. Matrix approach to modelling of SAR signals

    NARCIS (Netherlands)

    Lidicky, L.; Hoogeboom, P.

    2005-01-01

    The paper presents a matrix approach to implementation of SAR signal generating and processing schemes. This approach is advantageous when matrix oriented software such as Matlab is used. Algorithms written in this type of software packages run faster compared to the same algorithms written for the

  17. Integrated Diagnostics and Prognostics of Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Kam W. Ng

    1999-01-01

    Full Text Available This paper provides an overview of current research efforts in integrated diagnostics and prognostics of rotating machinery. Specifically, the following topics are discussed: sensing techniques and sensors; signal detection, identification and extraction; classification of faults; predictive failure models; data/model fusion; information management; and man–machine interface. Technical issues, recommendations, and future research directions are also addressed.

  18. Mathematical Modelling Approach in Mathematics Education

    Science.gov (United States)

    Arseven, Ayla

    2015-01-01

    The topic of models and modeling has come to be important for science and mathematics education in recent years. The topic of "Modeling" topic is especially important for examinations such as PISA which is conducted at an international level and measures a student's success in mathematics. Mathematical modeling can be defined as using…

  19. Data Assimilation using an Ensemble of Models: A hierarchical approach

    OpenAIRE

    Rayner, Peter

    2017-01-01

    One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case then applies it to the TRANSCO...

  20. Uncertainty in biology a computational modeling approach

    CERN Document Server

    Gomez-Cabrero, David

    2016-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate stude...

  1. Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Precursor parameters have been identified to enable development of a prognostic approach for insulated gate bipolar transistors (IGBT). The IGBT were subjected to...

  2. Modeling composting kinetics: A review of approaches

    NARCIS (Netherlands)

    Hamelers, H.V.M.

    2004-01-01

    Composting kinetics modeling is necessary to design and operate composting facilities that comply with strict market demands and tight environmental legislation. Current composting kinetics modeling can be characterized as inductive, i.e. the data are the starting point of the modeling process and

  3. Numerical modelling approach for mine backfill

    Indian Academy of Sciences (India)

    ... of mine backfill material needs special attention as the numerical model must behave realistically and in accordance with the site conditions. This paper discusses a numerical modelling strategy for modelling mine backfill material. Themodelling strategy is studied using a case study mine from Canadian mining industry.

  4. Evaluating survival model performance: a graphical approach.

    Science.gov (United States)

    Mandel, M; Galai, N; Simchen, E

    2005-06-30

    In the last decade, many statistics have been suggested to evaluate the performance of survival models. These statistics evaluate the overall performance of a model ignoring possible variability in performance over time. Using an extension of measures used in binary regression, we propose a graphical method to depict the performance of a survival model over time. The method provides estimates of performance at specific time points and can be used as an informal test for detecting time varying effects of covariates in the Cox model framework. The method is illustrated on real and simulated data using Cox proportional hazard model and rank statistics. Copyright 2005 John Wiley & Sons, Ltd.

  5. Delta model for end-stage liver disease and delta clinical prognostic indicator as predictors of mortality in patients with viral acute liver failure.

    Science.gov (United States)

    Pannu, Ashok Kumar; Bhalla, Ashish; Rao, Chelapati; Singh, Charanpreet

    2017-01-01

    The objective of the study is to compare the model for end-stage liver disease (MELD) with clinical prognostic indicators (CPI) specifically the change in these parameters after 48 h of admission in predicting the mortality in patients with acute liver failure (ALF) due to acute viral hepatitis. An open label, investigator-initiated prospective study was conducted that included 41 patients with acute viral hepatitis with ALF. The cases were followed prospectively till death or discharge. The MELD and CPI were calculated at admission and 48 h of admission. Patients having no change or worsening in CPI score, i.e., delta CPI more negative had a higher mortality over the next 48 h compared to patients having an improvement in their respective CPI score. Delta CPI predicted adverse outcome better than the presence of any three CPI on admission (P = 0.019). Patients having no change or a worsening in MELD score, i.e., delta MELD more negative, had a higher mortality in the next 48 h compared to the patients having improvement in their respective MELD score. However, MELD >33 on admission was superior to delta MELD in predicting the adverse outcome (P = 0.019). Among the patients with ALF due to viral hepatitis, delta CPI was found to be superior to delta MELD in predicting the adverse outcome in patients with viral ALF (P < 0.0001).

  6. The TETRAD Approach to Model Respecification.

    Science.gov (United States)

    Ting, K F

    1998-01-01

    The TETRAD project revives the tetrad analysis developed almost a century ago. Vanishing tetrads are overidentifying restrictions implied by the structure of a model. As such, it is possible to examine a model empirically by these constraints. Scheines, Spirtes, Glymour, Meek, & Richardson (1998) advocate using vanishing tetrads as a tool for automatic model searches. Despite the search algorithm proving to be superior to those from LISREL and EQS in an earlier report, it is argued that TETRAD II, the search program, is still a datamining procedure. It is important that substantive justifications should be given before, not after, a model is selected. This is impossible with any type of automatic, procedure for specification search. Researchers should take an active role in formulating alternative ' models rather than looking for a quick fix. Finally, the tetrad test developed by Bollen and Ting (1993) is discussed with its application for testing competing models or their components formulated in I advance.

  7. Towards A Model-Based Prognostics Methodology For Electrolytic Capacitors: A Case Study Based On Electrical Overstress Accelerated Aging

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a model-driven methodology for predict- ing the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter...

  8. A prognostic model for development of significant liver fibrosis in HIV-hepatitis C co-infection.

    Directory of Open Access Journals (Sweden)

    Nasheed Moqueet

    Full Text Available Liver fibrosis progresses rapidly in HIV-Hepatitis C virus (HCV co-infected individuals partially due to heightened inflammation. Immune markers targeting stages of fibrogenesis could aid in prognosis of fibrosis.A case-cohort study was nested in the prospective Canadian Co-infection Cohort (n = 1119. HCV RNA positive individuals without fibrosis, end-stage liver disease or chronic Hepatitis B at baseline (n = 679 were eligible. A random subcohort (n = 236 was selected from those eligible. Pro-fibrogenic markers and Interferon Lambda (IFNL rs8099917 genotype were measured from first available sample in all fibrosis cases (APRI ≥ 1.5 during follow-up and the subcohort. We used Cox proportional hazards and compared Model 1 (selected clinical predictors only to Model 2 (Model 1 plus selected markers for predicting 3-year risk of liver fibrosis using weighted Harrell's C and Net Reclassification Improvement indices.113 individuals developed significant liver fibrosis over 1300 person-years (8.63 per 100 person-years 95% CI: 7.08, 10.60. Model 1 (age, sex, current alcohol use, HIV RNA, baseline APRI, HCV genotype was nested in model 2, which also included IFNL genotype and IL-8, sICAM-1, RANTES, hsCRP, and sCD14. The C indexes (95% CI for model 1 vs. model 2 were 0.720 (0.649, 0.791 and 0.756 (0.688, 0.825, respectively. Model 2 classified risk more appropriately (overall net reclassification improvement, p<0.05.Including IFNL genotype and inflammatory markers IL-8, sICAM-1, RANTES, hs-CRP, and sCD14 enabled better prediction of the 3-year risk of significant liver fibrosis over clinical predictors alone. Whether this modest improvement in prediction justifies their additional cost requires further cost-benefit analyses.

  9. Prognostic factors in oligodendrogliomas

    DEFF Research Database (Denmark)

    Westergaard, L; Gjerris, F; Klinken, L

    1997-01-01

    An outcome analysis was performed on 96 patients with pure cerebral oligodendrogliomas operated in the 30-year period 1962 to 1991. The most important predictive prognostic factors were youth and no neurological deficit, demonstrated as a median survival for the group younger than 20 years of 17...

  10. DIVERSE APPROACHES TO MODELLING THE ASSIMILATIVE ...

    African Journals Online (AJOL)

    This study evaluated the assimilative capacity of Ikpoba River using different approaches namely: homogeneous differential equation, ANOVA/Duncan Multiple rage test, first and second order differential equations, correlation analysis, Eigen values and eigenvectors, multiple linear regression, bootstrapping and far-field ...

  11. A visual approach for modeling spatiotemporal relations

    NARCIS (Netherlands)

    R.L. Guimarães (Rodrigo); C.S.S. Neto; L.F.G. Soares

    2008-01-01

    htmlabstractTextual programming languages have proven to be difficult to learn and to use effectively for many people. For this sake, visual tools can be useful to abstract the complexity of such textual languages, minimizing the specification efforts. In this paper we present a visual approach for

  12. A universal approach to solvation modeling.

    Science.gov (United States)

    Cramer, Christopher J; Truhlar, Donald G

    2008-06-01

    Continuum mean-field models that have been carefully designed to address the various electrostatic and nonelectrostatic interactions that develop between a molecule and a surrounding medium are particularly efficient tools for studying the effects of condensed phases on molecular structure, energetics, properties, spectra, interaction potentials, and dynamics. The SM8 model may be combined with density functional theory or Hartree-Fock theory to describe a solute's electronic structure and its self-consistent-field polarization by a solvent. A key feature is the use of class IV charge models to obtain accurate charge distributions (either in the vapor phase or in solution), even when using small basis sets that are affordable for large systems. A second key feature is that nonelectrostatic effects due to cavity formation, dispersion interactions, and changes in solvent structure are included in terms of empirical atomic surface tensions that depend on geometry but do not require atom-type assignments by the user. Use of an analytic surface area algorithm provides very stable energy gradients that allow geometry optimization in solution. The SM8 continuum model, the culmination of a series of SMx models (x = 1-8), permits the modeling of such diverse media as aqueous and organic solvents, soils, lipid bilayers, and air-water interfaces. In addition to predicting accurate transfer free energies between gaseous and condensed phases or between two different condensed phases, SMx models have been useful for predicting the significant influence of condensed phases on processes associated with a change in molecular charge, including acid/base equilibria and oxidation/reduction processes. In this Account, we provide an overview of the algorithms associated with the computation of free energies of solvation in the SM8 model. We also compare the accuracies of the SM8 model with those of other continuum solvation models. Finally, we highlight applications of the SM8 models to

  13. Comparison of two novel approaches to model fibre reinforced concrete

    NARCIS (Netherlands)

    Radtke, F.K.F.; Simone, A.; Sluys, L.J.

    2009-01-01

    We present two approaches to model fibre reinforced concrete. In both approaches, discrete fibre distributions and the behaviour of the fibre-matrix interface are explicitly considered. One approach employs the reaction forces from fibre to matrix while the other is based on the partition of unity

  14. System Behavior Models: A Survey of Approaches

    Science.gov (United States)

    2016-06-01

    and the rulesets that apply. Modeling languages have a vocabulary (components of a model) and grammar (how they relate) (Maier 2009). This grammar, or...modularized) and dealt with individually, reducing the perceived complexity (Pressman 2015). “This separation allows for the locality of different kinds of

  15. A mixed modeling approach to predict the effect of environmental modification on species distributions.

    Directory of Open Access Journals (Sweden)

    Francesco Cozzoli

    Full Text Available Human infrastructures can modify ecosystems, thereby affecting the occurrence and spatial distribution of organisms, as well as ecosystem functionality. Sustainable development requires the ability to predict responses of species to anthropogenic pressures. We investigated the large scale, long term effect of important human alterations of benthic habitats with an integrated approach combining engineering and ecological modelling. We focused our analysis on the Oosterschelde basin (The Netherlands, which was partially embanked by a storm surge barrier (Oosterscheldekering, 1986. We made use of 1 a prognostic (numerical environmental (hydrodynamic model and 2 a novel application of quantile regression to Species Distribution Modeling (SDM to simulate both the realized and potential (habitat suitability abundance of four macrozoobenthic species: Scoloplos armiger, Peringia ulvae, Cerastoderma edule and Lanice conchilega. The analysis shows that part of the fluctuations in macrozoobenthic biomass stocks during the last decades is related to the effect of the coastal defense infrastructures on the basin morphology and hydrodynamics. The methodological framework we propose is particularly suitable for the analysis of large abundance datasets combined with high-resolution environmental data. Our analysis provides useful information on future changes in ecosystem functionality induced by human activities.

  16. Digital System e-Prognostics for Critical Aircraft Computer Systems Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Impact Technologies, in cooperation with Raytheon, proposes to develop and demonstrate an innovative prognostics approach for aircraft digital electronics. The...

  17. A New Approach for Magneto-Static Hysteresis Behavioral Modeling

    DEFF Research Database (Denmark)

    Astorino, Antonio; Swaminathan, Madhavan; Antonini, Giulio

    2016-01-01

    In this paper, a new behavioral modeling approach for magneto-static hysteresis is presented. Many accurate models are currently available, but none of them seems to be able to correctly reproduce all the possible B-H paths with low computational cost. By contrast, the approach proposed...

  18. Advanced language modeling approaches, case study: Expert search

    NARCIS (Netherlands)

    Hiemstra, Djoerd

    2008-01-01

    This tutorial gives a clear and detailed overview of advanced language modeling approaches and tools, including the use of document priors, translation models, relevance models, parsimonious models and expectation maximization training. Expert search will be used as a case study to explain the

  19. 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.

  20. C-reactive protein and serum amyloid A as early-phase and prognostic indicators of acute radiation exposure in nonhuman primate total-body irradiation model

    Energy Technology Data Exchange (ETDEWEB)

    Ossetrova, N.I., E-mail: ossetrova@afrri.usuhs.mil [Armed Forces Radiobiology Research Institute, 8901 Wisconsin Avenue, Bldg. 42, Bethesda, MD 20889-5603 (United States); Sandgren, D.J.; Blakely, W.F. [Armed Forces Radiobiology Research Institute, 8901 Wisconsin Avenue, Bldg. 42, Bethesda, MD 20889-5603 (United States)

    2011-09-15

    Terrorist radiological attacks or nuclear accidents could expose large numbers of people to ionizing radiation. In mass-casualty radiological incidents early medical-management requires triage tools for first-responders to quantitatively identify individuals exposed to life-threatening radiation doses and for early initiation (i.e., within one day after radiation exposure) of cytokine therapy for treatment of bone marrow acute radiation syndrome. Herein, we present results from 30 rhesus macaques total-body irradiated (TBI) to a broad dose range of 1-8.5 Gy with {sup 60}Co {gamma}-rays (0.55 Gy min{sup -1}) and demonstrate dose- and time-dependent changes in blood of C-reactive protein (CRP), serum amyloid A (SAA), and interleukin 6 (IL-6) measured by enzyme linked immunosorbent assay (ELISA). CRP and SAA dose-response results are consistent with {approx}1 Gy and {approx}0.2 Gy thresholds for photon-exposure at 24 h after TBI, respectively. Highly significant elevations of CRP and SAA (p = 0.00017 and p = 0.0024, respectively) were found in animal plasma at 6 h after all TBI doses suggesting their potential use as early-phase biodosimeters. Results also show that the dynamics and content of CRP and SAA levels reflect the course and severity of the acute radiation sickness (ARS) and may function as prognostic indicators of ARS outcome. These results demonstrate proof-of-concept that these radiation-responsive proteins show promise as a complementary approach to conventional biodosimetry for early assessment of radiation exposures and may also contribute as diagnostic indices in the medical management of radiation accidents.

  1. Challenges and opportunities for integrating lake ecosystem modelling approaches

    Science.gov (United States)

    Mooij, Wolf M.; Trolle, Dennis; Jeppesen, Erik; Arhonditsis, George; Belolipetsky, Pavel V.; Chitamwebwa, Deonatus B.R.; Degermendzhy, Andrey G.; DeAngelis, Donald L.; Domis, Lisette N. De Senerpont; Downing, Andrea S.; Elliott, J. Alex; Ruberto, Carlos Ruberto; Gaedke, Ursula; Genova, Svetlana N.; Gulati, Ramesh D.; Hakanson, Lars; Hamilton, David P.; Hipsey, Matthew R.; Hoen, Jochem 't; Hulsmann, Stephan; Los, F. Hans; Makler-Pick, Vardit; Petzoldt, Thomas; Prokopkin, Igor G.; Rinke, Karsten; Schep, Sebastiaan A.; Tominaga, Koji; Van Dam, Anne A.; Van Nes, Egbert H.; Wells, Scott A.; Janse, Jan H.

    2010-01-01

    A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative

  2. Infectious disease modeling a hybrid system approach

    CERN Document Server

    Liu, Xinzhi

    2017-01-01

    This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.

  3. An Integrated Approach to Modeling Evacuation Behavior

    Science.gov (United States)

    2011-02-01

    A spate of recent hurricanes and other natural disasters have drawn a lot of attention to the evacuation decision of individuals. Here we focus on evacuation models that incorporate two economic phenomena that seem to be increasingly important in exp...

  4. Implementing Ethics Auditing Model: New Approach

    OpenAIRE

    Merle Rihma; Birgy Lorenz; Mari Meel; Anu Leppiman

    2014-01-01

    The aims of this article are to test how does enhanced ethics audit model as a new tool for management in Estonian companies work and to investigate through ethics audit model the hidden ethical risks in information technology which occur in everyday work and may be of harm to stakeholders’ interests. Carrying out ethics audit requires the diversity of research methods. Therefore throughout the research the authors took into account triangulation method. The research was conducted through qu...

  5. Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.

    NARCIS (Netherlands)

    Beretta, L.; Santaniello, A.; Cappiello, F.; Chawla, N.V.; Vonk, M.C.; Carreira, P.E.; Allanore, Y.; Popa-Diaconu, D.A.; Cossu, M.; Bertolotti, F.; Ferraccioli, G.; Mazzone, A.; Scorza, R.

    2010-01-01

    OBJECTIVES: Systemic sclerosis (SSc) is a multiorgan disease with high mortality rates. Several clinical features have been associated with poor survival in different populations of SSc patients, but no clear and reproducible prognostic model to assess individual survival prediction in scleroderma

  6. Utilizing patch and site level greenhouse-gas concentration measurements in tandem with the prognostic model, ecosys

    Science.gov (United States)

    Morin, T. H.; Rey Sanchez, C.; Bohrer, G.; Riley, W. J.; Angle, J.; Mekonnen, Z. A.; Stefanik, K. C.; Wrighton, K. C.

    2016-12-01

    Estimates of wetland greenhouse gas (GHG) budgets currently have large uncertainties. While wetlands are the largest source of natural methane (CH4) emissions worldwide, they are also important carbon dioxide (CO2) sinks. Determining the GHG budget of a wetland is challenging, particularly because wetlands have intrinsically temporally and spatially heterogeneous land cover patterns and complex dynamics of CH4 production and emissions. These issues pose challenges to both measuring and modeling GHG budgets from wetlands. To improve wetland GHG flux predictability, we utilized the ecosys model to predict CH4 fluxes from a natural temperate estuarine wetland in northern Ohio. Multiple patches of terrain (that included Typha spp. and Nelumbo lutea) were represented as separate grid cells in the model. Cells were initialized with measured values but were allowed to dynamically evolve in response to meteorological, hydrological, and thermodynamic conditions. Trace gas surface emissions were predicted as the end result of microbial activity, physical transport, and plant processes. Corresponding to each model gridcell, measurements of dissolved gas concentrations were conducted with pore-water dialysis samplers (peepers). The peeper measurements were taken via a series of tubes, providing an undisturbed observation of the pore water concentrations of in situ dissolved gases along a vertical gradient. Non-steady state chambers and a flux tower provided both patch level and integrated site-level fluxes of CO2 and CH4. New Typha chambers were also developed to enclose entire plants and segregate the plant fluxes from soil/water fluxes. We expect ecosys to predict the seasonal and diurnal fluxes of CH4 from within each land cover type and to resolve where CH4 is generated within the soil column and its transmission mechanisms. We demonstrate the need for detailed information at both the patch and site level when using models to predict whole wetland ecosystem-scale GHG

  7. Physiology-based prognostic modeling of the influence of changes in precipitation on a keystone dryland plant species.

    Science.gov (United States)

    Coe, Kirsten K; Sparks, Jed P

    2014-12-01

    Fluctuations in mean annual precipitation (MAP) will strongly influence the ecology of dryland ecosystems in the future, yet, because individual precipitation events drive growth and resource availability for many dryland organisms, changes in intra-annual precipitation may disproportionately influence future dryland processes. This work examines the hypothesis that intra-annual precipitation changes will drive dryland productivity to a greater extent than changes to MAP. To test this hypothesis, we created a physiology-based model to predict the effects of precipitation change on a widespread biocrust moss that regulates soil structure, water retention, and nutrient cycling in drylands. First, we used the model to examine moss productivity over the next 100 years driven by alterations in MAP by ± 10, 20 and 30%, and changes in intra-annual precipitation (event size and frequency). Productivity increased as a function of MAP, but differed among simulations where intra-annual precipitation was manipulated under constant MAP. Supporting our hypothesis, this demonstrates that, even if MAP does not change, changes in the features of individual precipitation events can strongly influence long-term performance. Second, we used the model to examine 100-year productivity based on projected dryland precipitation from published global and regional models. These simulations predicted 25-63% reductions in productivity and increased moss mortality rates, declines that will likely alter water and nutrient cycling in dryland ecosystems. Intra-annual precipitation in model-based simulations was a stronger predictor of productivity compared to MAP, further supporting our hypothesis, and illustrating that intra-annual precipitation patterns may dominate dryland responses to altered precipitation in a future climate.

  8. "Dispersion modeling approaches for near road | Science ...

    Science.gov (United States)

    Roadway design and roadside barriers can have significant effects on the dispersion of traffic-generated pollutants, especially in the near-road environment. Dispersion models that can accurately simulate these effects are needed to fully assess these impacts for a variety of applications. For example, such models can be useful for evaluating the mitigation potential of roadside barriers in reducing near-road exposures and their associated adverse health effects. Two databases, a tracer field study and a wind tunnel study, provide measurements used in the development and/or validation of algorithms to simulate dispersion in the presence of noise barriers. The tracer field study was performed in Idaho Falls, ID, USA with a 6-m noise barrier and a finite line source in a variety of atmospheric conditions. The second study was performed in the meteorological wind tunnel at the US EPA and simulated line sources at different distances from a model noise barrier to capture the effect on emissions from individual lanes of traffic. In both cases, velocity and concentration measurements characterized the effect of the barrier on dispersion.This paper presents comparisons with the two datasets of the barrier algorithms implemented in two different dispersion models: US EPA’s R-LINE (a research dispersion modelling tool under development by the US EPA’s Office of Research and Development) and CERC’s ADMS model (ADMS-Urban). In R-LINE the physical features reveal

  9. Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists' input to improve patient outcomes. Protocol for an observational study.

    Science.gov (United States)

    Geeson, Cathy; Wei, Li; Franklin, Bryony Dean

    2017-06-14

    Medicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services there is a need to increase efficiency while maintaining patient safety. The aim of this study is to develop a prognostic model, the Medicines Optimisation Assessment Tool (MOAT), which can be used to target patients most in need of pharmacists' input while in hospital. The MOAT will be developed following recommendations of the Prognosis Research Strategy partnership. Using a cohort study we will prospectively include 1500 adult patients from the medical wards of two UK hospitals. Data on medication-related problems (MRPs) experienced by study patients will be collected by pharmacists at the study sites as part of their routine daily clinical assessment of patients. Data on potential risk factors such as polypharmacy, renal impairment and the use of 'high risk' medicines will be collected retrospectively from the information departments at the study sites, laboratory reporting systems and patient medical records. Multivariable logistic regression models will then be used to determine the relationship between potential risk factors and the study outcome of preventable MRPs that are at least moderate in severity. Bootstrapping will be used to adjust the MOAT for optimism, and predictive performance will be assessed using calibration and discrimination. A simplified scoring system will also be developed, which will be assessed for sensitivity and specificity. This study has been approved by the Proportionate Review Service Sub-Committee of the National Health Service Research Ethics Committee Wales REC 7 (16/WA/0016) and the Health Research Authority (project ID 197298). We plan to disseminate the results via presentations at relevant patient/public, professional, academic and scientific meetings and conferences, and will submit findings for publication in peer-reviewed journals. NCT02582463. © Article author(s) (or their employer(s) unless otherwise stated in

  10. A Systems Engineering Approach to Evolution of Physics based Prognostic Health Management of Aging Solid Rocket Motor System Assets (Conference Paper with Briefing Charts)

    Science.gov (United States)

    2017-10-09

    performance expectations. This paper outlines the general systems engineering approach, philosophy , and payoff of creating a PHM system, and illustrates when...systems engineering approach, philosophy , and payoff of creating a PHM system, and illustrates when and why mechanistic approaches are best. The...paper concludes with a discussion of the results obtained from the process on a demonstration system. 1. INTRODUCTION Today’s environment of

  11. Lightweight approach to model traceability in a CASE tool

    Science.gov (United States)

    Vileiniskis, Tomas; Skersys, Tomas; Pavalkis, Saulius; Butleris, Rimantas; Butkiene, Rita

    2017-07-01

    A term "model-driven" is not at all a new buzzword within the ranks of system development community. Nevertheless, the ever increasing complexity of model-driven approaches keeps fueling all kinds of discussions around this paradigm and pushes researchers forward to research and develop new and more effective ways to system development. With the increasing complexity, model traceability, and model management as a whole, becomes indispensable activities of model-driven system development process. The main goal of this paper is to present a conceptual design and implementation of a practical lightweight approach to model traceability in a CASE tool.

  12. Next-generation prognostic assessment for diffuse large B-cell lymphoma

    Science.gov (United States)

    Staton, Ashley D; Kof, Jean L; Chen, Qiushi; Ayer, Turgay; Flowers, Christopher R

    2015-01-01

    Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts. PMID:26289217

  13. The simplified models approach to constraining supersymmetry

    Energy Technology Data Exchange (ETDEWEB)

    Perez, Genessis [Institut fuer Theoretische Physik, Karlsruher Institut fuer Technologie (KIT), Wolfgang-Gaede-Str. 1, 76131 Karlsruhe (Germany); Kulkarni, Suchita [Laboratoire de Physique Subatomique et de Cosmologie, Universite Grenoble Alpes, CNRS IN2P3, 53 Avenue des Martyrs, 38026 Grenoble (France)

    2015-07-01

    The interpretation of the experimental results at the LHC are model dependent, which implies that the searches provide limited constraints on scenarios such as supersymmetry (SUSY). The Simplified Models Spectra (SMS) framework used by ATLAS and CMS collaborations is useful to overcome this limitation. SMS framework involves a small number of parameters (all the properties are reduced to the mass spectrum, the production cross section and the branching ratio) and hence is more generic than presenting results in terms of soft parameters. In our work, the SMS framework was used to test Natural SUSY (NSUSY) scenario. To accomplish this task, two automated tools (SModelS and Fastlim) were used to decompose the NSUSY parameter space in terms of simplified models and confront the theoretical predictions against the experimental results. The achievement of both, just as the strengths and limitations, are here expressed for the NSUSY scenario.

  14. A Model-Based Approach for Distributed User Interfaces

    OpenAIRE

    Melchior, Jérémie; Vanderdonckt, Jean; Van Roy, Peter; 3rd ACM Symposium on Engineering Interactive Computing Systems EICS’2011

    2011-01-01

    This paper describes a model-based approach for designing Distributed User Interfaces (DUIs), i.e., graphical user interfaces that are distributed along the following dimensions: end user, display device, computing platform, and physical environment. The three pillars of this model-based approach are: (i) a Concrete User Interface model for DUIs incorporating the distribution dimensions and expressing any DUI element in a XML-compliant format until the granularity of an individual DUI element...

  15. Multidisciplinary approach to railway pneumatic suspensions: pneumatic pipe modelling

    OpenAIRE

    Docquier, Nicolas; Fisette, Paul; Jeanmart, Hervé; Multibody Dynamics 2007 - ECCOMAS Thematic Conference

    2007-01-01

    On the majority of modern railway vehicles, airspring are used for the secondary suspension, i.e. the suspension located between the bogie frame and the carbody. The airspring is connected with several other pneumatic components such as auxiliary tanks, pipes, valves, etc. Such a system can be analysed in a multidisciplinary approach by coupling a multibody model of the train with a detailed pneumatic model of the suspension. This paper presents and compares various modelling approach for the...

  16. Phytoplankton as Particles - A New Approach to Modeling Algal Blooms

    Science.gov (United States)

    2013-07-01

    ER D C/ EL T R -1 3 -1 3 Civil Works Basic Research Program Phytoplankton as Particles – A New Approach to Modeling Algal Blooms E nv... Phytoplankton as Particles – A New Approach to Modeling Algal Blooms Carl F. Cerco and Mark R. Noel Environmental Laboratory U.S. Army Engineer Research... phytoplankton blooms can be modeled by treating phytoplankton as discrete particles capable of self- induced transport via buoyancy regulation or other

  17. Prospective validation of a prognostic model for respiratory syncytial virus bronchiolitis in late preterm infants: a multicenter birth cohort study.

    Directory of Open Access Journals (Sweden)

    Maarten O Blanken

    Full Text Available OBJECTIVES: This study aimed to update and validate a prediction rule for respiratory syncytial virus (RSV hospitalization in preterm infants 33-35 weeks gestational age (WGA. STUDY DESIGN: The RISK study consisted of 2 multicenter prospective birth cohorts in 41 hospitals. Risk factors were assessed at birth among healthy preterm infants 33-35 WGA. All hospitalizations for respiratory tract infection were screened for proven RSV infection by immunofluorescence or polymerase chain reaction. Multivariate logistic regression analysis was used to update an existing prediction model in the derivation cohort (n = 1,227. In the validation cohort (n = 1,194, predicted versus actual RSV hospitalization rates were compared to determine validity of the model. RESULTS: RSV hospitalization risk in both cohorts was comparable (5.7% versus 4.9%. In the derivation cohort, a prediction rule to determine probability of RSV hospitalization was developed using 4 predictors: family atopy (OR 1.9; 95%CI, 1.1-3.2, birth period (OR 2.6; 1.6-4.2, breastfeeding (OR 1.7; 1.0-2.7 and siblings or daycare attendance (OR 4.7; 1.7-13.1. The model showed good discrimination (c-statistic 0.703; 0.64-0.76, 0.702 after bootstrapping. External validation showed good discrimination and calibration (c-statistic 0.678; 0.61-0.74. CONCLUSIONS: Our prospectively validated prediction rule identifies infants at increased RSV hospitalization risk, who may benefit from targeted preventive interventions. This prediction rule can facilitate country-specific, cost-effective use of RSV prophylaxis in late preterm infants.

  18. Numerical modelling approach for mine backfill

    Indian Academy of Sciences (India)

    Muhammad Zaka Emad

    2017-07-24

    Jul 24, 2017 ... a safety factor of two is sufficient for CRF to withstand dynamic loading from blasting. In a study by Emad et al [10], the effects of different simulated blast loads have been examined through a numerical model parametric study. It has been shown that CRF failure could be initiated by blast vibrations.

  19. Energy and development : A modelling approach

    NARCIS (Netherlands)

    van Ruijven, B.J.|info:eu-repo/dai/nl/304834521

    2008-01-01

    Rapid economic growth of developing countries like India and China implies that these countries become important actors in the global energy system. Examples of this impact are the present day oil shortages and rapidly increasing emissions of greenhouse gases. Global energy models are used explore

  20. INDIVIDUAL BASED MODELLING APPROACH TO THERMAL ...

    Science.gov (United States)

    Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. Changes in river temperature regimes are producing an additional challenge for upstream migrating adult salmon and steelhead, species that are sensitive to absolute and cumulative thermal exposure. Adult salmon populations have been shown to utilize cold water patches along migration routes when mainstem river temperatures exceed thermal optimums. We are employing an individual based model (IBM) to explore the costs and benefits of spatially-distributed cold water refugia for adult migrating salmon. Our model, developed in the HexSim platform, is built around a mechanistic behavioral decision tree that drives individual interactions with their spatially explicit simulated environment. Population-scale responses to dynamic thermal regimes, coupled with other stressors such as disease and harvest, become emergent properties of the spatial IBM. Other model outputs include arrival times, species-specific survival rates, body energetic content, and reproductive fitness levels. Here, we discuss the challenges associated with parameterizing an individual based model of salmon and steelhead in a section of the Columbia River. Many rivers and streams in the Pacific Northwest are currently listed as impaired under the Clean Water Act as a result of high summer water temperatures. Adverse effec

  1. An Efficient Deterministic Approach to Model-based Prediction Uncertainty

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the...

  2. Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach

    Energy Technology Data Exchange (ETDEWEB)

    Liao, James C. [Univ. of California, Los Angeles, CA (United States)

    2016-10-01

    Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.

  3. Integration models: multicultural and liberal approaches confronted

    Science.gov (United States)

    Janicki, Wojciech

    2012-01-01

    European societies have been shaped by their Christian past, upsurge of international migration, democratic rule and liberal tradition rooted in religious tolerance. Boosting globalization processes impose new challenges on European societies, striving to protect their diversity. This struggle is especially clearly visible in case of minorities trying to resist melting into mainstream culture. European countries' legal systems and cultural policies respond to these efforts in many ways. Respecting identity politics-driven group rights seems to be the most common approach, resulting in creation of a multicultural society. However, the outcome of respecting group rights may be remarkably contradictory to both individual rights growing out from liberal tradition, and to reinforced concept of integration of immigrants into host societies. The hereby paper discusses identity politics upturn in the context of both individual rights and integration of European societies.

  4. No prognostic value added by vitamin D pathway SNPs to current prognostic system for melanoma survival.

    Science.gov (United States)

    Luo, Li; Orlow, Irene; Kanetsky, Peter A; Thomas, Nancy E; Fang, Shenying; Lee, Jeffrey E; Berwick, Marianne; Lee, Ji-Hyun

    2017-01-01

    The prognostic improvement attributed to genetic markers over current prognostic system has not been well studied for melanoma. The goal of this study is to evaluate the added prognostic value of Vitamin D Pathway (VitD) SNPs to currently known clinical and demographic factors such as age, sex, Breslow thickness, mitosis and ulceration (CDF). We utilized two large independent well-characterized melanoma studies: the Genes, Environment, and Melanoma (GEM) and MD Anderson studies, and performed variable selection of VitD pathway SNPs and CDF using Random Survival Forest (RSF) method in addition to Cox proportional hazards models. The Harrell's C-index was used to compare the performance of model predictability. The population-based GEM study enrolled 3,578 incident cases of cutaneous melanoma (CM), and the hospital-based MD Anderson study consisted of 1,804 CM patients. Including both VitD SNPs and CDF yielded C-index of 0.85, which provided slight but not significant improvement by CDF alone (C-index = 0.83) in the GEM study. Similar results were observed in the independent MD Anderson study (C-index = 0.84 and 0.83, respectively). The Cox model identified no significant associations after adjusting for multiplicity. Our results do not support clinically significant prognostic improvements attributable to VitD pathway SNPs over current prognostic system for melanoma survival.

  5. No prognostic value added by vitamin D pathway SNPs to current prognostic system for melanoma survival.

    Directory of Open Access Journals (Sweden)

    Li Luo

    Full Text Available The prognostic improvement attributed to genetic markers over current prognostic system has not been well studied for melanoma. The goal of this study is to evaluate the added prognostic value of Vitamin D Pathway (VitD SNPs to currently known clinical and demographic factors such as age, sex, Breslow thickness, mitosis and ulceration (CDF. We utilized two large independent well-characterized melanoma studies: the Genes, Environment, and Melanoma (GEM and MD Anderson studies, and performed variable selection of VitD pathway SNPs and CDF using Random Survival Forest (RSF method in addition to Cox proportional hazards models. The Harrell's C-index was used to compare the performance of model predictability. The population-based GEM study enrolled 3,578 incident cases of cutaneous melanoma (CM, and the hospital-based MD Anderson study consisted of 1,804 CM patients. Including both VitD SNPs and CDF yielded C-index of 0.85, which provided slight but not significant improvement by CDF alone (C-index = 0.83 in the GEM study. Similar results were observed in the independent MD Anderson study (C-index = 0.84 and 0.83, respectively. The Cox model identified no significant associations after adjusting for multiplicity. Our results do not support clinically significant prognostic improvements attributable to VitD pathway SNPs over current prognostic system for melanoma survival.

  6. ISM Approach to Model Offshore Outsourcing Risks

    Directory of Open Access Journals (Sweden)

    Sunand Kumar

    2014-07-01

    Full Text Available In an effort to achieve a competitive advantage via cost reductions and improved market responsiveness, organizations are increasingly employing offshore outsourcing as a major component of their supply chain strategies. But as evident from literature number of risks such as Political risk, Risk due to cultural differences, Compliance and regulatory risk, Opportunistic risk and Organization structural risk, which adversely affect the performance of offshore outsourcing in a supply chain network. This also leads to dissatisfaction among different stake holders. The main objective of this paper is to identify and understand the mutual interaction among various risks which affect the performance of offshore outsourcing.  To this effect, authors have identified various risks through extant review of literature.  From this information, an integrated model using interpretive structural modelling (ISM for risks affecting offshore outsourcing is developed and the structural relationships between these risks are modeled.  Further, MICMAC analysis is done to analyze the driving power and dependency of risks which shall be helpful to managers to identify and classify important criterions and to reveal the direct and indirect effects of each criterion on offshore outsourcing. Results show that political risk and risk due to cultural differences are act as strong drivers.

  7. Different experimental approaches in modelling cataractogenesis

    Science.gov (United States)

    Kyselova, Zuzana

    2010-01-01

    Cataract, the opacification of eye lens, is the leading cause of blindness worldwide. At present, the only remedy is surgical removal of the cataractous lens and substitution with a lens made of synthetic polymers. However, besides significant costs of operation and possible complications, an artificial lens just does not have the overall optical qualities of a normal one. Hence it remains a significant public health problem, and biochemical solutions or pharmacological interventions that will maintain the transparency of the lens are highly required. Naturally, there is a persistent demand for suitable biological models. The ocular lens would appear to be an ideal organ for maintaining culture conditions because of lacking blood vessels and nerves. The lens in vivo obtains its nutrients and eliminates waste products via diffusion with the surrounding fluids. Lens opacification observed in vivo can be mimicked in vitro by addition of the cataractogenic agent sodium selenite (Na2SeO3) to the culture medium. Moreover, since an overdose of sodium selenite induces also cataract in young rats, it became an extremely rapid and convenient model of nuclear cataract in vivo. The main focus of this review will be on selenium (Se) and its salt sodium selenite, their toxicological characteristics and safety data in relevance of modelling cataractogenesis, either under in vivo or in vitro conditions. The studies revealing the mechanisms of lens opacification induced by selenite are highlighted, the representatives from screening for potential anti-cataract agents are listed. PMID:21217865

  8. Walking in circles: a modelling approach.

    Science.gov (United States)

    Maus, Horst-Moritz; Seyfarth, Andre

    2014-10-06

    Blindfolded or disoriented people have the tendency to walk in circles rather than on a straight line even if they wanted to. Here, we use a minimalistic walking model to examine this phenomenon. The bipedal spring-loaded inverted pendulum exhibits asymptotically stable gaits with centre of mass (CoM) dynamics and ground reaction forces similar to human walking in the sagittal plane. We extend this model into three dimensions, and show that stable walking patterns persist if the leg is aligned with respect to the body (here: CoM velocity) instead of a world reference frame. Further, we demonstrate that asymmetric leg configurations, which are common in humans, will typically lead to walking in circles. The diameter of these circles depends strongly on parameter configuration, but is in line with empirical data from human walkers. Simulation results suggest that walking radius and especially direction of rotation are highly dependent on leg configuration and walking velocity, which explains inconsistent veering behaviour in repeated trials in human data. Finally, we discuss the relation between findings in the model and implications for human walking. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  9. SERPINB3 in the chicken model of ovarian cancer: a prognostic factor for platinum resistance and survival in patients with epithelial ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Whasun Lim

    Full Text Available Serine protease inhibitors (SERPINs appear to be ubiquitously expressed in a variety of species and play important roles in pivotal physiological processes such as angiogenesis, immune responses, blood coagulation and fibronolysis. Of these, squamous cell carcinoma antigen 1 (SCCA1, also known as a SERPINB3, was first identified in squamous cell carcinoma tissue from the cervix of women. However, there is little known about the SERPINB3 expression in human epithelial ovarian cancer (EOC. Therefore, in the present study, we investigated the functional role of SERPINB3 gene in human EOC using chickens, the most relevant animal model. In 136 chickens, EOC was found in 10 (7.4%. SERPINB3 mRNA was induced in cancerous, but not normal ovaries of chickens (P<0.01, and it was abundant only in the glandular epithelium of cancerous ovaries of chickens. Further, several microRNAs, specifically miR-101, miR-1668 and miR-1681 were discovered to influence SERPINB3 expression via its 3'-UTR which suggests that post-transcriptional regulation influences SERPINB3 expression in chickens. SERPINB3 protein was localized predominantly to the glandular epithelium in cancerous ovaries of chickens, and it was abundant in the nucleus of both chicken and human ovarian cancer cell lines. In 109 human patients with EOC, 15 (13.8%, 66 (60.6% and 28 (25.7% patients showed weak, moderate and strong expression of SERPINB3 protein, respectively. Strong expression of SERPINB3 protein was a prognostic factor for platinum resistance (adjusted OR; odds ratio, 5.94; 95% Confidence Limits, 1.21-29.15, and for poor progression-free survival (PFS; adjusted HR; hazard ratio, 2.07; 95% CI; confidence interval, 1.03-4.41. Therefore, SERPINB3 may play an important role in ovarian carcinogenesis and be a novel biomarker for predicting platinum resistance and a poor prognosis for survival in patients with EOC.

  10. Models of Psi Mediation: A Classical and Quantum Approach

    OpenAIRE

    Kelly, Theresa M.

    2011-01-01

    In this chapter I address both classical and quantum mechanical modeling approaches to psi phenomena including those pertaining to the role of psi phenomena such as the psi-mediated instrumental response (PMIR) and relative need-serving qualities of psi, psychokinesis as a primary psi process, and psi as a product of evolution via Darwinian theory. In addition, I address classical models including electromagnetic models, energy field models, and the zero-point field model. I address the assoc...

  11. Comparison of the prognostic value of Chronic Liver Failure Consortium scores and traditional models for predicting mortality in patients with cirrhosis.

    Science.gov (United States)

    Antunes, Artur Gião; Teixeira, Cristina; Vaz, Ana Margarida; Martins, Cláudio; Queirós, Patrícia; Alves, Ana; Velasco, Francisco; Peixe, Bruno; Oliveira, Ana Paula; Guerreiro, Horácio

    2017-04-01

    Recently, the European Association for the Study of the Liver - Chronic Liver Failure (CLIF) Consortium defined two new prognostic scores, according to the presence or absence of acute-on-chronic liver failure (ACLF): the CLIF Consortium ACLF score (CLIF-C ACLFs) and the CLIF-C Acute Decompensation score (CLIF-C ADs). We sought to compare their accuracy in predicting 30- and 90-day mortality with some of the existing models: Child-Turcotte-Pugh (CTP), Model for End-Stage Liver Disease (MELD), MELD-Na, integrated MELD (iMELD), MELD to serum sodium ratio index (MESO), Refit MELD and Refit MELD-Na. Retrospective cohort study that evaluated all admissions due to decompensated cirrhosis in 2 centers between 2011 and 2014. At admission each score was assessed, and the discrimination ability was compared by measuring the area under the ROC curve (AUROC). A total of 779 hospitalizations were evaluated. Two hundred and twenty-two patients met criteria for ACLF (25.9%). The 30- and 90-day mortality were respectively 17.7 and 37.3%. CLIF-C ACLFs presented an AUROC for predicting 30- and 90-day mortality of 0.684 (95% CI: 0.599-0.770) and 0.666 (95% CI: 0.588-0.744) respectively. No statistically significant differences were found when compared to traditional models. For patients without ACLF, CLIF-C ADs had an AUROC for predicting 30- and 90-day mortality of 0.689 (95% CI: 0.614-0.763) and 0.672 (95% CI: 0.624-0.720) respectively. When compared to other scores, it was only statistically superior to MELD for predicting 30-day mortality (p=0.0296). The new CLIF-C scores were not statistically superior to the traditional models, with the exception of CLIF-C ADs for predicting 30-day mortality. Copyright © 2017 Elsevier España, S.L.U., AEEH y AEG. All rights reserved.

  12. [Development and validation of risk score model for acute myocardial infarction in China: prognostic value thereof for in hospital major adverse cardiac events and evaluation of revascularization].

    Science.gov (United States)

    Wu, Xiao-fan; Lü, Shu-zheng; Chen, Yun-dai; Pan, Wei-qi; Song, Xian-tao; Li, Jing; Liu, Xin; Wang, Xi-zhi; Zhang, Li-jie; Ren, Fang; Luo, Jing-guang

    2008-07-08

    To develop a simple risk score model of in-hospital major adverse cardiac events (MACE) including all-cause mortality, new or recurrent myocardial infarction (MI), and evaluate the efficacy about revascularization on patients with different risk. The basic characteristics, diagnosis, therapy, and in-hospital outcomes of 1512 ACS patients from Global Registry of Acute Coronary Events (GRACE) study of China were collected to develop a risk score model by multivariable stepwise logistic regression. The goodness-of-fit test and discriminative power of the final model were assessed respectively. The best cut-off value for the risk score was used to assess the impact of revascularization for ST-elevation MI (STEMI) and non-ST elevation acute coronary artery syndrome (NSTEACS) on in-hospital outcomes. (1) The following 6 independent risk factors accounted for about 92.5% of the prognostic information: age > or =80 years (4 points), SBP or =90 mm Hg (2 points), Killip II (3 points), Killip III or IV (9 points), cardiac arrest during presentation (4 points), ST-segment elevation (3 points) or depression (5 points) or combination of elevation and depression (4 points) on electrocardiogram at presentation. (2) CHIEF risk model was excellent with Hosmer-Lemeshow goodness-of-fit test of 0.673 and c statistics of 0.776. (3)1301 ACS patients previously enrolled in GRACE study were divided into 2 groups with the best cut-off value of 5.5 points. The impact of revascularization on the in-hospital MACE of the higher risk subsets was stronger than that of the lower risk subsets both in STEMI [OR (95% CI) = 0.32 (0.11, 0.94), chi2 = 5.39, P = 0.02] and NSTEACS [OR (95% CI) = 0.32 (0.06, 0.94), chi2 =4.17, P = 0.04] population. However, both STEMI (61.7% vs. 78.3%, P = 0.000) and NSTEACS (42.0% vs 62.3%, P = 0.000) patients with the risk scores more than 5.5 points had lower revascularization rates. The risk score provides excellent ability to predict in-hospital death or (re) MI

  13. Assessing calibration of prognostic risk scores.

    Science.gov (United States)

    Crowson, Cynthia S; Atkinson, Elizabeth J; Therneau, Terry M

    2016-08-01

    Current methods used to assess calibration are limited, particularly in the assessment of prognostic models. Methods for testing and visualizing calibration (e.g. the Hosmer-Lemeshow test and calibration slope) have been well thought out in the binary regression setting. However, extension of these methods to Cox models is less well known and could be improved. We describe a model-based framework for the assessment of calibration in the binary setting that provides natural extensions to the survival data setting. We show that Poisson regression models can be used to easily assess calibration in prognostic models. In addition, we show that a calibration test suggested for use in survival data has poor performance. Finally, we apply these methods to the problem of external validation of a risk score developed for the general population when assessed in a special patient population (i.e. patients with particular comorbidities, such as rheumatoid arthritis). © The Author(s) 2013.

  14. Agribusiness model approach to territorial food development

    Directory of Open Access Journals (Sweden)

    Murcia Hector Horacio

    2011-04-01

    Full Text Available

    Several research efforts have coordinated the academic program of Agricultural Business Management from the University De La Salle (Bogota D.C., to the design and implementation of a sustainable agribusiness model applied to food development, with territorial projection. Rural development is considered as a process that aims to improve the current capacity and potential of the inhabitant of the sector, which refers not only to production levels and productivity of agricultural items. It takes into account the guidelines of the Organization of the United Nations “Millennium Development Goals” and considered the concept of sustainable food and agriculture development, including food security and nutrition in an integrated interdisciplinary context, with holistic and systemic dimension. Analysis is specified by a model with an emphasis on sustainable agribusiness production chains related to agricultural food items in a specific region. This model was correlated with farm (technical objectives, family (social purposes and community (collective orientations projects. Within this dimension are considered food development concepts and methodologies of Participatory Action Research (PAR. Finally, it addresses the need to link the results to low-income communities, within the concepts of the “new rurality”.

  15. A modular approach to numerical human body modeling

    NARCIS (Netherlands)

    Forbes, P.A.; Griotto, G.; Rooij, L. van

    2007-01-01

    The choice of a human body model for a simulated automotive impact scenario must take into account both accurate model response and computational efficiency as key factors. This study presents a "modular numerical human body modeling" approach which allows the creation of a customized human body

  16. A market model for stochastic smile: a conditional density approach

    NARCIS (Netherlands)

    Zilber, A.

    2005-01-01

    The purpose of this paper is to introduce a new approach that allows to construct no-arbitrage market models of for implied volatility surfaces (in other words, stochastic smile models). That is to say, the idea presented here allows us to model prices of liquidly traded vanilla options as separate

  17. Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches

    Science.gov (United States)

    Erbas, Ayhan Kürsat; Kertil, Mahmut; Çetinkaya, Bülent; Çakiroglu, Erdinç; Alacaci, Cengiz; Bas, Sinem

    2014-01-01

    Mathematical modeling and its role in mathematics education have been receiving increasing attention in Turkey, as in many other countries. The growing body of literature on this topic reveals a variety of approaches to mathematical modeling and related concepts, along with differing perspectives on the use of mathematical modeling in teaching and…

  18. Multiscale approach to equilibrating model polymer melts

    DEFF Research Database (Denmark)

    Svaneborg, Carsten; Ali Karimi-Varzaneh, Hossein; Hojdis, Nils

    2016-01-01

    to be computationally effective at each scale. Density fluctuations in the melt structure above the tube scale are minimized through a Monte Carlo simulated annealing of a lattice polymer model. Subsequently the melt structure below the tube scale is equilibrated via the Rouse dynamics of a force-capped Kremer...... of 15.000 monomers. To validate the equilibration process we study the time evolution of bulk, collective, and single-chain observables at the monomeric, mesoscopic, and macroscopic length scales. Extension of the present method to longer, branched, or polydisperse chains, and/or larger system sizes...

  19. Applied Regression Modeling A Business Approach

    CERN Document Server

    Pardoe, Iain

    2012-01-01

    An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

  20. Implicit moral evaluations: A multinomial modeling approach.

    Science.gov (United States)

    Cameron, C Daryl; Payne, B Keith; Sinnott-Armstrong, Walter; Scheffer, Julian A; Inzlicht, Michael

    2017-01-01

    Implicit moral evaluations-i.e., immediate, unintentional assessments of the wrongness of actions or persons-play a central role in supporting moral behavior in everyday life. Yet little research has employed methods that rigorously measure individual differences in implicit moral evaluations. In five experiments, we develop a new sequential priming measure-the Moral Categorization Task-and a multinomial model that decomposes judgment on this task into multiple component processes. These include implicit moral evaluations of moral transgression primes (Unintentional Judgment), accurate moral judgments about target actions (Intentional Judgment), and a directional tendency to judge actions as morally wrong (Response Bias). Speeded response deadlines reduced Intentional Judgment but not Unintentional Judgment (Experiment 1). Unintentional Judgment was stronger toward moral transgression primes than non-moral negative primes (Experiments 2-4). Intentional Judgment was associated with increased error-related negativity, a neurophysiological indicator of behavioral control (Experiment 4). Finally, people who voted for an anti-gay marriage amendment had stronger Unintentional Judgment toward gay marriage primes (Experiment 5). Across Experiments 1-4, implicit moral evaluations converged with moral personality: Unintentional Judgment about wrong primes, but not negative primes, was negatively associated with psychopathic tendencies and positively associated with moral identity and guilt proneness. Theoretical and practical applications of formal modeling for moral psychology are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Gene Expression of the EGF System-a Prognostic Model in Non-Small Cell Lung Cancer Patients Without Activating EGFR Mutations

    DEFF Research Database (Denmark)

    Sandfeld-Paulsen, Birgitte; Folkersen, Birgitte Holst; Rasmussen, Torben Riis

    2016-01-01

    OBJECTIVES: Contradicting results have been demonstrated for the expression of the epidermal growth factor receptor (EGFR) as a prognostic marker in non-small cell lung cancer (NSCLC). The complexity of the EGF system with four interacting receptors and more than a dozen activating ligands...

  2. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  3. Modeling Approaches for Describing Microbial Population Heterogeneity

    DEFF Research Database (Denmark)

    Lencastre Fernandes, Rita

    population consists of cells in different states, and it implies a heterogeneous distribution of activities (e.g. respiration, product yield), including different responses to extracellular stimuli. The existence of a heterogeneous cell population may explain the lower productivities obtained......) to predict distributions of certain population properties including particle size, mass or volume, and molecular weight. Similarly, PBM allow for a mathematical description of distributed cell properties within microbial populations. Cell total protein content distributions (a measure of cell mass) have been...... population dynamics, in response to the substrate consumption observed during batch cultivation. Cell size and cell cycle position distributions were used to describe the cell population. A two-stage PBM was developed and coupled to an unstructured model describing the extracellular environment. The good...

  4. Continuous Molecular Fields Approach Applied to Structure-Activity Modeling

    CERN Document Server

    Baskin, Igor I

    2013-01-01

    The Method of Continuous Molecular Fields is a universal approach to predict various properties of chemical compounds, in which molecules are represented by means of continuous fields (such as electrostatic, steric, electron density functions, etc). The essence of the proposed approach consists in performing statistical analysis of functional molecular data by means of joint application of kernel machine learning methods and special kernels which compare molecules by computing overlap integrals of their molecular fields. This approach is an alternative to traditional methods of building 3D structure-activity and structure-property models based on the use of fixed sets of molecular descriptors. The methodology of the approach is described in this chapter, followed by its application to building regression 3D-QSAR models and conducting virtual screening based on one-class classification models. The main directions of the further development of this approach are outlined at the end of the chapter.

  5. Prognostic factors and outcomes for osteosarcoma: an international collaboration

    NARCIS (Netherlands)

    Pakos, Emilios E.; Nearchou, Andreas D.; Grimer, Robert J.; Koumoullis, Haris D.; Abudu, Adesegun; Bramer, Jos A. M.; Jeys, Lee M.; Franchi, Alessandro; Scoccianti, Guido; Campanacci, Domenico; Capanna, Rodolfo; Aparicio, Jorge; Tabone, Marie-Dominique; Holzer, Gerold; Abdolvahab, Fashid; Funovics, Philipp; Dominkus, Martin; Ilhan, Inci; Berrak, Su G.; Patino-Garcia, Ana; Sierrasesumaga, Luis; San-Julian, Mikel; Garraus, Moira; Petrilli, Antonio Sergio; Filho, Reynaldo Jesus Garcia; Macedo, Carla Renata Pacheco Donato; Alves, Maria Teresa de Seixas; Seiwerth, Sven; Nagarajan, Rajaram; Cripe, Timothy P.; Ioannidis, John P. A.

    2009-01-01

    We aimed to evaluate the prognostic significance of traditional clinical predictors in osteosarcoma through an international collaboration of 10 teams of investigators (2680 patients) who participated. In multivariate models the mortality risk increased with older age, presence of metastatic disease

  6. Frontline Therapy for Classical Hodgkin Lymphoma by Stage and Prognostic Factors

    Science.gov (United States)

    Allen, Pamela B; Gordon, Leo I

    2017-01-01

    Hodgkin lymphoma is a highly curable malignancy in early and advanced stages. Most patients are diagnosed in their teens or twenties and are expected to live decades beyond their treatment. Therefore, the toxicity of treatment must be balanced with the goal of cure. Thus, treatment has been refined through prognostic models and positron emission tomography-computed tomography (PET-CT)-directed therapy. Stratification by prognostic models defines groups of patients with favorable characteristics who may be treated with less intensive therapy upfront, including fewer cycles of chemotherapy, lower doses of radiation, or omission of radiation altogether. Alternatively, high-risk patients may be assigned to a more aggressive initial approach. The modern use of interim PET-CT allows further tailoring of treatment by response. PMID:28989291

  7. Pattern-based approach for logical traffic isolation forensic modelling

    CSIR Research Space (South Africa)

    Dlamini, I

    2009-08-01

    Full Text Available The use of design patterns usually changes the approach of software design and makes software development relatively easy. This paper extends work on a forensic model for Logical Traffic Isolation (LTI) based on Differentiated Services (Diff...

  8. Simple queueing approach to segregation dynamics in Schelling model

    OpenAIRE

    Sobkowicz, Pawel

    2007-01-01

    A simple queueing approach for segregation of agents in modified one dimensional Schelling segregation model is presented. The goal is to arrive at simple formula for the number of unhappy agents remaining after the segregation.

  9. A semantic-web approach for modeling computing infrastructures

    NARCIS (Netherlands)

    Ghijsen, M.; van der Ham, J.; Grosso, P.; Dumitru, C.; Zhu, H.; Zhao, Z.; de Laat, C.

    2013-01-01

    This paper describes our approach to modeling computing infrastructures. Our main contribution is the Infrastructure and Network Description Language (INDL) ontology. The aim of INDL is to provide technology independent descriptions of computing infrastructures, including the physical resources as

  10. The Intersystem Model of Psychotherapy: An Integrated Systems Treatment Approach

    Science.gov (United States)

    Weeks, Gerald R.; Cross, Chad L.

    2004-01-01

    This article introduces the intersystem model of psychotherapy and discusses its utility as a truly integrative and comprehensive approach. The foundation of this conceptually complex approach comes from dialectic metatheory; hence, its derivation requires an understanding of both foundational and integrational constructs. The article provides a…

  11. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  12. Towards Translating Graph Transformation Approaches by Model Transformations

    NARCIS (Netherlands)

    Hermann, F.; Kastenberg, H.; Modica, T.; Karsai, G.; Taentzer, G.

    2006-01-01

    Recently, many researchers are working on semantics preserving model transformation. In the field of graph transformation one can think of translating graph grammars written in one approach to a behaviourally equivalent graph grammar in another approach. In this paper we translate graph grammars

  13. Modeling of phase equilibria with CPA using the homomorph approach

    DEFF Research Database (Denmark)

    Breil, Martin Peter; Tsivintzelis, Ioannis; Kontogeorgis, Georgios

    2011-01-01

    For association models, like CPA and SAFT, a classical approach is often used for estimating pure-compound and mixture parameters. According to this approach, the pure-compound parameters are estimated from vapor pressure and liquid density data. Then, the binary interaction parameters, kij, are ...

  14. An Almost Integration-free Approach to Ordered Response Models

    NARCIS (Netherlands)

    van Praag, B.M.S.; Ferrer-i-Carbonell, A.

    2006-01-01

    'In this paper we propose an alternative approach to the estimation of ordered response models. We show that the Probit-method may be replaced by a simple OLS-approach, called P(robit)OLS, without any loss of efficiency. This method can be generalized to the analysis of panel data. For large-scale

  15. Virtuous organization: A structural equation modeling approach

    Directory of Open Access Journals (Sweden)

    Majid Zamahani

    2013-02-01

    Full Text Available For years, the idea of virtue was unfavorable among researchers and virtues were traditionally considered as culture-specific, relativistic and they were supposed to be associated with social conservatism, religious or moral dogmatism, and scientific irrelevance. Virtue and virtuousness have been recently considered seriously among organizational researchers. The proposed study of this paper examines the relationships between leadership, organizational culture, human resource, structure and processes, care for community and virtuous organization. Structural equation modeling is employed to investigate the effects of each variable on other components. The data used in this study consists of questionnaire responses from employees in Payam e Noor University in Yazd province. A total of 250 questionnaires were sent out and a total of 211 valid responses were received. Our results have revealed that all the five variables have positive and significant impacts on virtuous organization. Among the five variables, organizational culture has the most direct impact (0.80 and human resource has the most total impact (0.844 on virtuous organization.

  16. An optimization approach to kinetic model reduction for combustion chemistry

    CERN Document Server

    Lebiedz, Dirk

    2013-01-01

    Model reduction methods are relevant when the computation time of a full convection-diffusion-reaction simulation based on detailed chemical reaction mechanisms is too large. In this article, we review a model reduction approach based on optimization of trajectories and show its applicability to realistic combustion models. As most model reduction methods, it identifies points on a slow invariant manifold based on time scale separation in the dynamics of the reaction system. The numerical approximation of points on the manifold is achieved by solving a semi-infinite optimization problem, where the dynamics enter the problem as constraints. The proof of existence of a solution for an arbitrarily chosen dimension of the reduced model (slow manifold) is extended to the case of realistic combustion models including thermochemistry by considering the properties of proper maps. The model reduction approach is finally applied to three models based on realistic reaction mechanisms: 1. ozone decomposition as a small t...

  17. Development of a Conservative Model Validation Approach for Reliable Analysis

    Science.gov (United States)

    2015-01-01

    validation approach and extended it to solve a multivariate output problem [6]. Arendt et al. applied the Bayesian approach for both single and...32), pp. 2431-2441. [7] Arendt , P., Apley, D., and Chen, W., 2012, "Quantification of Model Uncertainty: Calibration, Model Discrepancy, and...Identifiability," Journal of Mechanical Design, 134(10). [8] Arendt , P., Apley, D., Chen, W., Lamb, D., and Gorsich, D., 2012, "Improving Identifiability

  18. A mechanistic approach to modeling respiratory sensitization.

    Science.gov (United States)

    Mekenyan, Ovanes; Patlewicz, Grace; Kuseva, Chanita; Popova, Ioanna; Mehmed, Aycel; Kotov, Stefan; Zhechev, Teodor; Pavlov, Todor; Temelkov, Stanislav; Roberts, David W

    2014-02-17

    Chemical respiratory sensitization is an important occupational health problem which may lead to severely incapacitated human health, yet there are currently no validated or widely accepted models for identifying and characterizing the potential of a chemical to induce respiratory sensitization. This is in part due to the ongoing uncertainty about the immunological mechanisms through which respiratory sensitization may be acquired. Despite the lack of test method, regulations such as REACH still require an assessment of respiratory sensitization for risk assessment and/or for the purposes of classification and labeling. The REACH guidance describes an integrated evaluation strategy to characterize what information sources could be available to facilitate such an assessment. The components of this include a consideration of well-established structural alerts and existing data (whether it be derived from read-across, (quantitative) structure-activity relationships ((Q)SAR), in vivo studies etc.). There has been some progress in developing SARs as well as a handful of empirical QSARs. More recently, efforts have been focused on exploring whether the reaction chemistry mechanistic domains first characterized for skin sensitization are relevant for respiratory sensitization and to what extent modifications or refinements are needed to rationalize the differences between the two end points as far as their chemistry is concerned. This study has built upon the adverse outcome pathway (AOP) for skin sensitization that was developed and published by the OECD in 2012. We have structured a workflow to characterize the initiating events that are relevant in driving respiratory sensitization. OASIS pipeline technology was used to encode these events as components in a software platform to enable a prediction of respiratory sensitization potential to be made for new untested chemicals. This prediction platform could be useful in the assessment of respiratory sensitization

  19. Data Analysis A Model Comparison Approach, Second Edition

    CERN Document Server

    Judd, Charles M; Ryan, Carey S

    2008-01-01

    This completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. T

  20. Heuristic approaches to models and modeling in systems biology

    NARCIS (Netherlands)

    MacLeod, Miles Alexander James

    2016-01-01

    Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must

  1. A novel approach to modeling and diagnosing the cardiovascular system

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States); Allen, P.A. [Life Link, Richland, WA (United States)

    1995-07-01

    A novel approach to modeling and diagnosing the cardiovascular system is introduced. A model exhibits a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. Potentially, a model will be incorporated into a cardiovascular diagnostic system. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the variables of an individual at a given time are used for diagnosis. This approach also exploits sensor fusion to optimize the utilization of biomedical sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.

  2. An approach to cardiac arrhythmia analysis using hidden Markov models.

    Science.gov (United States)

    Coast, D A; Stern, R M; Cano, G G; Briller, S A

    1990-09-01

    This paper describes a new approach to ECG arrhythmia analysis based on "hidden Markov modeling" (HMM), a technique successfully used since the mid-1970's to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The hidden Markov modeling approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum likelihood reestimation algorithm. Initial results suggest that this approach may provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat including the P wave.

  3. A model-driven approach to information security compliance

    Science.gov (United States)

    Correia, Anacleto; Gonçalves, António; Teodoro, M. Filomena

    2017-06-01

    The availability, integrity and confidentiality of information are fundamental to the long-term survival of any organization. Information security is a complex issue that must be holistically approached, combining assets that support corporate systems, in an extended network of business partners, vendors, customers and other stakeholders. This paper addresses the conception and implementation of information security systems, conform the ISO/IEC 27000 set of standards, using the model-driven approach. The process begins with the conception of a domain level model (computation independent model) based on information security vocabulary present in the ISO/IEC 27001 standard. Based on this model, after embedding in the model mandatory rules for attaining ISO/IEC 27001 conformance, a platform independent model is derived. Finally, a platform specific model serves the base for testing the compliance of information security systems with the ISO/IEC 27000 set of standards.

  4. Asteroid modeling for testing spacecraft approach and landing.

    Science.gov (United States)

    Martin, Iain; Parkes, Steve; Dunstan, Martin; Rowell, Nick

    2014-01-01

    Spacecraft exploration of asteroids presents autonomous-navigation challenges that can be aided by virtual models to test and develop guidance and hazard-avoidance systems. Researchers have extended and applied graphics techniques to create high-resolution asteroid models to simulate cameras and other spacecraft sensors approaching and descending toward asteroids. A scalable model structure with evenly spaced vertices simplifies terrain modeling, avoids distortion at the poles, and enables triangle-strip definition for efficient rendering. To create the base asteroid models, this approach uses two-phase Poisson faulting and Perlin noise. It creates realistic asteroid surfaces by adding both crater models adapted from lunar terrain simulation and multiresolution boulders. The researchers evaluated the virtual asteroids by comparing them with real asteroid images, examining the slope distributions, and applying a surface-relative feature-tracking algorithm to the models.

  5. Mathematical models for therapeutic approaches to control HIV disease transmission

    CERN Document Server

    Roy, Priti Kumar

    2015-01-01

    The book discusses different therapeutic approaches based on different mathematical models to control the HIV/AIDS disease transmission. It uses clinical data, collected from different cited sources, to formulate the deterministic as well as stochastic mathematical models of HIV/AIDS. It provides complementary approaches, from deterministic and stochastic points of view, to optimal control strategy with perfect drug adherence and also tries to seek viewpoints of the same issue from different angles with various mathematical models to computer simulations. The book presents essential methods and techniques for students who are interested in designing epidemiological models on HIV/AIDS. It also guides research scientists, working in the periphery of mathematical modeling, and helps them to explore a hypothetical method by examining its consequences in the form of a mathematical modelling and making some scientific predictions. The model equations, mathematical analysis and several numerical simulations that are...

  6. A genetic predictive model for canine hip dysplasia: integration of Genome Wide Association Study (GWAS) and candidate gene approaches.

    Science.gov (United States)

    Bartolomé, Nerea; Segarra, Sergi; Artieda, Marta; Francino, Olga; Sánchez, Elisenda; Szczypiorska, Magdalena; Casellas, Joaquim; Tejedor, Diego; Cerdeira, Joaquín; Martínez, Antonio; Velasco, Alfonso; Sánchez, Armand

    2015-01-01

    Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B) and case (D/E). C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85) and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors' opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds.

  7. A genetic predictive model for canine hip dysplasia: integration of Genome Wide Association Study (GWAS and candidate gene approaches.

    Directory of Open Access Journals (Sweden)

    Nerea Bartolomé

    Full Text Available Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B and case (D/E. C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85 and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors' opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds.

  8. Prognostic Implications of Magnetic Resonance - Derived Quantification in Asymptomatic Patients with Organic Mitral Regurgitation: Comparison with Doppler Echocardiography-Derived Integrative Approach.

    Science.gov (United States)

    Penicka, Martin; Vecera, Jan; Mirica, Daniela C; Kotrc, Martin; Kockova, Radka; Van Camp, Guy

    2017-12-21

    Background -Magnetic resonance imaging (MRI) is an accurate method for the quantitative assessment of organic mitral regurgitation (OMR). The aim of the present study was to compare the discriminative power of MRI quantification and the recommended Doppler-echocardiography (ECHO)-derived integrative approach to identify asymptomatic patients with OMR and adverse outcome. Methods -The study population consisted of 258 asymptomatic patients (63±14 years, 60% males) with preserved left ventricular (LV) ejection fraction (>60%) and chronic moderate and severe OMR (flail 25%, prolapse 75%) defined using the ECHO-derived integrative approach. All patients underwent MRI to quantify regurgitant volume (RV) of OMR by subtracting the aortic forward flow volume from the total LV stroke volume. Severe OMR was defined as RV≥60ml. Results -Mean ECHO-derived RV was on average 17.1ml larger than the MRI-derived RV (pderived LV end-systolic volume index, RV and OMR category (severe vs. moderate), and the ECHO-derived OMR category were independent predictors of all-cause mortality (all pderived RV showed the largest area under the curve to predict mortality (0.72) or its combination with development of indication for mitral valve surgery (0.83). Conclusions -The findings of the present study suggest that the MRI-derived assessment of OMR can better identify patients with severe OMR and adverse outcome than ECHO-derived integrative approach warranting close follow-up and perhaps, early mitral valve surgery.

  9. Prognostic factors in oligodendrogliomas

    DEFF Research Database (Denmark)

    Westergaard, L; Gjerris, F; Klinken, L

    1997-01-01

    .5 years and for the group older than 60 years of 13 months. The group without neurological deficits had a 5-years survival of 43 per cent while the group with deficits had a 5-years survival of 5 per cent. The 5-years survival for oligodendroglioma of grade II was 46 per cent and for grade III 10 per cent......An outcome analysis was performed on 96 patients with pure cerebral oligodendrogliomas operated in the 30-year period 1962 to 1991. The most important predictive prognostic factors were youth and no neurological deficit, demonstrated as a median survival for the group younger than 20 years of 17...

  10. An approach for activity-based DEVS model specification

    DEFF Research Database (Denmark)

    Alshareef, Abdurrahman; Sarjoughian, Hessam S.; Zarrin, Bahram

    2016-01-01

    activity-based behavior modeling of parallel DEVS atomic models. We consider UML activities and actions as fundamental units of behavior modeling, especially in the presence of recent advances in the UML 2.5 specifications. We describe in detail how to approach activity modeling with a set of elemental...... and their corresponding software specifications from both structural and behavioral aspects. Unlike structural modeling, developing models to specify behavior of systems is known to be harder and more complex, particularly when operations with non-trivial control schemes are required. In this paper, we propose specifying...... behavioral constructs for atomic DEVS model. We show how Activity models correspond to the atomic DEVS model using an exemplar. We also highlight the complementary roles of Activity and Statecharts models....

  11. Hybrid continuum-atomistic approach to model electrokinetics in nanofluidics

    Energy Technology Data Exchange (ETDEWEB)

    Amani, Ehsan, E-mail: eamani@aut.ac.ir; Movahed, Saeid, E-mail: smovahed@aut.ac.ir

    2016-06-07

    In this study, for the first time, a hybrid continuum-atomistic based model is proposed for electrokinetics, electroosmosis and electrophoresis, through nanochannels. Although continuum based methods are accurate enough to model fluid flow and electric potential in nanofluidics (in dimensions larger than 4 nm), ionic concentration is too low in nanochannels for the continuum assumption to be valid. On the other hand, the non-continuum based approaches are too time-consuming and therefore is limited to simple geometries, in practice. Here, to propose an efficient hybrid continuum-atomistic method of modelling the electrokinetics in nanochannels; the fluid flow and electric potential are computed based on continuum hypothesis coupled with an atomistic Lagrangian approach for the ionic transport. The results of the model are compared to and validated by the results of the molecular dynamics technique for a couple of case studies. Then, the influences of bulk ionic concentration, external electric field, size of nanochannel, and surface electric charge on the electrokinetic flow and ionic mass transfer are investigated, carefully. The hybrid continuum-atomistic method is a promising approach to model more complicated geometries and investigate more details of the electrokinetics in nanofluidics. - Highlights: • A hybrid continuum-atomistic model is proposed for electrokinetics in nanochannels. • The model is validated by molecular dynamics. • This is a promising approach to model more complicated geometries and physics.

  12. Applying a Model-Based Approach for Embedded System Development

    NARCIS (Netherlands)

    Bunse, C.; Gross, H.G.; Peper, C.

    2007-01-01

    Model-based and component-oriented software development approaches are slowly superseding traditional ways of developing embedded systems. For investigating to which extent model-based development is feasible for embedded system development, we conducted a case study in which a small embedded system

  13. A MIXTURE LIKELIHOOD APPROACH FOR GENERALIZED LINEAR-MODELS

    NARCIS (Netherlands)

    WEDEL, M; DESARBO, WS

    1995-01-01

    A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of

  14. A Model-Driven Approach to Teaching Concurrency

    Science.gov (United States)

    Carro, Manuel; Herranz, Angel; Marino, Julio

    2013-01-01

    We present an undergraduate course on concurrent programming where formal models are used in different stages of the learning process. The main practical difference with other approaches lies in the fact that the ability to develop correct concurrent software relies on a systematic transformation of formal models of inter-process interaction (so…

  15. Different Approaches to Covariate Inclusion in the Mixture Rasch Model

    Science.gov (United States)

    Li, Tongyun; Jiao, Hong; Macready, George B.

    2016-01-01

    The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo…

  16. A generative language modeling approach for ranking entities

    NARCIS (Netherlands)

    Weerkamp, W.; Balog, K.; Meij, E.

    2009-01-01

    We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the

  17. Interoperable transactions in business models: A structured approach

    NARCIS (Netherlands)

    Weigand, H.; Verharen, E.; Dignum, F.P.M.

    1996-01-01

    Recent database research has given much attention to the specification of "flexible" transactions that can be used in interoperable systems. Starting from a quite different angle, Business Process Modelling has approached the area of communication modelling as well (the Language/Action

  18. Child human model development: a hybrid validation approach

    NARCIS (Netherlands)

    Forbes, P.A.; Rooij, L. van; Rodarius, C.; Crandall, J.

    2008-01-01

    The current study presents a development and validation approach of a child human body model that will help understand child impact injuries and improve the biofidelity of child anthropometric test devices. Due to the lack of fundamental child biomechanical data needed to fully develop such models a

  19. Modeling Alaska boreal forests with a controlled trend surface approach

    Science.gov (United States)

    Mo Zhou; Jingjing Liang

    2012-01-01

    An approach of Controlled Trend Surface was proposed to simultaneously take into consideration large-scale spatial trends and nonspatial effects. A geospatial model of the Alaska boreal forest was developed from 446 permanent sample plots, which addressed large-scale spatial trends in recruitment, diameter growth, and mortality. The model was tested on two sets of...

  20. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  1. Numerical linked-cluster approach to quantum lattice models.

    Science.gov (United States)

    Rigol, Marcos; Bryant, Tyler; Singh, Rajiv R P

    2006-11-03

    We present a novel algorithm that allows one to obtain temperature dependent properties of quantum lattice models in the thermodynamic limit from exact diagonalization of small clusters. Our numerical linked-cluster approach provides a systematic framework to assess finite-size effects and is valid for any quantum lattice model. Unlike high temperature expansions, which have a finite radius of convergence in inverse temperature, these calculations are accurate at all temperatures provided the range of correlations is finite. We illustrate the power of our approach studying spin models on kagomé, triangular, and square lattices.

  2. Modular Modelling and Simulation Approach - Applied to Refrigeration Systems

    DEFF Research Database (Denmark)

    Sørensen, Kresten Kjær; Stoustrup, Jakob

    2008-01-01

    This paper presents an approach to modelling and simulation of the thermal dynamics of a refrigeration system, specifically a reefer container. A modular approach is used and the objective is to increase the speed and flexibility of the developed simulation environment. The refrigeration system...... is divided into components where the inputs and outputs are described by a set of XML files that can be combined into a composite system model that may be loaded into MATLABtrade. A set of tools that allows the user to easily load the model and run a simulation are provided. The results show a simulation...

  3. Meta-analysis a structural equation modeling approach

    CERN Document Server

    Cheung, Mike W-L

    2015-01-01

    Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the impo

  4. Towards modeling future energy infrastructures - the ELECTRA system engineering approach

    DEFF Research Database (Denmark)

    Uslar, Mathias; Heussen, Kai

    2016-01-01

    Within this contribution, we provide an overview based on previous work conducted in the ELECTRA project to come up with a consistent method for modeling the ELECTRA WoC approach according to the methods established with the M/490 mandate of the European Commission. We will motivate the use...... of the IEC 62559 use case template as well as needed changes to cope particularly with the aspects of controller conflicts and Greenfield technology modeling. From the original envisioned use of the standards, we show a possible transfer on how to properly deal with a Greenfield approach when modeling....

  5. Computational approaches for generating electromagnetic Gaussian Schell-model sources.

    Science.gov (United States)

    Basu, Santasri; Hyde, Milo W; Xiao, Xifeng; Voelz, David G; Korotkova, Olga

    2014-12-29

    Two different methodologies for generating an electromagnetic Gaussian-Schell model source are discussed. One approach uses a sequence of random phase screens at the source plane and the other uses a sequence of random complex transmittance screens. The relationships between the screen parameters and the desired electromagnetic Gaussian-Schell model source parameters are derived. The approaches are verified by comparing numerical simulation results with published theory. This work enables one to design an electromagnetic Gaussian-Schell model source with pre-defined characteristics for wave optics simulations or laboratory experiments.

  6. A study of multidimensional modeling approaches for data warehouse

    Science.gov (United States)

    Yusof, Sharmila Mat; Sidi, Fatimah; Ibrahim, Hamidah; Affendey, Lilly Suriani

    2016-08-01

    Data warehouse system is used to support the process of organizational decision making. Hence, the system must extract and integrate information from heterogeneous data sources in order to uncover relevant knowledge suitable for decision making process. However, the development of data warehouse is a difficult and complex process especially in its conceptual design (multidimensional modeling). Thus, there have been various approaches proposed to overcome the difficulty. This study surveys and compares the approaches of multidimensional modeling and highlights the issues, trend and solution proposed to date. The contribution is on the state of the art of the multidimensional modeling design.

  7. Learning the Task Management Space of an Aircraft Approach Model

    Science.gov (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty

    2014-01-01

    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  8. Current approaches to model extracellular electrical neural microstimulation

    Directory of Open Access Journals (Sweden)

    Sébastien eJoucla

    2014-02-01

    Full Text Available Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include 1 the computation of the electrical potential field created by the stimulation in the tissue, and 2 the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron’s response in a cable equation framework (compartmentalized neuron models. Then, we present a whole finite element approach allows the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

  9. Benchmarking novel approaches for modelling species range dynamics.

    Science.gov (United States)

    Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

    2016-08-01

    Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches

  10. Modeling Approaches and Systems Related to Structured Modeling.

    Science.gov (United States)

    1987-02-01

    Lasdon 򒾂> and Maturana 򒾃> for surveys of several modern systems. A -6- N NN- %0 CAMPS (Lucas and Mitra 򒾁>) -- Computer Assisted Mathe- %l...583-589. MATURANA , S. 򒾃>. "Comparative Analysis of Mathematical Modeling Systems," informal note, Graduate School of Manage- ment, UCLA, February

  11. Modelling Micro-Vibrations By Finite Element Model Approach

    Science.gov (United States)

    Soula, Laurent; Laduree, Gregory

    2012-07-01

    With payloads requiring more and more severe environment stability and spacecrafts becoming more and more sensitive to internal mechanical disturbances, micro-vibrations are a key contributor to the performance of new missions. To help predict such behaviour by analyses and verify it by testing, a “METhodology for Analysis of structure-borne MICro- vibrations” is being defined in the frame of the above- named ESA R&D study (METAMIC). This methodology is soon to be validated by a full-test campaign. Meanwhile, this paper proposes a description of the current processes using the Finite Element Models, which start from the perturbation source. Based on ASTRIUM experience, a classification of disturbance sources is proposed. Three different types are selected to illustrate the modelling and the micro- vibrations characterization performed by tests: momentum wheels, cryo-coolers, and stepper motor mechanisms. The perturbation is then to be implemented into system modelling in order to predict its propagation and effect on overall performance. The main assumptions made on structure modelling have to be identified as well as the level of coupling with the disturbance sources has to be anticipated. Most of the questions a project should ask to deal with micro- vibrations are tackled, with the objective to identify all uncertainties, limitations, and validity domains for micro-vibrations prediction.

  12. Prognostic factors in lupus nephritis

    DEFF Research Database (Denmark)

    Faurschou, Mikkel; Starklint, Henrik; Halberg, Poul

    2006-01-01

    To evaluate the prognostic significance of clinical and renal biopsy findings in an unselected cohort of patients with systemic lupus erythematosus (SLE) and nephritis.......To evaluate the prognostic significance of clinical and renal biopsy findings in an unselected cohort of patients with systemic lupus erythematosus (SLE) and nephritis....

  13. Requirements Specifications for Prognostics: An Overview

    Data.gov (United States)

    National Aeronautics and Space Administration — With recent advancements in prognostics methodologies there has been a significant interest in maturing Prognostics and Health Management (PHM) to increase its...

  14. On Applying the Prognostic Performance Metrics

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics performance evaluation has gained significant attention in the past few years. *As prognostics technology matures and more sophisticated methods for...

  15. Metrics for Offline Evaluation of Prognostic Performance

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostic performance evaluation has gained significant attention in the past few years.*Currently, prognostics concepts lack standard definitions and suffer from...

  16. Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

    Science.gov (United States)

    Saha, Sankalita; Celaya, Jose Ramon; Vashchenko, Vladislav; Mahiuddin, Shompa; Goebel, Kai F.

    2011-01-01

    Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

  17. Formulation of consumables management models. Development approach for the mission planning processor working model

    Science.gov (United States)

    Connelly, L. C.

    1977-01-01

    The mission planning processor is a user oriented tool for consumables management and is part of the total consumables subsystem management concept. The approach to be used in developing a working model of the mission planning processor is documented. The approach includes top-down design, structured programming techniques, and application of NASA approved software development standards. This development approach: (1) promotes cost effective software development, (2) enhances the quality and reliability of the working model, (3) encourages the sharing of the working model through a standard approach, and (4) promotes portability of the working model to other computer systems.

  18. Prognostic impact of autophagy biomarkers for cutaneous melanoma.

    Directory of Open Access Journals (Sweden)

    Diana Yao Li Tang

    2016-11-01

    Full Text Available Prognosis and survival for malignant melanoma is highly dependent on early diagnosis and treatment. While the American Joint Committee on Cancer (AJCC criteria provides a means of staging melanomas and guiding treatment approaches, it is unable to identify the risk of disease progression of early stage tumours or provide reliable stratification for novel adjuvant therapies. The demand for credible prognostic/companion biomarkers able to identify high risk melanoma subgroups as well as guide more effective personalised/precision based therapy is therefore of paramount importance. Autophagy, the principle lysosomal-mediated process for the degradation/recycling of cellular debris, is a hot topic in cancer medicine and observations of its deregulation in melanoma have brought its potential as a prognostic biomarker to the forefront of current research. Key regulatory proteins, including Atg8/microtubule-associated light chain 3 (LC3 and BECN1 (Beclin 1 have been proposed as potential prognostic biomarkers. However, given the dynamic nature of autophagy, their expression in vitro does not translate to their use as a prognostic biomarker for melanoma in vivo. We have recently identified the expression levels of Sequestosome1/SQSTM1 (p62 and activating molecule in Beclin 1 regulated autophagy protein 1 (AMBRA1 as novel independent prognostic biomarkers for early stage melanomas. While increasing followed by subsequent decreasing levels of p62 expression reflects the paradoxical role of autophagy in melanoma, expression levels additionally define a novel prognostic biomarker for AJCC stage II tumours. Conversely, loss of AMBRA1 in the epidermis overlying primary melanomas defines a novel prognostic biomarker for AJCC stage I tumours. Collectively, the definition of AMBRA1 and p62 as prognostic biomarkers for early stage melanomas provides novel and accurate means through which to identify tumours at risk of disease progression, facilitating earlier

  19. Prognostic Impact of Autophagy Biomarkers for Cutaneous Melanoma.

    Science.gov (United States)

    Tang, Diana Y L; Ellis, Robert A; Lovat, Penny E

    2016-01-01

    Prognosis and survival for malignant melanoma is highly dependent on early diagnosis and treatment. While the American Joint Committee on Cancer (AJCC) criterion provides a means of staging melanomas and guiding treatment approaches, it is unable to identify the risk of disease progression of early stage tumors or provide reliable stratification for novel adjuvant therapies. The demand for credible prognostic/companion biomarkers able to identify high-risk melanoma subgroups as well as guide more effective personalized/precision-based therapy is therefore of paramount importance. Autophagy, the principle lysosomal-mediated process for the degradation/recycling of cellular debris, is a hot topic in cancer medicine, and observations of its deregulation in melanoma have brought its potential as a prognostic biomarker to the forefront of current research. Key regulatory proteins, including Atg8/microtubule-associated light chain 3 (LC3) and BECN1 (Beclin 1), have been proposed as potential prognostic biomarkers. However, given the dynamic nature of autophagy, their expression in vitro does not translate to their use as a prognostic biomarker for melanoma in vivo. We have recently identified the expression levels of Sequestosome1/SQSTM1 (p62) and activating molecule in Beclin 1-regulated autophagy protein 1 (AMBRA1) as novel independent prognostic biomarkers for early stage melanomas. While increasing followed by subsequent decreasing levels of p62 expression reflects the paradoxical role of autophagy in melanoma, expression levels additionally define a novel prognostic biomarker for AJCC stage II tumors. Conversely, loss of AMBRA1 in the epidermis overlying primary melanomas defines a novel prognostic biomarker for AJCC stage I tumors. Collectively, the definition of AMBRA1 and p62 as prognostic biomarkers for early stage melanomas provides novel and accurate means through which to identify tumors at risk of disease progression, facilitating earlier patient therapeutic

  20. Intelligent Transportation and Evacuation Planning A Modeling-Based Approach

    CERN Document Server

    Naser, Arab

    2012-01-01

    Intelligent Transportation and Evacuation Planning: A Modeling-Based Approach provides a new paradigm for evacuation planning strategies and techniques. Recently, evacuation planning and modeling have increasingly attracted interest among researchers as well as government officials. This interest stems from the recent catastrophic hurricanes and weather-related events that occurred in the southeastern United States (Hurricane Katrina and Rita). The evacuation methods that were in place before and during the hurricanes did not work well and resulted in thousands of deaths. This book offers insights into the methods and techniques that allow for implementing mathematical-based, simulation-based, and integrated optimization and simulation-based engineering approaches for evacuation planning. This book also: Comprehensively discusses the application of mathematical models for evacuation and intelligent transportation modeling Covers advanced methodologies in evacuation modeling and planning Discusses principles a...

  1. THE FAIRSHARES MODEL: AN ETHICAL APPROACH TO SOCIAL ENTERPRISE DEVELOPMENT?

    OpenAIRE

    Ridley-Duff, R.

    2015-01-01

    This paper is based on the keynote address to the 14th International Association of Public and Non-Profit Marketing (IAPNM) conference. It explore the question "What impact do ethical values in the FairShares Model have on social entrepreneurial behaviour?" In the first part, three broad approaches to social enterprise are set out: co-operative and mutual enterprises (CMEs), social and responsible businesses (SRBs) and charitable trading activities (CTAs). The ethics that guide each approach ...

  2. A Model for Optimizing Enterprise's Inventory Costs : a Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    Witold Kosiński

    2013-01-01

    Full Text Available Applicability of a fuzzy approach to a problem originating from administrative accounting, namely to determine an economic order quantity (EOQ in a variable competitive environment with imprecise and vague data, has been presented. For this purpose, the model of ordered fuzzy numbers developed by the first author and his two co-workers is used. The present approach generalizes the one developed within the framework of convex fuzzy numbers and stays outside the probabilistic one. (original abstract

  3. Towards a whole-cell modeling approach for synthetic biology

    Science.gov (United States)

    Purcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.

    2013-06-01

    Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

  4. A model-data based systems approach to process intensification

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    a model-based synthesis method to systematically generate and evaluate alternatives in the first stage and an experiment-model based validation in the second stage. In this way, the search for alternatives is done very quickly, reliably and systematically over a wide range, while resources are preserved...... for focused validation of only the promising candidates in the second-stage. This approach, however, would be limited to intensification based on “known” unit operations, unless the PI process synthesis/design is considered at a lower level of aggregation, namely the phenomena level. That is, the model-based....... Their developments, however, are largely due to experiment based trial and error approaches and while they do not require validation, they can be time consuming and resource intensive. Also, one may ask, can a truly new intensified unit operation be obtained in this way? An alternative two-stage approach is to apply...

  5. Approaches to modeling gene regulatory networks: a gentle introduction.

    Science.gov (United States)

    Schlitt, Thomas

    2013-01-01

    This chapter is split into two main sections; first, I will present an introduction to gene networks. Second, I will discuss various approaches to gene network modeling which will include some examples for using different data sources. Computational modeling has been used for many different biological systems and many approaches have been developed addressing the different needs posed by the different application fields. The modeling approaches presented here are not limited to gene regulatory networks and occasionally I will present other examples. The material covered here is an update based on several previous publications by Thomas Schlitt and Alvis Brazma (FEBS Lett 579(8),1859-1866, 2005; Philos Trans R Soc Lond B Biol Sci 361(1467), 483-494, 2006; BMC Bioinformatics 8(suppl 6), S9, 2007) that formed the foundation for a lecture on gene regulatory networks at the In Silico Systems Biology workshop series at the European Bioinformatics Institute in Hinxton.

  6. Gene expression analysis of a Helicobacter pylori-infected and high-salt diet-treated mouse gastric tumor model: identification of CD177 as a novel prognostic factor in patients with gastric cancer.

    Science.gov (United States)

    Toyoda, Takeshi; Tsukamoto, Tetsuya; Yamamoto, Masami; Ban, Hisayo; Saito, Noriko; Takasu, Shinji; Shi, Liang; Saito, Ayumi; Ito, Seiji; Yamamura, Yoshitaka; Nishikawa, Akiyoshi; Ogawa, Kumiko; Tanaka, Takuji; Tatematsu, Masae

    2013-07-30

    Helicobacter pylori (H. pylori) infection and excessive salt intake are known as important risk factors for stomach cancer in humans. However, interactions of these two factors with gene expression profiles during gastric carcinogenesis remain unclear. In the present study, we investigated the global gene expression associated with stomach carcinogenesis and prognosis of human gastric cancer using a mouse model. To find candidate genes involved in stomach carcinogenesis, we firstly constructed a carcinogen-induced mouse gastric tumor model combined with H. pylori infection and high-salt diet. C57BL/6J mice were given N-methyl-N-nitrosourea in their drinking water and sacrificed after 40 weeks. Animals of a combination group were inoculated with H. pylori and fed a high-salt diet. Gene expression profiles in glandular stomach of the mice were investigated by oligonucleotide microarray. Second, we examined an availability of the candidate gene as prognostic factor for human patients. Immunohistochemical analysis of CD177, one of the up-regulated genes, was performed in human advanced gastric cancer specimens to evaluate the association with prognosis. The multiplicity of gastric tumor in carcinogen-treated mice was significantly increased by combination of H. pylori infection and high-salt diet. In the microarray analysis, 35 and 31 more than two-fold up-regulated and down-regulated genes, respectively, were detected in the H. pylori-infection and high-salt diet combined group compared with the other groups. Quantitative RT-PCR confirmed significant over-expression of two candidate genes including Cd177 and Reg3g. On immunohistochemical analysis of CD177 in human advanced gastric cancer specimens, over-expression was evident in 33 (60.0%) of 55 cases, significantly correlating with a favorable prognosis (P = 0.0294). Multivariate analysis including clinicopathological factors as covariates revealed high expression of CD177 to be an independent prognostic factor

  7. METHODOLOGICAL APPROACHES FOR MODELING THE RURAL SETTLEMENT DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Gorbenkova Elena Vladimirovna

    2017-10-01

    Full Text Available Subject: the paper describes the research results on validation of a rural settlement developmental model. The basic methods and approaches for solving the problem of assessment of the urban and rural settlement development efficiency are considered. Research objectives: determination of methodological approaches to modeling and creating a model for the development of rural settlements. Materials and methods: domestic and foreign experience in modeling the territorial development of urban and rural settlements and settlement structures was generalized. The motivation for using the Pentagon-model for solving similar problems was demonstrated. Based on a systematic analysis of existing development models of urban and rural settlements as well as the authors-developed method for assessing the level of agro-towns development, the systems/factors that are necessary for a rural settlement sustainable development are identified. Results: we created the rural development model which consists of five major systems that include critical factors essential for achieving a sustainable development of a settlement system: ecological system, economic system, administrative system, anthropogenic (physical system and social system (supra-structure. The methodological approaches for creating an evaluation model of rural settlements development were revealed; the basic motivating factors that provide interrelations of systems were determined; the critical factors for each subsystem were identified and substantiated. Such an approach was justified by the composition of tasks for territorial planning of the local and state administration levels. The feasibility of applying the basic Pentagon-model, which was successfully used for solving the analogous problems of sustainable development, was shown. Conclusions: the resulting model can be used for identifying and substantiating the critical factors for rural sustainable development and also become the basis of

  8. An algebraic approach to modeling in software engineering

    Energy Technology Data Exchange (ETDEWEB)

    Loegel, G.J. [Superconducting Super Collider Lab., Dallas, TX (United States)]|[Michigan Univ., Ann Arbor, MI (United States); Ravishankar, C.V. [Michigan Univ., Ann Arbor, MI (United States)

    1993-09-01

    Our work couples the formalism of universal algebras with the engineering techniques of mathematical modeling to develop a new approach to the software engineering process. Our purpose in using this combination is twofold. First, abstract data types and their specification using universal algebras can be considered a common point between the practical requirements of software engineering and the formal specification of software systems. Second, mathematical modeling principles provide us with a means for effectively analyzing real-world systems. We first use modeling techniques to analyze a system and then represent the analysis using universal algebras. The rest of the software engineering process exploits properties of universal algebras that preserve the structure of our original model. This paper describes our software engineering process and our experience using it on both research and commercial systems. We need a new approach because current software engineering practices often deliver software that is difficult to develop and maintain. Formal software engineering approaches use universal algebras to describe ``computer science`` objects like abstract data types, but in practice software errors are often caused because ``real-world`` objects are improperly modeled. There is a large semantic gap between the customer`s objects and abstract data types. In contrast, mathematical modeling uses engineering techniques to construct valid models for real-world systems, but these models are often implemented in an ad hoc manner. A combination of the best features of both approaches would enable software engineering to formally specify and develop software systems that better model real systems. Software engineering, like mathematical modeling, should concern itself first and foremost with understanding a real system and its behavior under given circumstances, and then with expressing this knowledge in an executable form.

  9. The PDF Approach for Modelling Particle Transport in Turbulent Flows

    Science.gov (United States)

    Reeks, Michael

    2004-11-01

    The Probabaility Density (PDF) approach for modelling dispersed particle flow is analogous to the classical kinetic theory gases. That is, there exists a master equation (analogous to the Maxwell Boltzmann equation of Kinetic Theory) which can be used in a formal way to derive the two-fluid model equations for both phases of the flow and the associated constitutive relations. In addition the approach deals with the near wall behaviour, incorporating the natural boundary conditions of the flow. There are currently two forms of pdf approach: the first form is similar to kinetic theory in that the pdf P(v,x,t) refers to particle velocity v and position x at time t; a second approach in which the pdf P(v,u,x,t) involves the carrier flow velocity u encountered by a particle based on a generalised Langevin equation. Both approaches deal with both dilute and dense particle flows: the influence of inter-particle collisions is directly analogous to the treatment of molecular collisions in kinetic theory. This presentation will describe how each PDF master equation is derived and the form of the closure approximations for the turbulent fluxes. The form of the continuum equations and constitutive relations derived from these equations will be presented and contrasted and the treatment of near wall behaviour briefly discussed. Validation of these approaches for homogeneous and simple shear flows will be given as well as model predictions for a number of test cases involving transport of particles in non-uniform flows.

  10. Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model Descriptor Consistent Between Processes

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.; Dress, W.B.; Kercel, S.W.

    1999-05-10

    A major problem with cavitation in pumps and other hydraulic devices is that there is no effective method for detecting or predicting its inception. The traditional approach is to declare the pump in cavitation when the total head pressure drops by some arbitrary value (typically 3o/0) in response to a reduction in pump inlet pressure. However, the pump is already cavitating at this point. A method is needed in which cavitation events are captured as they occur and characterized by their process dynamics. The object of this research was to identify specific features of cavitation that could be used as a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor was based on the physics of the phenomena, capturing the salient features of the process dynamics. An important element of this concept is the development and formulation of the extended process feature vector @) or model vector. Thk model-based descriptor encodes the specific information that describes the phenomena and its dynamics and is formulated as a data structure consisting of several elements. The first is a descriptive model abstracting the phenomena. The second is the parameter list associated with the functional model. The third is a figure of merit, a single number between [0,1] representing a confidence factor that the functional model and parameter list actually describes the observed data. Using this as a basis and applying it to the cavitation problem, any given location in a flow loop will have this data structure, differing in value but not content. The extended process feature vector is formulated as follows: E`> [ , {parameter Iist}, confidence factor]. (1) For this study, the model that characterized cavitation was a chirped-exponentially decaying sinusoid. Using the parameters defined by this model, the parameter list included frequency, decay, and chirp rate. Based on this, the

  11. A Comparison of Three Approaches to Model Human Behavior

    Science.gov (United States)

    Palmius, Joel; Persson-Slumpi, Thomas

    2010-11-01

    One way of studying social processes is through the use of simulations. The use of simulations for this purpose has been established as its own field, social simulations, and has been used for studying a variety of phenomena. A simulation of a social setting can serve as an aid for thinking about that social setting, and for experimenting with different parameters and studying the outcomes caused by them. When using the simulation as an aid for thinking and experimenting, the chosen simulation approach will implicitly steer the simulationist towards thinking in a certain fashion in order to fit the model. To study the implications of model choice on the understanding of a setting where human anticipation comes into play, a simulation scenario of a coffee room was constructed using three different simulation approaches: Cellular Automata, Systems Dynamics and Agent-based modeling. The practical implementations of the models were done in three different simulation packages: Stella for Systems Dynamic, CaFun for Cellular automata and SesAM for Agent-based modeling. The models were evaluated both using Randers' criteria for model evaluation, and through introspection where the authors reflected upon how their understanding of the scenario was steered through the model choice. Further the software used for implementing the simulation models was evaluated, and practical considerations for the choice of software package are listed. It is concluded that the models have very different strengths. The Agent-based modeling approach offers the most intuitive support for thinking about and modeling a social setting where the behavior of the individual is in focus. The Systems Dynamics model would be preferable in situations where populations and large groups would be studied as wholes, but where individual behavior is of less concern. The Cellular Automata models would be preferable where processes need to be studied from the basis of a small set of very simple rules. It is

  12. Individual participant data meta-analysis of prognostic factor studies: state of the art?

    Directory of Open Access Journals (Sweden)

    Abo-Zaid Ghada

    2012-04-01

    Full Text Available Abstract Background Prognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. Meta-analysis using individual participant data (IPD, where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. We assessed the feasibility and conduct of this approach. Methods A systematic review to identify published IPD meta-analyses of prognostic factors studies, followed by detailed assessment of a random sample of 20 articles published from 2006. Six of these 20 articles were from the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in traumatic brain injury collaboration, for which additional information was also used from simultaneously published companion papers. Results Forty-eight published IPD meta-analyses of prognostic factors were identified up to March 2009. Only three were published before 2000 but thereafter a median of four articles exist per year, with traumatic brain injury the most active research field. Availability of IPD offered many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility of assessing for non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. The IMPACT initiative is a leading example, and had generally strong design, methodological and statistical standards. Elsewhere, standards are not always as high and improvements in the conduct of IPD meta-analyses of prognostic factor studies are often needed; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely

  13. A Spatial Clustering Approach for Stochastic Fracture Network Modelling

    Science.gov (United States)

    Seifollahi, S.; Dowd, P. A.; Xu, C.; Fadakar, A. Y.

    2014-07-01

    Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach

  14. Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

    Directory of Open Access Journals (Sweden)

    Hongqiang Liu

    2017-01-01

    Full Text Available A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

  15. An Approach to Quality Estimation in Model-Based Development

    DEFF Research Database (Denmark)

    Holmegaard, Jens Peter; Koch, Peter; Ravn, Anders Peter

    2004-01-01

    We present an approach to estimation of parameters for design space exploration in Model-Based Development, where synthesis of a system is done in two stages. Component qualities like space, execution time or power consumption are defined in a repository by platform dependent values. Connectors...... are treated as special components; they have platform dependent overhead values for the qualities and composition functions, defining how qualities are computed from the values of connected components. The approach is exemplified with a prototype estimation tool applied to an OFDM-decoding module modelled...

  16. Patient-specific data fusion defines prognostic cancer subtypes.

    Directory of Open Access Journals (Sweden)

    Yinyin Yuan

    2011-10-01

    Full Text Available Different data types can offer complementary perspectives on the same biological phenomenon. In cancer studies, for example, data on copy number alterations indicate losses and amplifications of genomic regions in tumours, while transcriptomic data point to the impact of genomic and environmental events on the internal wiring of the cell. Fusing different data provides a more comprehensive model of the cancer cell than that offered by any single type. However, biological signals in different patients exhibit diverse degrees of concordance due to cancer heterogeneity and inherent noise in the measurements. This is a particularly important issue in cancer subtype discovery, where personalised strategies to guide therapy are of vital importance. We present a nonparametric Bayesian model for discovering prognostic cancer subtypes by integrating gene expression and copy number variation data. Our model is constructed from a hierarchy of Dirichlet Processes and addresses three key challenges in data fusion: (i To separate concordant from discordant signals, (ii to select informative features, (iii to estimate the number of disease subtypes. Concordance of signals is assessed individually for each patient, giving us an additional level of insight into the underlying disease structure. We exemplify the power of our model in prostate cancer and breast cancer and show that it outperforms competing methods. In the prostate cancer data, we identify an entirely new subtype with extremely poor survival outcome and show how other analyses fail to detect it. In the breast cancer data, we find subtypes with superior prognostic value by using the concordant results. These discoveries were crucially dependent on our model's ability to distinguish concordant and discordant signals within each patient sample, and would otherwise have been missed. We therefore demonstrate the importance of taking a patient-specific approach, using highly-flexible nonparametric

  17. Towards a CPN-Based Modelling Approach for Reconciling Verification and Implementation of Protocol Models

    DEFF Research Database (Denmark)

    Simonsen, Kent Inge; Kristensen, Lars Michael

    2013-01-01

    Formal modelling of protocols is often aimed at one specific purpose such as verification or automatically generating an implementation. This leads to models that are useful for one purpose, but not for others. Being able to derive models for verification and implementation from a single model...... is beneficial both in terms of reduced total modelling effort and confidence that the verification results are valid also for the implementation model. In this paper we introduce the concept of a descriptive specification model and an approach based on refining a descriptive model to target both verification...... and implementation. Our approach has been developed in the context of the Coloured Petri Nets (CPNs) modelling language. We illustrate our approach by presenting a descriptive specification model of the Websocket protocol which is currently under development by the Internet Engineering Task Force (IETF), and we show...

  18. Environmental Radiation Effects on Mammals A Dynamical Modeling Approach

    CERN Document Server

    Smirnova, Olga A

    2010-01-01

    This text is devoted to the theoretical studies of radiation effects on mammals. It uses the framework of developed deterministic mathematical models to investigate the effects of both acute and chronic irradiation in a wide range of doses and dose rates on vital body systems including hematopoiesis, small intestine and humoral immunity, as well as on the development of autoimmune diseases. Thus, these models can contribute to the development of the system and quantitative approaches in radiation biology and ecology. This text is also of practical use. Its modeling studies of the dynamics of granulocytopoiesis and thrombocytopoiesis in humans testify to the efficiency of employment of the developed models in the investigation and prediction of radiation effects on these hematopoietic lines. These models, as well as the properly identified models of other vital body systems, could provide a better understanding of the radiation risks to health. The modeling predictions will enable the implementation of more ef...

  19. A model-data based systems approach to process intensification

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    is to apply a model-based synthesis method to systematically generate and evaluate alternatives in the first stage and an experiment-model based validation in the second stage. In this way, the search for alternatives is done very quickly, reliably and systematically over a wide range, while resources......, the model-based synthesis method must employ models at lower levels of aggregation and through combination rules for phenomena, generate (synthesize) new intensified unit operations. An efficient solution procedure for the synthesis problem is needed to tackle the potentially large number of options....... Their developments, however, are largely due to experiment based trial and error approaches and while they do not require validation, they can be time consuming and resource intensive. Also, one may ask, can a trully new intensified unit operation be obtained in this way? An alternative two-stage approach...

  20. Accounting for Errors in Model Analysis Theory: A Numerical Approach

    Science.gov (United States)

    Sommer, Steven R.; Lindell, Rebecca S.

    2004-09-01

    By studying the patterns of a group of individuals' responses to a series of multiple-choice questions, researchers can utilize Model Analysis Theory to create a probability distribution of mental models for a student population. The eigenanalysis of this distribution yields information about what mental models the students possess, as well as how consistently they utilize said mental models. Although the theory considers the probabilistic distribution to be fundamental, there exists opportunities for random errors to occur. In this paper we will discuss a numerical approach for mathematically accounting for these random errors. As an example of this methodology, analysis of data obtained from the Lunar Phases Concept Inventory will be presented. Limitations and applicability of this numerical approach will be discussed.

  1. Prognostic factors for musculoskeletal injury identified through medical screening and training load monitoring in professional football (soccer): a systematic review.

    Science.gov (United States)

    Hughes, T; Sergeant, J C; Parkes, M; Callaghan, M

    2017-05-10

    To identify prognostic factors and models for spinal and lower extremity injuries in adult professional/elite football players from medical screening and training load monitoring processes. The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus electronic bibliographic databases and the Cochrane Database of Systematic Reviews were searched from inception to July 2016. Searches were limited to original research, published in peer reviewed journals of any language. The Quality in Prognostic Studies (QUIPS) tool was used for appraisal and the modified GRADE approach was used for synthesis. Prospective and retrospective cohort study designs of spinal and lower extremity injury incidence were found from populations of adult professional/elite football players, between 16 and 40 years. Non-football or mixed sports were excluded. 858 manuscripts were identified. Removing duplications left 551 studies, which were screened for eligibility by title and abstract. Of these, 531 studies were not eligible and were excluded. The full text of the remaining 20 studies were obtained; a further 10 studies were excluded. 10 studies were included for appraisal and analysis, for 3344 participants. Due to the paucity and heterogeneity of the literature, and shortcomings in methodology and reporting, the evidence is of very low or low quality and therefore cannot be deemed robust enough to suggest conclusive prognostic factors for all lower limb musculoskeletal injury outcomes identified. No studies were identified that examined spinal injury outcomes or prognostic models.

  2. Statistical approach for uncertainty quantification of experimental modal model parameters

    DEFF Research Database (Denmark)

    Luczak, M.; Peeters, B.; Kahsin, M.

    2014-01-01

    . This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...... estimates obtained from vibration experiments. Modal testing results are influenced by numerous factors introducing uncertainty to the measurement results. Different experimental techniques applied to the same test item or testing numerous nominally identical specimens yields different test results...

  3. Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings

    OpenAIRE

    Wang, Lu; Cardie, Claire

    2016-01-01

    We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words. Concretely, a series of unsupervised topic models is explored and experimental results show that fine-grained topic models, which discover topics at the utterance-level rather than the document-level, can better identify the gist of the decision-making process. Moreover, our proposed token-level summarization approach, which is able to remove re...

  4. Urban Modelling with Typological Approach. Case Study: Merida, Yucatan, Mexico

    Science.gov (United States)

    Rodriguez, A.

    2017-08-01

    In three-dimensional models of urban historical reconstruction, missed contextual architecture faces difficulties because it does not have much written references in contrast to the most important monuments. This is the case of Merida, Yucatan, Mexico during the Colonial Era (1542-1810), which has lost much of its heritage. An alternative to offer a hypothetical view of these elements is a typological - parametric definition that allows a 3D modeling approach to the most common features of this heritage evidence.

  5. Modeling Electronic Circular Dichroism within the Polarizable Embedding Approach

    DEFF Research Database (Denmark)

    Nørby, Morten S; Olsen, Jógvan Magnus Haugaard; Steinmann, Casper

    2017-01-01

    We present a systematic investigation of the key components needed to model single chromophore electronic circular dichroism (ECD) within the polarizable embedding (PE) approach. By relying on accurate forms of the embedding potential, where especially the inclusion of local field effects...... sampling. We show that a significant number of snapshots are needed to avoid artifacts in the calculated electronic circular dichroism parameters due to insufficient configurational sampling, thus highlighting the efficiency of the PE model....

  6. Gray-box modelling approach for description of storage tunnel

    DEFF Research Database (Denmark)

    Harremoës, Poul; Carstensen, Jacob

    1999-01-01

    The dynamics of a storage tunnel is examined using a model based on on-line measured data and a combination of simple deterministic and black-box stochastic elements. This approach, called gray-box modeling, is a new promising methodology for giving an on-line state description of sewer systems. ...... in a SCADA system because the most important information on the specific system is provided on-line...

  7. Computational Fluid Dynamic Approach for Biological System Modeling

    OpenAIRE

    Huang, Weidong; Wu, Chundu; Xiao, Bingjia; Xia, Weidong

    2005-01-01

    Various biological system models have been proposed in systems biology, which are based on the complex biological reactions kinetic of various components. These models are not practical because we lack of kinetic information. In this paper, it is found that the enzymatic reaction and multi-order reaction rate is often controlled by the transport of the reactants in biological systems. A Computational Fluid Dynamic (CFD) approach, which is based on transport of the components and kinetics of b...

  8. Data Mining Approaches for Modeling Complex Electronic Circuit Design Activities

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Yongjin [Ajou University, Suwon, South Korea; Omitaomu, Olufemi A [ORNL; Wang, Gi-Nam [Ajou University, Suwon, South Korea

    2008-01-01

    A printed circuit board (PCB) is an essential part of modern electronic circuits. It is made of a flat panel of insulating materials with patterned copper foils that act as electric pathways for various components such as ICs, diodes, capacitors, resistors, and coils. The size of PCBs has been shrinking over the years, while the number of components mounted on these boards has increased considerably. This trend makes the design and fabrication of PCBs ever more difficult. At the beginning of design cycles, it is important to estimate the time to complete the steps required accurately, based on many factors such as the required parts, approximate board size and shape, and a rough sketch of schematics. Current approach uses multiple linear regression (MLR) technique for time and cost estimations. However, the need for accurate predictive models continues to grow as the technology becomes more advanced. In this paper, we analyze a large volume of historical PCB design data, extract some important variables, and develop predictive models based on the extracted variables using a data mining approach. The data mining approach uses an adaptive support vector regression (ASVR) technique; the benchmark model used is the MLR technique currently being used in the industry. The strengths of SVR for this data include its ability to represent data in high-dimensional space through kernel functions. The computational results show that a data mining approach is a better prediction technique for this data. Our approach reduces computation time and enhances the practical applications of the SVR technique.

  9. The Generalised Ecosystem Modelling Approach in Radiological Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Klos, Richard

    2008-03-15

    An independent modelling capability is required by SSI in order to evaluate dose assessments carried out in Sweden by, amongst others, SKB. The main focus is the evaluation of the long-term radiological safety of radioactive waste repositories for both spent fuel and low-level radioactive waste. To meet the requirement for an independent modelling tool for use in biosphere dose assessments, SSI through its modelling team CLIMB commissioned the development of a new model in 2004, a project to produce an integrated model of radionuclides in the landscape. The generalised ecosystem modelling approach (GEMA) is the result. GEMA is a modular system of compartments representing the surface environment. It can be configured, through water and solid material fluxes, to represent local details in the range of ecosystem types found in the past, present and future Swedish landscapes. The approach is generic but fine tuning can be carried out using local details of the surface drainage system. The modular nature of the modelling approach means that GEMA modules can be linked to represent large scale surface drainage features over an extended domain in the landscape. System change can also be managed in GEMA, allowing a flexible and comprehensive model of the evolving landscape to be constructed. Environmental concentrations of radionuclides can be calculated and the GEMA dose pathway model provides a means of evaluating the radiological impact of radionuclide release to the surface environment. This document sets out the philosophy and details of GEMA and illustrates the functioning of the model with a range of examples featuring the recent CLIMB review of SKB's SR-Can assessment

  10. Prognostic value of proliferation in pleomorphic soft tissue sarcomas

    DEFF Research Database (Denmark)

    Seinen, Jojanneke M; Jönsson, Mats; Bendahl, Pär-Ola O

    2012-01-01

    = 1.6-12.1), Top2a (hazard ratio = 2.2, CI = 1.2-3.5) and high S-phase fraction (hazard ratio = 1.8, CI = 1.2-3.7) significantly correlated with risk for metastasis. When combined with currently used prognostic factors, Ki-67, S-phase fraction and Top2a fraction contributed to refined identification...... of prognostic risk groups. Proliferation, as assessed by expression of Ki-67 and Top2a and evaluation of S-phase fraction and applied to statistical decision-tree models, provides prognostic information in soft tissue sarcomas of the extremity and trunk wall. Though proliferation contributes independently...... to currently applied prognosticators, its role is particularly strong when few other factors are available, which suggests a role in preoperative decision-making related to identification of high-risk individuals who would benefit from neoadjuvant therapy....

  11. Ice Accretion Modeling using an Eulerian Approach for Droplet Impingement

    Science.gov (United States)

    Kim, Joe Woong; Garza, Dennis P.; Sankar, Lakshmi N.; Kreeger, Richard E.

    2012-01-01

    A three-dimensional Eulerian analysis has been developed for modeling droplet impingement on lifting bodes. The Eulerian model solves the conservation equations of mass and momentum to obtain the droplet flow field properties on the same mesh used in CFD simulations. For complex configurations such as a full rotorcraft, the Eulerian approach is more efficient because the Lagrangian approach would require a significant amount of seeding for accurate estimates of collection efficiency. Simulations are done for various benchmark cases such as NACA0012 airfoil, MS317 airfoil and oscillating SC2110 airfoil to illustrate its use. The present results are compared with results from the Lagrangian approach used in an industry standard analysis called LEWICE.

  12. Explaining ESL Essay Holistic Scores: A Multilevel Modeling Approach

    Science.gov (United States)

    Barkaoui, Khaled

    2010-01-01

    This study adopted a multilevel modeling (MLM) approach to examine the contribution of rater and essay factors to variability in ESL essay holistic scores. Previous research aiming to explain variability in essay holistic scores has focused on either rater or essay factors. The few studies that have examined the contribution of more than one…

  13. Teaching Modeling with Partial Differential Equations: Several Successful Approaches

    Science.gov (United States)

    Myers, Joseph; Trubatch, David; Winkel, Brian

    2008-01-01

    We discuss the introduction and teaching of partial differential equations (heat and wave equations) via modeling physical phenomena, using a new approach that encompasses constructing difference equations and implementing these in a spreadsheet, numerically solving the partial differential equations using the numerical differential equation…

  14. Smeared crack modelling approach for corrosion-induced concrete damage

    DEFF Research Database (Denmark)

    Thybo, Anna Emilie Anusha; Michel, Alexander; Stang, Henrik

    2017-01-01

    compared to experimental data obtained by digital image correlation and published in the literature. Excellent agreements between experimentally observed and numerically predicted crack patterns at the micro and macro scale indicate the capability of the modelling approach to accurately capture corrosion...

  15. A novel Monte Carlo approach to hybrid local volatility models

    NARCIS (Netherlands)

    van der Stoep, A.W.; Grzelak, L.A.; Oosterlee, C.W.

    2017-01-01

    We present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant. Finance,

  16. A new approach in modeling the response of RPC detectors

    CERN Document Server

    Benussi, L; Colafranceschi, S; Fabbri, F L; Giardoni, M; Passamonti, L; Piccolo, D; Pierluigi, D; Russo, A; Saviano, G; Buontempo, S; Cimmino, A; de Gruttola, M; Fabozzi, F; Iorio, A O M; Lista, L; Paolucci, P; Baesso, P; Belli, G; Pagano, D; Ratti, S P; Vicini, A; Vitulo, P; Viviani, C; Sharma, A; Bhattacharyya, A K

    2012-01-01

    The response of RPC detectors is highly sensitive to environmental variables. A novel approach is presented to model the response of RPC detectors in a variety of experimental conditions. The algorithm, based on Artificial Neural Networks, has been developed and tested on the CMS RPC gas gain monitoring system during commissioning.

  17. Model-independent approach for dark matter phenomenology ...

    Indian Academy of Sciences (India)

    Abstract. We have studied the phenomenology of dark matter at the ILC and cosmic positron experiments based on model-independent approach. We have found a strong correlation between dark matter signatures at the ILC and those in the indirect detec- tion experiments of dark matter. Once the dark matter is discovered ...

  18. Application of stochastic frontier approach model to assess technical ...

    African Journals Online (AJOL)

    Application of stochastic frontier approach model to assess technical efficiency in Kenya's maize production. ... primary school education would enhance maize productivity. Thus, if hybrid seeds, tractor services and agricultural credit ... efficiency would increase. Key words: Socio-economic factors, farm characteristics, maize ...

  19. Export of microplastics from land to sea. A modelling approach

    NARCIS (Netherlands)

    Siegfried, Max; Koelmans, A.A.; Besseling, E.; Kroeze, C.

    2017-01-01

    Quantifying the transport of plastic debris from river to sea is crucial for assessing the risks of plastic debris to human health and the environment. We present a global modelling approach to analyse the composition and quantity of point-source microplastic fluxes from European rivers to the sea.

  20. Improving waterfowl population estimates using hierarchical models: A new approach

    OpenAIRE

    Barker, Nicole; Cumming, Steve; Darveau, Marcel

    2013-01-01

    Recommended citation: Barker, N. K. S., S. G. Cumming, and M. Darveau. 2013. Improving waterfowl population estimates using hierarchical models: A new approach. Poster, Ecology and Conservation of North American Waterfowl. Memphis, TN, USA. Retrieved from figshare: http://dx.doi.org/10.6084/m9.figshare.658776.

  1. EPA and EFSA approaches for Benchmark Dose modeling

    Science.gov (United States)

    Benchmark dose (BMD) modeling has become the preferred approach in the analysis of toxicological dose-response data for the purpose of deriving human health toxicity values. The software packages most often used are Benchmark Dose Software (BMDS, developed by EPA) and PROAST (de...

  2. A fuzzy approach to the Weighted Overlap Dominance model

    DEFF Research Database (Denmark)

    Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt

    2013-01-01

    in an interactive way, where input data can take the form of uniquely-graded or interval-valued information. Here we explore the Weighted Overlap Dominance (WOD) model from a fuzzy perspective and its outranking approach to decision support and multidimensional interval analysis. Firstly, imprecision measures...

  3. Comparing State SAT Scores Using a Mixture Modeling Approach

    Science.gov (United States)

    Kim, YoungKoung Rachel

    2009-01-01

    Presented at the national conference for AERA (American Educational Research Association) in April 2009. The large variability of SAT taker population across states makes state-by-state comparisons of the SAT scores challenging. Using a mixture modeling approach, therefore, the current study presents a method of identifying subpopulations in terms…

  4. Vibro-acoustics of porous materials - waveguide modeling approach

    DEFF Research Database (Denmark)

    Darula, Radoslav; Sorokin, Sergey V.

    2016-01-01

    The porous material is considered as a compound multi-layered waveguide (i.e. a fluid layer surrounded with elastic layers) with traction free boundary conditions. The attenuation of the vibro-acoustic waves in such a material is assessed. This approach is compared with a conventional Biot's model...

  5. Atomistic approach for modeling metal-semiconductor interfaces

    DEFF Research Database (Denmark)

    Stradi, Daniele; Martinez, Umberto; Blom, Anders

    2016-01-01

    We present a general framework for simulating interfaces using an atomistic approach based on density functional theory and non-equilibrium Green's functions. The method includes all the relevant ingredients, such as doping and an accurate value of the semiconductor band gap, required to model re...

  6. Non-frontal Model Based Approach to Forensic Face Recognition

    NARCIS (Netherlands)

    Dutta, A.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

    2012-01-01

    In this paper, we propose a non-frontal model based approach which ensures that a face recognition system always gets to compare images having similar view (or pose). This requires a virtual suspect reference set that consists of non-frontal suspect images having pose similar to the surveillance

  7. Pruning Chinese trees : an experimental and modelling approach

    NARCIS (Netherlands)

    Zeng, Bo

    2001-01-01

    Pruning of trees, in which some branches are removed from the lower crown of a tree, has been extensively used in China in silvicultural management for many purposes. With an experimental and modelling approach, the effects of pruning on tree growth and on the harvest of plant material were studied.

  8. A Training Model for Women--An Androgynous Approach

    Science.gov (United States)

    Bolton, Elizabeth B.; Humphreys, Luther Wade

    1977-01-01

    Both management and women workers are asking for a reexamination of traditional training and development practices. The existing training model that has worked so well for developing men into managers and leaders should be expanded to include women--an androgynous approach to training--with similarities, rather than differences in abilities…

  9. Model-independent approach for dark matter phenomenology ...

    Indian Academy of Sciences (India)

    We have studied the phenomenology of dark matter at the ILC and cosmic positron experiments based on model-independent approach. We have found a strong correlation between dark matter signatures at the ILC and those in the indirect detection experiments of dark matter. Once the dark matter is discovered in the ...

  10. Understanding Gulf War Illness: An Integrative Modeling Approach

    Science.gov (United States)

    2015-10-01

    students Gaytri Patel and Trevor Barker prepared and submitted abstracts to 2 international conferences: (i) the Anxiety and Depression Association of...Southeastern University, 3301 College Avenue, Fort Lauderdale, FL 33314 Centers for Disease Control, NIOSH, 1095 Willowdale Road, Morgantown, WV 26505...research efforts using a novel mathematical model. The computational biology approach will enable the consortium to quickly identify targets of

  11. EXTENDE MODEL OF COMPETITIVITY THROUG APPLICATION OF NEW APPROACH DIRECTIVES

    Directory of Open Access Journals (Sweden)

    Slavko Arsovski

    2009-03-01

    Full Text Available The basic subject of this work is the model of new approach impact on quality and safety products, and competency of our companies. This work represents real hypothesis on the basis of expert's experiences, in regard to that the infrastructure with using new approach directives wasn't examined until now, it isn't known which product or industry of Serbia is related to directives of the new approach and CE mark, and it is not known which are effects of the use of the CE mark. This work should indicate existing quality reserves and product's safety, the level of possible competency improvement and increasing the profit by discharging new approach directive requires.

  12. Reconciliation with oneself and with others: From approach to model

    Directory of Open Access Journals (Sweden)

    Nikolić-Ristanović Vesna

    2010-01-01

    Full Text Available The paper intends to present the approach to dealing with war and its consequences which was developed within Victimology Society of Serbia over the last five years, in the framework of Association Joint Action for Truth and Reconciliation (ZAIP. First, the short review of the Association and the process through which ZAIP approach to dealing with a past was developed is presented. Then, the detailed description of the approach itself, with identification of its most important specificities, is presented. In the conclusion, next steps, aimed at development of the model of reconciliation which will have the basis in ZAIP approach and which will be appropriate to social context of Serbia and its surrounding, are suggested.

  13. Data assimilation and prognostic whole ice sheet modelling with the variationally derived, higher order, open source, and fully parallel ice sheet model VarGlaS

    Directory of Open Access Journals (Sweden)

    D. J. Brinkerhoff

    2013-07-01

    Full Text Available We introduce a novel, higher order, finite element ice sheet model called VarGlaS (Variational Glacier Simulator, which is built on the finite element framework FEniCS. Contrary to standard procedure in ice sheet modelling, VarGlaS formulates ice sheet motion as the minimization of an energy functional, conferring advantages such as a consistent platform for making numerical approximations, a coherent relationship between motion and heat generation, and implicit boundary treatment. VarGlaS also solves the equations of enthalpy rather than temperature, avoiding the solution of a contact problem. Rather than include a lengthy model spin-up procedure, VarGlaS possesses an automated framework for model inversion. These capabilities are brought to bear on several benchmark problems in ice sheet modelling, as well as a 500 yr simulation of the Greenland ice sheet at high resolution. VarGlaS performs well in benchmarking experiments and, given a constant climate and a 100 yr relaxation period, predicts a mass evolution of the Greenland ice sheet that matches present-day observations of mass loss. VarGlaS predicts a thinning in the interior and thickening of the margins of the ice sheet.

  14. Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

    Directory of Open Access Journals (Sweden)

    Carla Chia-Ming Chen

    Full Text Available Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

  15. Setting conservation management thresholds using a novel participatory modeling approach.

    Science.gov (United States)

    Addison, P F E; de Bie, K; Rumpff, L

    2015-10-01

    We devised a participatory modeling approach for setting management thresholds that show when management intervention is required to address undesirable ecosystem changes. This approach was designed to be used when management thresholds: must be set for environmental indicators in the face of multiple competing objectives; need to incorporate scientific understanding and value judgments; and will be set by participants with limited modeling experience. We applied our approach to a case study where management thresholds were set for a mat-forming brown alga, Hormosira banksii, in a protected area management context. Participants, including management staff and scientists, were involved in a workshop to test the approach, and set management thresholds to address the threat of trampling by visitors to an intertidal rocky reef. The approach involved trading off the environmental objective, to maintain the condition of intertidal reef communities, with social and economic objectives to ensure management intervention was cost-effective. Ecological scenarios, developed using scenario planning, were a key feature that provided the foundation for where to set management thresholds. The scenarios developed represented declines in percent cover of H. banksii that may occur under increased threatening processes. Participants defined 4 discrete management alternatives to address the threat of trampling and estimated the effect of these alternatives on the objectives under each ecological scenario. A weighted additive model was used to aggregate participants' consequence estimates. Model outputs (decision scores) clearly expressed uncertainty, which can be considered by decision makers and used to inform where to set management thresholds. This approach encourages a proactive form of conservation, where management thresholds and associated actions are defined a priori for ecological indicators, rather than reacting to unexpected ecosystem changes in the future. © 2015 The

  16. Evaluation of Workflow Management Systems - A Meta Model Approach

    Directory of Open Access Journals (Sweden)

    Michael Rosemann

    1998-11-01

    Full Text Available The automated enactment of processes through the use of workflow management systems enables the outsourcing of the control flow from application systems. By now a large number of systems, that follow different workflow paradigms, are available. This leads to the problem of selecting the appropriate workflow management system for a given situation. In this paper we outline the benefits of a meta model approach for the evaluation and comparison of different workflow management systems. After a general introduction on the topic of meta modeling the meta models of the workflow management systems WorkParty (Siemens Nixdorf and FlowMark (IBM are compared as an example. These product specific meta models can be generalized to meta reference models, which helps to specify a workflow methodology. Exemplary, an organisational reference meta model is presented, which helps users in specifying their requirements for a workflow management system.

  17. Modeling electricity spot and futures price dependence: A multifrequency approach

    Science.gov (United States)

    Malo, Pekka

    2009-11-01

    Electricity prices are known to exhibit multifractal properties. We accommodate this finding by investigating multifractal models for electricity prices. In this paper we propose a flexible Copula-MSM (Markov Switching Multifractal) approach for modeling spot and weekly futures price dynamics. By using a conditional copula function, the framework allows us to separately model the dependence structure, while enabling use of multifractal stochastic volatility models to characterize fluctuations in marginal returns. An empirical experiment is carried out using data from Nord Pool. A study of volatility forecasting performance for electricity spot prices reveals that multifractal techniques are a competitive alternative to GARCH models. We also demonstrate how the Copula-MSM model can be employed for finding optimal portfolios, which minimizes the Conditional Value-at-Risk.

  18. Multiphysics modeling using COMSOL a first principles approach

    CERN Document Server

    Pryor, Roger W

    2011-01-01

    Multiphysics Modeling Using COMSOL rapidly introduces the senior level undergraduate, graduate or professional scientist or engineer to the art and science of computerized modeling for physical systems and devices. It offers a step-by-step modeling methodology through examples that are linked to the Fundamental Laws of Physics through a First Principles Analysis approach. The text explores a breadth of multiphysics models in coordinate systems that range from 1D to 3D and introduces the readers to the numerical analysis modeling techniques employed in the COMSOL Multiphysics software. After readers have built and run the examples, they will have a much firmer understanding of the concepts, skills, and benefits acquired from the use of computerized modeling techniques to solve their current technological problems and to explore new areas of application for their particular technological areas of interest.

  19. Approaches to the structural modelling of insect wings.

    Science.gov (United States)

    Wootton, R J; Herbert, R C; Young, P G; Evans, K E

    2003-09-29

    Insect wings lack internal muscles, and the orderly, necessary deformations which they undergo in flight and folding are in part remotely controlled, in part encoded in their structure. This factor is crucial in understanding their complex, extremely varied morphology. Models have proved particularly useful in clarifying the facilitation and control of wing deformation. Their development has followed a logical sequence from conceptual models through physical and simple analytical to numerical models. All have value provided their limitations are realized and constant comparisons made with the properties and mechanical behaviour of real wings. Numerical modelling by the finite element method is by far the most time-consuming approach, but has real potential in analysing the adaptive significance of structural details and interpreting evolutionary trends. Published examples are used to review the strengths and weaknesses of each category of model, and a summary is given of new work using finite element modelling to investigate the vibration properties and response to impact of hawkmoth wings.

  20. A simplified GIS approach to modeling global leaf water isoscapes.

    Directory of Open Access Journals (Sweden)

    Jason B West

    Full Text Available The stable hydrogen (delta(2H and oxygen (delta(18O isotope ratios of organic and inorganic materials record biological and physical processes through the effects of substrate isotopic composition and fractionations that occur as reactions proceed. At large scales, these processes can exhibit spatial predictability because of the effects of coherent climatic patterns over the Earth's surface. Attempts to model spatial variation in the stable isotope ratios of water have been made for decades. Leaf water has a particular importance for some applications, including plant organic materials that record spatial and temporal climate variability and that may be a source of food for migrating animals. It is also an important source of the variability in the isotopic composition of atmospheric gases. Although efforts to model global-scale leaf water isotope ratio spatial variation have been made (especially of delta(18O, significant uncertainty remains in models and their execution across spatial domains. We introduce here a Geographic Information System (GIS approach to the generation of global, spatially-explicit isotope landscapes (= isoscapes of "climate normal" leaf water isotope ratios. We evaluate the approach and the resulting products by comparison with simulation model outputs and point measurements, where obtainable, over the Earth's surface. The isoscapes were generated using biophysical models of isotope fractionation and spatially continuous precipitation isotope and climate layers as input model drivers. Leaf water delta(18O isoscapes produced here generally agreed with latitudinal averages from GCM/biophysical model products, as well as mean values from point measurements. These results show global-scale spatial coherence in leaf water isotope ratios, similar to that observed for precipitation and validate the GIS approach to modeling leaf water isotopes. These results demonstrate that relatively simple models of leaf water enrichment