Model of critical diagnostic reasoning: achieving expert clinician performance.

Science.gov (United States)

Harjai, Prashant Kumar; Tiwari, Ruby

2009-01-01

Diagnostic reasoning refers to the analytical processes used to determine patient health problems. While the education curriculum and health care system focus on training nurse clinicians to accurately recognize and rescue clinical situations, assessments of non-expert nurses have yielded less than satisfactory data on diagnostic competency. The contrast between the expert and non-expert nurse clinician raises the important question of how differences in thinking may contribute to a large divergence in accurate diagnostic reasoning. This article recognizes superior organization of one's knowledge base, using prototypes, and quick retrieval of pertinent information, using similarity recognition as two reasons for the expert's superior diagnostic performance. A model of critical diagnostic reasoning, using prototypes and similarity recognition, is proposed and elucidated using case studies. This model serves as a starting point toward bridging the gap between clinical data and accurate problem identification, verification, and management while providing a structure for a knowledge exchange between expert and non-expert clinicians.

Reactor noise diagnostics based on multivariate autoregressive modeling: Application to LOFT [Loss-of-Fluid-Test] reactor process noise

International Nuclear Information System (INIS)

Gloeckler, O.; Upadhyaya, B.R.

1987-01-01

Multivariate noise analysis of power reactor operating signals is useful for plant diagnostics, for isolating process and sensor anomalies, and for automated plant monitoring. In order to develop a reliable procedure, the previously established techniques for empirical modeling of fluctuation signals in power reactors have been improved. Application of the complete algorithm to operational data from the Loss-of-Fluid-Test (LOFT) Reactor showed that earlier conjectures (based on physical modeling) regarding the perturbation sources in a Pressurized Water Reactor (PWR) affecting coolant temperature and neutron power fluctuations can be systematically explained. This advanced methodology has important implication regarding plant diagnostics, and system or sensor anomaly isolation. 6 refs., 24 figs

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

Science.gov (United States)

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

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Swagata Payra

2014-01-01

Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

Modification of the Integrated Sasang Constitutional Diagnostic Model

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Jiho Nam

2017-01-01

Full Text Available In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0% and the female SE type (43.7% → 64.5%, which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine.

PC based diagnostic system for nitrogen production unit of HWP

International Nuclear Information System (INIS)

Lamba, D.S.; Rao, V.C.; Krishnan, S.; Kamaraj, T.; Krishnaswamy, C.

1992-01-01

The plant diagnostic system monitors the input data from local processing unit and tries to diagnose the cause of the failure. The system is a rule based application program that can perform tasks itself using fault tree model which displays the logical relationships between critical events and their possible ways occurrence, i.e. hardware failure, process faults and human error etc. Unit 37 Nitrogen Plant is taken as a prototype model for trying the plant diagnostics system. (author). 3 refs., 2 figs

Model-based versus specific dosimetry in diagnostic context: Comparison of three dosimetric approaches

Energy Technology Data Exchange (ETDEWEB)

Marcatili, S., E-mail: sara.marcatili@inserm.fr; Villoing, D.; Mauxion, T.; Bardiès, M. [Inserm, UMR1037 CRCT, Toulouse F-31000, France and Université Toulouse III-Paul Sabatier, UMR1037 CRCT, Toulouse F-31000 (France); McParland, B. J. [Imaging Technology Group, GE Healthcare, Life Sciences, B22U The Grove Centre, White Lion Road, Amersham, England HP7 9LL (United Kingdom)

2015-03-15

Purpose: The dosimetric assessment of novel radiotracers represents a legal requirement in most countries. While the techniques for the computation of internal absorbed dose in a therapeutic context have made huge progresses in recent years, in a diagnostic scenario the absorbed dose is usually extracted from model-based lookup tables, most often derived from International Commission on Radiological Protection (ICRP) or Medical Internal Radiation Dose (MIRD) Committee models. The level of approximation introduced by these models may impact the resulting dosimetry. The aim of this work is to establish whether a more refined approach to dosimetry can be implemented in nuclear medicine diagnostics, by analyzing a specific case. Methods: The authors calculated absorbed doses to various organs in six healthy volunteers administered with flutemetamol ({sup 18}F) injection. Each patient underwent from 8 to 10 whole body 3D PET/CT scans. This dataset was analyzed using a Monte Carlo (MC) application developed in-house using the toolkit GATE that is capable to take into account patient-specific anatomy and radiotracer distribution at the voxel level. They compared the absorbed doses obtained with GATE to those calculated with two commercially available software: OLINDA/EXM and STRATOS implementing a dose voxel kernel convolution approach. Results: Absorbed doses calculated with GATE were higher than those calculated with OLINDA. The average ratio between GATE absorbed doses and OLINDA’s was 1.38 ± 0.34 σ (from 0.93 to 2.23). The discrepancy was particularly high for the thyroid, with an average GATE/OLINDA ratio of 1.97 ± 0.83 σ for the six patients. Differences between STRATOS and GATE were found to be higher. The average ratio between GATE and STRATOS absorbed doses was 2.51 ± 1.21 σ (from 1.09 to 6.06). Conclusions: This study demonstrates how the choice of the absorbed dose calculation algorithm may introduce a bias when gamma radiations are of importance, as is

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments

Directory of Open Access Journals (Sweden)

Bethany Shinkins

2017-04-01

Full Text Available Abstract Background Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1 what evidence aside from test accuracy was searched for and synthesised, 2 which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3 how/whether threshold effects were explored, 4 how the potential dependency between multiple tests in a pathway was accounted for, and 5 for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings

A general diagnostic model applied to language testing data.

Science.gov (United States)

von Davier, Matthias

2008-11-01

Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well-known models, such as univariate and multivariate versions of the Rasch model and the two-parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL Internet-based testing.

Mixed Portmanteau Test for Diagnostic Checking of Time Series Models

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Sohail Chand

2014-01-01

Full Text Available Model criticism is an important stage of model building and thus goodness of fit tests provides a set of tools for diagnostic checking of the fitted model. Several tests are suggested in literature for diagnostic checking. These tests use autocorrelation or partial autocorrelation in the residuals to criticize the adequacy of fitted model. The main idea underlying these portmanteau tests is to identify if there is any dependence structure which is yet unexplained by the fitted model. In this paper, we suggest mixed portmanteau tests based on autocorrelation and partial autocorrelation functions of the residuals. We derived the asymptotic distribution of the mixture test and studied its size and power using Monte Carlo simulations.

Nanotechnology based diagnostics for neurological disorders

Energy Technology Data Exchange (ETDEWEB)

Kurek, Nicholas S; Chandra, Sathees B., E-mail: schandra@roosevelt.edu [Department of Biological, Chemical and Physical Sciences, Roosevelt University, Chicago, IL (United States)

2012-07-01

Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

Nanotechnology based diagnostics for neurological disorders

Energy Technology Data Exchange (ETDEWEB)

Kurek, Nicholas S.; Chandra, Sathees B., E-mail: schandra@roosevelt.edu [Department of Biological, Chemical and Physical Sciences, Roosevelt University, Chicago, IL (United States)

2012-07-01

Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

Nanotechnology based diagnostics for neurological disorders

International Nuclear Information System (INIS)

Kurek, Nicholas S.; Chandra, Sathees B.

2012-01-01

Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

Comparing the relative cost-effectiveness of diagnostic studies: a new model

International Nuclear Information System (INIS)

Patton, D.D.; Woolfenden, J.M.; Wellish, K.L.

1986-01-01

We have developed a model to compare the relative cost-effectiveness of two or more diagnostic tests. The model defines a cost-effectiveness ratio (CER) for a diagnostic test as the ratio of effective cost to base cost, only dollar costs considered. Effective cost includes base cost, cost of dealing with expected side effects, and wastage due to imperfect test performance. Test performance is measured by diagnostic utility (DU), a measure of test outcomes incorporating the decision-analytic variables sensitivity, specificity, equivocal fraction, disease probability, and outcome utility. Each of these factors affecting DU, and hence CER, is a local, not universal, value; these local values strongly affect CER, which in effect becomes a property of the local medical setting. When DU = +1 and there are no adverse effects, CER = 1 and the patient benefits from the test dollar for dollar. When there are adverse effects effective cost exceeds base cost, and for an imperfect test DU 1. As DU approaches 0 (worthless test), CER approaches infinity (no effectiveness at any cost). If DU is negative, indicating that doing the test at all would be detrimental, CER also becomes negative. We conclude that the CER model is a useful preliminary method for ranking the relative cost-effectiveness of diagnostic tests, and that the comparisons would best be done using local values; different groups might well arrive at different rankings. (Author)

Using Text Models In Diagnostic Tasks.

Directory of Open Access Journals (Sweden)

Korostil Yuriy

2015-09-01

Full Text Available This paper contains developing of a method of solving diagnostic tasks for complex technical objects (STO based on using text models (TMi to describe the functioning of STO. A TMi model is a text description, in normalized form, of all fragments of STO functioning process. The description of TMi is for med using semantic vocabularies of different types, which are generated on the basis of usage of information about all the aspects of STO construction and functioning. Such interpretation description is a subject area for tasks of STO diagnostics. Detection of malfunction and deviations of a functioning process of STO from an established functioning mode is implemented on the basis of analysis of semantic parameters of text description of the STO functioning process in order to determine semantic anomalies which occur in the descriptions of the STO functioning process, as well as in the descriptions of fragments of its functioning. Semantic anomalies occur in case when values of semantic parameters go beyond their established limits.

Internet gaming disorder: Inadequate diagnostic criteria wrapped in a constraining conceptual model.

Science.gov (United States)

Starcevic, Vladan

2017-06-01

Background and aims The paper "Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field" by Kuss, Griffiths, and Pontes (in press) critically examines the DSM-5 diagnostic criteria for Internet gaming disorder (IGD) and addresses the issue of whether IGD should be reconceptualized as gaming disorder, regardless of whether video games are played online or offline. This commentary provides additional critical perspectives on the concept of IGD. Methods The focus of this commentary is on the addiction model on which the concept of IGD is based, the nature of the DSM-5 criteria for IGD, and the inclusion of withdrawal symptoms and tolerance as the diagnostic criteria for IGD. Results The addiction framework on which the DSM-5 concept of IGD is based is not without problems and represents only one of multiple theoretical approaches to problematic gaming. The polythetic, non-hierarchical DSM-5 diagnostic criteria for IGD make the concept of IGD unacceptably heterogeneous. There is no support for maintaining withdrawal symptoms and tolerance as the diagnostic criteria for IGD without their substantial revision. Conclusions The addiction model of IGD is constraining and does not contribute to a better understanding of the various patterns of problematic gaming. The corresponding diagnostic criteria need a thorough overhaul, which should be based on a model of problematic gaming that can accommodate its disparate aspects.

Model-based Diagnostics for Propellant Loading Systems

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National Aeronautics and Space Administration — The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that...

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

Science.gov (United States)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Modeling sequential context effects in diagnostic interpretation of screening mammograms.

Science.gov (United States)

Alamudun, Folami; Paulus, Paige; Yoon, Hong-Jun; Tourassi, Georgia

2018-07-01

Prior research has shown that physicians' medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist's visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist's current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski-Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists' current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Convergence diagnostics for Eigenvalue problems with linear regression model

International Nuclear Information System (INIS)

Shi, Bo; Petrovic, Bojan

2011-01-01

Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)

Surface CUrrents from a Diagnostic model (SCUD): Pacific

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The SCUD data product is an estimate of upper-ocean velocities computed from a diagnostic model (Surface CUrrents from a Diagnostic model). This model makes daily...

Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist.

Science.gov (United States)

Doble, Brett; Tan, Marcus; Harris, Anthony; Lorgelly, Paula

2015-02-01

The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. The aim of this study was to review the characteristics of companion diagnostics that are of importance for inclusion in an economic evaluation. Approaches for including these characteristics in model-based economic evaluations are compared with the intent to describe best practice methods. Five databases and government agency websites were searched to identify model-based economic evaluations comparing a companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic (primary synthesis). Economic evaluations that limited model parameters for the companion diagnostic to only its cost were also identified (secondary synthesis). Quality was assessed using the Quality of Health Economic Studies instrument. 30 studies were included in the review (primary synthesis n = 12; secondary synthesis n = 18). Incremental cost-effectiveness ratios may be lower when the only parameter for the companion diagnostic included in a model is the cost of testing. Incorporating the test's accuracy in addition to its cost may be a more appropriate methodological approach. Altering the prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests can all impact overall model results. The impact of altering a test's threshold for positivity is unknown as it was not addressed in any of the included studies. Additional quality criteria as outlined in our methodological checklist should be considered due to the shortcomings of standard quality assessment tools in differentiating studies that incorporate important test-related characteristics and those that do not. There is a need to refine methods for incorporating the characteristics

Automated extraction of knowledge for model-based diagnostics

Science.gov (United States)

Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

1990-01-01

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

Flowfield modeling and diagnostics

International Nuclear Information System (INIS)

Gupta, A.K.; Lilley, D.G.

1985-01-01

This textbook is devoted solely to flowfield modeling and diagnostics; their practical use, recent and current research, and projected developments and trends. It provides an account of the use of a broad range of techniques in industrial and research practice, both with and without combustion. Application ideas are complemented by details about experimental and modeling techniques

Towards intelligent diagnostic system employing integration of mathematical and engineering model

International Nuclear Information System (INIS)

Isa, Nor Ashidi Mat

2015-01-01

as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image

Towards intelligent diagnostic system employing integration of mathematical and engineering model

Science.gov (United States)

Isa, Nor Ashidi Mat

2015-05-01

as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

Towards intelligent diagnostic system employing integration of mathematical and engineering model

Energy Technology Data Exchange (ETDEWEB)

Isa, Nor Ashidi Mat [Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang (Malaysia)

2015-05-15

as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

Case-Based Fault Diagnostic System

International Nuclear Information System (INIS)

Mohamed, A.H.

2014-01-01

Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

Target Diagnostic Instrument-Based Controls Framework for the National Ignition Facility

International Nuclear Information System (INIS)

Shelton, R; O'Brien, D; Nelson, J; Kamperschroer, J

2007-01-01

NIF target diagnostics are being developed to observe and measure the extreme physics of targets irradiated by the 192-beam laser. The response time of target materials can be on the order of 100ps--the time it takes light to travel 3 cm--temperatures more than 100 times hotter than the surface of the sun, and pressures that exceed 109 atmospheres. Optical and x-ray diagnostics were developed and fielded to observe and record the results of the first 4-beam experiments at NIF. Hard and soft x-ray spectra were measured, and time-integrated and gated x-ray images of hydrodynamics experiments were recorded. Optical diagnostics recorded backscatter from the target, and VISAR laser velocimetry measurements were taken of laser-shocked target surfaces. Additional diagnostics are being developed and commissioned to observe and diagnose ignition implosions, including various neutron and activation diagnostics. NIF's diagnostics are being developed at LLNL and with collaborators at other sites. To accommodate the growing number of target diagnostics, an Instrument-Based Controls hardware-software framework has been developed to facilitate development and ease integration into the NIF Integrated Computer Control System (ICCS). Individual WindowsXP PC controllers for each digitizer, power supply and camera (i.e., instruments) execute controls software unique to each instrument model. Each hardware-software controller manages a single instrument, in contrast to the complexity of combining all the controls software needed for a diagnostic into a single controller. Because of this simplification, controllers can be more easily tested on the actual hardware, evaluating all normal and off-normal conditions. Each target diagnostic is then supported by a number of instruments, each with its own hardware-software instrument-based controller. Advantages of the instrument-based control architecture and framework include reusability, testability, and improved reliability of the deployed

Target Diagnostic Instrument-Based Controls Framework for the National Ignition Facility

Energy Technology Data Exchange (ETDEWEB)

Shelton, R; O' Brien, D; Nelson, J; Kamperschroer, J

2007-05-07

NIF target diagnostics are being developed to observe and measure the extreme physics of targets irradiated by the 192-beam laser. The response time of target materials can be on the order of 100ps--the time it takes light to travel 3 cm--temperatures more than 100 times hotter than the surface of the sun, and pressures that exceed 109 atmospheres. Optical and x-ray diagnostics were developed and fielded to observe and record the results of the first 4-beam experiments at NIF. Hard and soft x-ray spectra were measured, and time-integrated and gated x-ray images of hydrodynamics experiments were recorded. Optical diagnostics recorded backscatter from the target, and VISAR laser velocimetry measurements were taken of laser-shocked target surfaces. Additional diagnostics are being developed and commissioned to observe and diagnose ignition implosions, including various neutron and activation diagnostics. NIF's diagnostics are being developed at LLNL and with collaborators at other sites. To accommodate the growing number of target diagnostics, an Instrument-Based Controls hardware-software framework has been developed to facilitate development and ease integration into the NIF Integrated Computer Control System (ICCS). Individual WindowsXP PC controllers for each digitizer, power supply and camera (i.e., instruments) execute controls software unique to each instrument model. Each hardware-software controller manages a single instrument, in contrast to the complexity of combining all the controls software needed for a diagnostic into a single controller. Because of this simplification, controllers can be more easily tested on the actual hardware, evaluating all normal and off-normal conditions. Each target diagnostic is then supported by a number of instruments, each with its own hardware-software instrument-based controller. Advantages of the instrument-based control architecture and framework include reusability, testability, and improved reliability of the

Experimental and numerical studies on liquid wicking into filter papers for paper-based diagnostics

International Nuclear Information System (INIS)

Liu, Zhi; Hu, Jie; Zhao, Yimeng; Qu, Zhiguo; Xu, Feng

2015-01-01

Paper-based diagnostics have shown promising potential applications in human disease surveillance and food safety analysis at the point-of-care (POC). The liquid wicking behavior in diagnostic fibrous paper plays an important role in development of paper-based diagnostics. In the current study, we performed experimental and numerical research on the liquid wicking height and mass with three width strips into filter paper. The effective porosity could be conveniently measured in the light of the linear correlation between wicking height and mass by the experimental system. A modified model with considering evaporation effect was proposed to predict wicking height and mass. The predicted wicking height and mass using the evaporation model was much closer to the experimental data compared with the model without evaporation. The wicking speed initially decreased significantly and then maintained at a constant value at lower level. The evaporation effect tends to reduce the wicking flow speed. More wicking mass could be obtained at larger strip width but the corresponding reagent loss became significant. The proposed model with evaporation paved a way to understanding the fundamental of fluid flow in diagnostic paper and was essential to provide meaningful and useful reference for the research and development of paper-based diagnostics devices. - Highlights: • A model with considering evaporation was proposed to predict wicking height and mass. • Flow characteristics of filter paper were experimentally and theoretically studied. • Effective porosity could be conveniently measured by the experimental platform. • The evaporation effect tended to reduce the wicking flow speed

Acquiring, Representing, and Evaluating a Competence Model of Diagnostic Strategy.

Science.gov (United States)

Clancey, William J.

This paper describes NEOMYCIN, a computer program that models one physician's diagnostic reasoning within a limited area of medicine. NEOMYCIN's knowledge base and reasoning procedure constitute a model of how human knowledge is organized and how it is used in diagnosis. The hypothesis is tested that such a procedure can be used to simulate both…

A computational framework for converting textual clinical diagnostic criteria into the quality data model.

Science.gov (United States)

Hong, Na; Li, Dingcheng; Yu, Yue; Xiu, Qiongying; Liu, Hongfang; Jiang, Guoqian

2016-10-01

Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria. To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142 individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification. Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization. Copyright © 2016 Elsevier Inc. All rights reserved.

About the Big Graphs Arising when Forming the Diagnostic Models in a Reconfigurable Computing Field of Functional Monitoring and Diagnostics System of the Spacecraft Onboard Control Complex

Directory of Open Access Journals (Sweden)

L. V. Savkin

2015-01-01

Full Text Available One of the problems in implementation of the multipurpose complete systems based on the reconfigurable computing fields (RCF is the problem of optimum redistribution of logicalarithmetic resources in growing scope of functional tasks. Irrespective of complexity, all of them are transformed into an orgraph, which functional and topological structure is appropriately imposed on the RCF based, as a rule, on the field programmable gate array (FPGA.Due to limitation of the hardware configurations and functions realized by means of the switched logical blocks (SLB, the abovementioned problem becomes even more critical when there is a need, within the strictly allocated RCF fragment, to realize even more complex challenge in comparison with the problem which was solved during the previous computing step. In such cases it is possible to speak about graphs of big dimensions with respect to allocated RCF fragment.The article considers this problem through development of diagnostic algorithms to implement diagnostics and control of an onboard control complex of the spacecraft using RCF. It gives examples of big graphs arising with respect to allocated RCF fragment when forming the hardware levels of a diagnostic model, which, in this case, is any hardware-based algorithm of diagnostics in RCF.The article reviews examples of arising big graphs when forming the complicated diagnostic models due to drastic difference in formation of hardware levels on closely located RCF fragments. It also pays attention to big graphs emerging when the multichannel diagnostic models are formed.Three main ways to solve the problem of big graphs with respect to allocated RCF fragment are given. These are: splitting the graph into fragments, use of pop-up windows with relocating and memorizing intermediate values of functions of high hardware levels of diagnostic models, and deep adaptive update of diagnostic model.It is shown that the last of three ways is the most efficient

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

Science.gov (United States)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

Evidence-based diagnostics: adult septic arthritis.

Science.gov (United States)

Carpenter, Christopher R; Schuur, Jeremiah D; Everett, Worth W; Pines, Jesse M

2011-08-01

Acutely swollen or painful joints are common complaints in the emergency department (ED). Septic arthritis in adults is a challenging diagnosis, but prompt differentiation of a bacterial etiology is crucial to minimize morbidity and mortality. The objective was to perform a systematic review describing the diagnostic characteristics of history, physical examination, and bedside laboratory tests for nongonococcal septic arthritis. A secondary objective was to quantify test and treatment thresholds using derived estimates of sensitivity and specificity, as well as best-evidence diagnostic and treatment risks and anticipated benefits from appropriate therapy. Two electronic search engines (PUBMED and EMBASE) were used in conjunction with a selected bibliography and scientific abstract hand search. Inclusion criteria included adult trials of patients presenting with monoarticular complaints if they reported sufficient detail to reconstruct partial or complete 2 × 2 contingency tables for experimental diagnostic test characteristics using an acceptable criterion standard. Evidence was rated by two investigators using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS). When more than one similarly designed trial existed for a diagnostic test, meta-analysis was conducted using a random effects model. Interval likelihood ratios (LRs) were computed when possible. To illustrate one method to quantify theoretical points in the probability of disease whereby clinicians might cease testing altogether and either withhold treatment (test threshold) or initiate definitive therapy in lieu of further diagnostics (treatment threshold), an interactive spreadsheet was designed and sample calculations were provided based on research estimates of diagnostic accuracy, diagnostic risk, and therapeutic risk/benefits. The prevalence of nongonococcal septic arthritis in ED patients with a single acutely painful joint is approximately 27% (95% confidence interval [CI] = 17

CBDS: Constraint-based diagnostic system for malfunction identification in the nuclear power plant

International Nuclear Information System (INIS)

Ha, J.

1992-01-01

Traditional rule-based diagnostic expert systems use the experience of experts in the form of rules that associate symptoms with underlying faults. A commonly recognized failing of such systems is their narrow range of expertise and their inability to recognize problems outside this range of expertise. A model base diagnostic system isolating malfunctioning components-CBDS, the Constraint based Diagnostic System-has been developed. Since the intended behavior of a device is more predictable than unintended behaviors (faults), a model based system using the intended behavior has a potential to diagnose unexpected malfunctions by considering faults as open-quotes anything other than the intended behavior.close quotes As a knowledge base, the CBDS generates and decomposes a constraint network based on the structure and behavior model, which are represented symbolically in algebraic equations. Behaviors of generic components are organized in a component model library. Once the library is available, actual domain knowledge can be represented by declaring component types and their connections. To capture various plant knowledge, the mixed model was developed which allow the use of different parameter types in one equation by defining various operators. The CBDS uses the general idea of model based diagnosis. It detects a discrepancy between observation and prediction using constraint propagation, which carriers and accumulates the assumptions when parameter values are deduced. When measured plant parameters are asserted into a constraint network and are propagated through the network, a discrepancy will be detected if there exists any malfunctioning component. The CBDS was tested in the Recirculation Flow Control System of a BWR, and has been shown to be able to diagnose unexpected events

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

Science.gov (United States)

Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

2016-11-30

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Development of a rule-based diagnostic platform on an object-oriented expert system shell

International Nuclear Information System (INIS)

Wang, Wenlin; Yang, Ming; Seong, Poong Hyun

2016-01-01

Highlights: • Multilevel Flow Model represents system knowledge as a domain map in expert system. • Rule-based fault diagnostic expert system can identify root cause via a causal chain. • Rule-based fault diagnostic expert system can be used for fault simulation training. - Abstract: This paper presents the development and implementation of a real-time rule-based diagnostic platform. The knowledge is acquired from domain experts and textbooks and the design of the fault diagnosis expert system was performed in the following ways: (i) establishing of corresponding classes and instances to build the domain map, (ii) creating of generic fault models based on events, and (iii) building of diagnostic reasoning based on rules. Knowledge representation is a complicated issue of expert systems. One highlight of this paper is that the Multilevel Flow Model has been used to represent the knowledge, which composes the domain map within the expert system as well as providing a concise description of the system. The developed platform is illustrated using the pressure safety system of a pressurized water reactor as an example of the simulation test bed; the platform is developed using the commercial and industrially validated software G2. The emulation test was conducted and it has been proven that the fault diagnosis expert system can identify the faults correctly and in a timely way; this system can be used as a simulation-based training tool to assist operators to make better decisions.

CAD-Based Shielding Analysis for ITER Port Diagnostics

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Serikov Arkady

2017-01-01

Full Text Available Radiation shielding analysis conducted in support of design development of the contemporary diagnostic systems integrated inside the ITER ports is relied on the use of CAD models. This paper presents the CAD-based MCNP Monte Carlo radiation transport and activation analyses for the Diagnostic Upper and Equatorial Port Plugs (UPP #3 and EPP #8, #17. The creation process of the complicated 3D MCNP models of the diagnostics systems was substantially accelerated by application of the CAD-to-MCNP converter programs MCAM and McCad. High performance computing resources of the Helios supercomputer allowed to speed-up the MCNP parallel transport calculations with the MPI/OpenMP interface. The found shielding solutions could be universal, reducing ports R&D costs. The shield block behind the Tritium and Deposit Monitor (TDM optical box was added to study its influence on Shut-Down Dose Rate (SDDR in Port Interspace (PI of EPP#17. Influence of neutron streaming along the Lost Alpha Monitor (LAM on the neutron energy spectra calculated in the Tangential Neutron Spectrometer (TNS of EPP#8. For the UPP#3 with Charge eXchange Recombination Spectroscopy (CXRS-core, an excessive neutron streaming along the CXRS shutter, which should be prevented in further design iteration.

CAD-Based Shielding Analysis for ITER Port Diagnostics

Science.gov (United States)

Serikov, Arkady; Fischer, Ulrich; Anthoine, David; Bertalot, Luciano; De Bock, Maartin; O'Connor, Richard; Juarez, Rafael; Krasilnikov, Vitaly

2017-09-01

Radiation shielding analysis conducted in support of design development of the contemporary diagnostic systems integrated inside the ITER ports is relied on the use of CAD models. This paper presents the CAD-based MCNP Monte Carlo radiation transport and activation analyses for the Diagnostic Upper and Equatorial Port Plugs (UPP #3 and EPP #8, #17). The creation process of the complicated 3D MCNP models of the diagnostics systems was substantially accelerated by application of the CAD-to-MCNP converter programs MCAM and McCad. High performance computing resources of the Helios supercomputer allowed to speed-up the MCNP parallel transport calculations with the MPI/OpenMP interface. The found shielding solutions could be universal, reducing ports R&D costs. The shield block behind the Tritium and Deposit Monitor (TDM) optical box was added to study its influence on Shut-Down Dose Rate (SDDR) in Port Interspace (PI) of EPP#17. Influence of neutron streaming along the Lost Alpha Monitor (LAM) on the neutron energy spectra calculated in the Tangential Neutron Spectrometer (TNS) of EPP#8. For the UPP#3 with Charge eXchange Recombination Spectroscopy (CXRS-core), an excessive neutron streaming along the CXRS shutter, which should be prevented in further design iteration.

Eigenvalue based inverse model of beam for structural modification and diagnostics: theoretical formulation

Directory of Open Access Journals (Sweden)

Leszek Majkut

Full Text Available In the work, the problems of the beam structural modification through coupling the additional mass or elastic support, as well as the problem of diagnostics of the beam cracks, are discussed. The common feature for both problems is that the material parameters in each of the discussed cases change only in one point (additional mass, the support in one point, the crack described by the elastic joint. These systems, after determination of the value of additional element and its localization, should have a given natural vibration frequency. In order to solve the inverse problem, i.e. the problem of finding values of the additional quantities (mass, elasticity, the beam inverse model was proposed. Analysis of this model allows finding such a value of additional mass (elasticity as a function of its localization so that the system has the free vibration frequency, which is desired in the modification problem or measured on the object in the diagnostics.

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation Value-Added Product

Energy Technology Data Exchange (ETDEWEB)

Zhang, Chengzhu [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Xie, Shaocheng [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-10-15

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which

Diagnostic tests’ decision-making rules based upon analysis of ROC-curves

Directory of Open Access Journals (Sweden)

Л. В. Батюк

2015-10-01

Full Text Available In this paper we propose the model which substantiates diagnostics decision making based on the analysis of Receiver Operating Characteristic curves (ROC-curves and predicts optimal values of diagnostic indicators of biomedical information. To assess the quality of the test result prediction the standard criteria of the sensitivity and specificity of the model were used. Values of these criteria were calculated for the cases when the sensitivity of the test was greater than specificity by several times, when the number of correct diagnoses was maximal, when the sensitivity of the test was equal to its specificity and the sensitivity of the test was several times greater than the specificity of the test. To assess the significance of the factor characteristics and to compare the prognostic characteristics of models we used mathematical modeling and plotting the ROC-curves. The optimal value of the diagnostic indicator was found to be achieved when the sensitivity of the test is equal to its specificity. The model was adapted to solve the case when the sensitivity of the test is greater than specificity of the test.

Model-based evaluation of the use of polycyclic aromatic hydrocarbons molecular diagnostic ratios as a source identification tool

International Nuclear Information System (INIS)

Katsoyiannis, Athanasios; Breivik, Knut

2014-01-01

Polycyclic Aromatic Hydrocarbons (PAHs) molecular diagnostic ratios (MDRs) are unitless concentration ratios of pair-PAHs with the same molecular weight (MW); MDRs have long been used as a tool for PAHs source identification purposes. In the present paper, the efficiency of the MDR methodology is evaluated through the use of a multimedia fate model, the calculation of characteristic travel distances (CTD) and the estimation of air concentrations for individual PAHs as a function of distance from an initial point source. The results show that PAHs with the same MW are sometimes characterized by substantially different CTDs and therefore their air concentrations and hence MDRs are predicted to change as the distance from the original source increases. From the assessed pair-PAHs, the biggest CTD difference is seen for Fluoranthene (107 km) vs. Pyrene (26 km). This study provides a strong indication that MDRs are of limited use as a source identification tool. -- Highlights: • Model-based evaluation of the PAHs molecular diagnostic ratios efficiency. • Individual PAHs are characterized by different characteristic travel distances. • MDRs are proven to be a limited tool for source identification. • Use of MDRs for other environmental media is likely unfeasible. -- PAHs molecular diagnostic ratios which change greatly as a function of distance from the emitting source are improper for source identification purposes

In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

Science.gov (United States)

Yi, Yeon-Sook

2017-01-01

This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

Process-Oriented Diagnostics of Tropical Cyclones in Global Climate Models

Science.gov (United States)

Moon, Y.; Kim, D.; Camargo, S. J.; Wing, A. A.; Sobel, A. H.; Bosilovich, M. G.; Murakami, H.; Reed, K. A.; Vecchi, G. A.; Wehner, M. F.; Zarzycki, C. M.; Zhao, M.

2017-12-01

Simulating tropical cyclone (TC) activity with global climate models (GCMs) remains a challenging problem. While some GCMs are able to simulate TC activity that is in good agreement with the observations, many other models exhibit strong biases. Decreasing horizontal grid spacing of the GCM simulations tends to improve the characteristics of simulated TCs, but this enhancement alone does not necessarily lead to greater skill in simulating TC activity. This study uses process-based diagnostics to identify model characteristics that could explain why some GCM simulations are able to produce more realistic TC activity than others. The diagnostics examine how convection, moisture, clouds and related processes are coupled at individual grid points, which yields useful information into how convective parameterizations interact with resolved model dynamics. These diagnostics share similarities with those originally developed to examine the Madden-Julian Oscillations in climate models. This study will examine TCs in eight different GCM simulations performed at NOAA/GFDL, NCAR and NASA that have different horizontal resolutions and ocean coupling. Preliminary results suggest that stronger TCs are closely associated with greater rainfall - thus greater diabatic heating - in the inner-core regions of the storms, which is consistent with previous theoretical studies. Other storm characteristics that can be used to infer why GCM simulations with comparable horizontal grid spacings produce different TC activity will be examined.

Cognitive balanced model: a conceptual scheme of diagnostic decision making.

Science.gov (United States)

Lucchiari, Claudio; Pravettoni, Gabriella

2012-02-01

Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. While traditional understanding of clinical reasoning has failed to consider contextual factors, most debiasing techniques seem to fail in raising sound and safer medical praxis. Technological solutions, being data driven, are fundamental in increasing care safety, but they need to consider human factors. Thus, balanced models, cognitive driven and technology based, are needed in day-to-day applications to actually improve the diagnostic process. The purpose of this article, then, is to provide insight into cognitive influences that have resulted in wrong, delayed or missed diagnosis. Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps. © 2011 Blackwell Publishing Ltd.

Diagnostic reliability of MMPI-2 computer-based test interpretations.

Science.gov (United States)

Pant, Hina; McCabe, Brian J; Deskovitz, Mark A; Weed, Nathan C; Williams, John E

2014-09-01

Reflecting the common use of the MMPI-2 to provide diagnostic considerations, computer-based test interpretations (CBTIs) also typically offer diagnostic suggestions. However, these diagnostic suggestions can sometimes be shown to vary widely across different CBTI programs even for identical MMPI-2 profiles. The present study evaluated the diagnostic reliability of 6 commercially available CBTIs using a 20-item Q-sort task developed for this study. Four raters each sorted diagnostic classifications based on these 6 CBTI reports for 20 MMPI-2 profiles. Two questions were addressed. First, do users of CBTIs understand the diagnostic information contained within the reports similarly? Overall, diagnostic sorts of the CBTIs showed moderate inter-interpreter diagnostic reliability (mean r = .56), with sorts for the 1/2/3 profile showing the highest inter-interpreter diagnostic reliability (mean r = .67). Second, do different CBTIs programs vary with respect to diagnostic suggestions? It was found that diagnostic sorts of the CBTIs had a mean inter-CBTI diagnostic reliability of r = .56, indicating moderate but not strong agreement across CBTIs in terms of diagnostic suggestions. The strongest inter-CBTI diagnostic agreement was found for sorts of the 1/2/3 profile CBTIs (mean r = .71). Limitations and future directions are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

An ARM data-oriented diagnostics package to evaluate the climate model simulation

Science.gov (United States)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle

Directory of Open Access Journals (Sweden)

Ewert Linder

2016-06-01

Full Text Available Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or limitations set by visual perception of a microscopist. Thus diagnostics currently almost exclusively dependent on recognition of morphological features of pathogenic organisms could be based on additional properties, such as motility characteristics recognizable by computer vision. Of special interest are infectious larval stages and “micro swimmers” of e.g., the schistosome life cycle, which infect the intermediate and definitive hosts, respectively. The ciliated miracidium, emerges from the excreted egg upon its contact with water. This means that for diagnostics, recognition of a swimming miracidium is equivalent to recognition of an egg. The motility pattern of miracidia could be defined by computer vision and used as a diagnostic criterion. To develop motility pattern-based diagnostics of schistosomiasis using simple imaging devices, we analyzed Paramecium as a model for the schistosome miracidium. As a model for invasive nematodes, such as strongyloids and filaria, we examined a different type of motility in the apathogenic nematode Turbatrix, the “vinegar eel.” The results of motion time and frequency analysis suggest that target motility may be expressed as specific spectrograms serving as “diagnostic fingerprints.”

Statistical physics of medical diagnostics: Study of a probabilistic model.

Science.gov (United States)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Statistical physics of medical diagnostics: Study of a probabilistic model

Science.gov (United States)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Experimental Investigation on Admittance-Based Piezoelectric Sensor Diagnostic Process

Energy Technology Data Exchange (ETDEWEB)

Jo, Hyejin; Park, Tongil; Park, Gyuhae [Chonnam National University, Gwangju (Korea, Republic of)

2015-01-15

Structural health monitoring (SHM) techniques based on the use of active-sensing piezoelectric (PZT) materials have received considerable attention. The validation of the PZT functionality during SHM operation is critical to successfully implementing a reliable SHM system. In this study, we investigated several parameters that affect the admittance-based sensor diagnostic process. We experimentally identified the temperature dependency of the active-sensor diagnostic process. We found that the admittance-based sensor diagnostic process can differentiate the adhesion conditions of bonding materials that are used to install a PZT on a structure, which is important when designing a sensor diagnostic process for an SHM system.

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

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Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Diagnostics for generalized linear hierarchical models in network meta-analysis.

Science.gov (United States)

Zhao, Hong; Hodges, James S; Carlin, Bradley P

2017-09-01

Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

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Chen, Yong; Liu, Yulun; Ning, Jing; Cormier, Janice; Chu, Haitao

2014-01-01

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies. In order to overcome the potential computational difficulties in the standard full likelihood inference of the proposed hybrid model, we propose an alternative inference procedure based on the composite likelihood. Such composite likelihood based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model mis-specifications compared to the standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma. PMID:25897179

On-line experimental validation of a model-based diagnostic algorithm dedicated to a solid oxide fuel cell system

Science.gov (United States)

Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas

2016-02-01

In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.

Applications of Diagnostic Classification Models: A Literature Review and Critical Commentary

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Sessoms, John; Henson, Robert A.

2018-01-01

Diagnostic classification models (DCMs) classify examinees based on the skills they have mastered given their test performance. This classification enables targeted feedback that can inform remedial instruction. Unfortunately, applications of DCMs have been criticized (e.g., no validity support). Generally, these evaluations have been brief and…

Graphene-based nanoprobes for molecular diagnostics.

Science.gov (United States)

Chen, Shixing; Li, Fuwu; Fan, Chunhai; Song, Shiping

2015-10-07

In recent years, graphene has received widespread attention owing to its extraordinary electrical, chemical, optical, mechanical and structural properties. Lately, considerable interest has been focused on exploring the potential applications of graphene in life sciences, particularly in disease-related molecular diagnostics. In particular, the coupling of functional molecules with graphene as a nanoprobe offers an excellent platform to realize the detection of biomarkers, such as nucleic acids, proteins and other bioactive molecules, with high performance. This article reviews emerging graphene-based nanoprobes in electrical, optical and other assay methods and their application in various strategies of molecular diagnostics. In particular, this review focuses on the construction of graphene-based nanoprobes and their special advantages for the detection of various bioactive molecules. Properties of graphene-based materials and their functionalization are also comprehensively discussed in view of the development of nanoprobes. Finally, future challenges and perspectives of graphene-based nanoprobes are discussed.

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

Science.gov (United States)

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

Influence of the Atmospheric Model on Hanle Diagnostics

Science.gov (United States)

Ishikawa, Ryohko; Uitenbroek, Han; Goto, Motoshi; Iida, Yusuke; Tsuneta, Saku

2018-05-01

We clarify the uncertainty in the inferred magnetic field vector via the Hanle diagnostics of the hydrogen Lyman-α line when the stratification of the underlying atmosphere is unknown. We calculate the anisotropy of the radiation field with plane-parallel semi-empirical models under the nonlocal thermal equilibrium condition and derive linear polarization signals for all possible parameters of magnetic field vectors based on an analytical solution of the atomic polarization and Hanle effect. We find that the semi-empirical models of the inter-network region (FAL-A) and network region (FAL-F) show similar degrees of anisotropy in the radiation field, and this similarity results in an acceptable inversion error ( e.g., {˜} 40 G instead of 50 G in field strength and {˜} 100° instead of 90° in inclination) when FAL-A and FAL-F are swapped. However, the semi-empirical models of FAL-C (averaged quiet-Sun model including both inter-network and network regions) and FAL-P (plage regions) yield an atomic polarization that deviates from all other models, which makes it difficult to precisely determine the magnetic field vector if the correct atmospheric model is not known ( e.g., the inversion error is much larger than 40% of the field strength; {>} 70 G instead of 50 G). These results clearly demonstrate that the choice of model atmosphere is important for Hanle diagnostics. As is well known, one way to constrain the average atmospheric stratification is to measure the center-to-limb variation of the linear polarization signals. The dependence of the center-to-limb variations on the atmospheric model is also presented in this paper.

Diagnostic tests based on human basophils

DEFF Research Database (Denmark)

Kleine-Tebbe, Jörg; Erdmann, Stephan; Knol, Edward F

2006-01-01

-maximal responses, termed 'intrinsic sensitivity'. These variables give rise to shifts in the dose-response curves which, in a diagnostic setting where only a single antigen concentration is employed, may produce false-negative data. Thus, in order to meaningfully utilize the current basophil activation tests....... Diagnostic studies using CD63 or CD203c in hymenoptera, food and drug allergy are critically discussed. Basophil-based tests are indicated for allergy testing in selected cases but should only be performed by experienced laboratories....

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing.

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Tsalatsanis, Athanasios; Hozo, Iztok; Kumar, Ambuj; Djulbegovic, Benjamin

2015-01-01

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing.

Directory of Open Access Journals (Sweden)

Athanasios Tsalatsanis

Full Text Available Dual Processing Theories (DPT assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive and type 2 (deliberative. Based on DPT we have derived a Dual Processing Model (DPM to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

DIVA and DIAPO: two diagnostic knowledge based systems used for French nuclear power plants

International Nuclear Information System (INIS)

Porcheron, M.; Ricard, B.; Joussellin, A.

1997-01-01

In order to improve monitoring and diagnosis capabilities in nuclear power plants, Electricite de France (EDF) has designed an integrated monitoring and diagnosis assistance system: PSAD-Poste de Surveillance et d'Aide au Diagnostic. The development of such a sophisticated monitoring and data processing systems has emphasized the need for the addition of analysis and diagnosis assistance capabilities. Therefore, diagnostic knowledge based systems have been added to the functions monitored in PSAD: DIVA for turbine generators, and DIAPO for reactor coolant pumps. These systems were designed from a representation of the diagnostic reasoning process of experts and of the supporting knowledge. Diagnosis in both systems relies on an abduction reasoning process applied to component fault models and observations derived from their actual behavior, as provided by the monitoring functions. The basic theoretical elements of this diagnostic model are summarized in a first part. In a second part, DIVA and DIAPO specific elements are described

Study design requirements for RNA sequencing-based breast cancer diagnostics.

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Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

Diagnostics of enterprise bankruptcy occurrence probability in an anti-crisis management: modern approaches and classification of models

Directory of Open Access Journals (Sweden)

I.V. Zhalinska

2015-09-01

Full Text Available Diagnostics of enterprise bankruptcy occurrence probability is defined as an important tool ensuring the viability of an organization under conditions of unpredictable dynamic environment. The paper aims to define the basic features of diagnostics of bankruptcy occurrence probability models and their classification. The article grounds the objective increasing of crisis probability in modern enterprises where such increasing leads to the need to improve the efficiency of anti-crisis enterprise activities. The system of anti-crisis management is based on the subsystem of diagnostics of bankruptcy occurrence probability. Such a subsystem is the main one for further measures to prevent and overcome the crisis. The classification of existing models of enterprise bankruptcy occurrence probability has been suggested. The classification is based on methodical and methodological principles of models. The following main groups of models are determined: the models using financial ratios, aggregates and scores, the models of discriminated analysis, the methods of strategic analysis, informal models, artificial intelligence systems and the combination of the models. The classification made it possible to identify the analytical capabilities of each of the groups of models suggested.

Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia

Science.gov (United States)

Yang, Quan‐Jun; Zhao, Jiang‐Rong; Hao, Juan; Li, Bin; Huo, Yan; Han, Yong‐Long; Wan, Li‐Li; Li, Jie; Huang, Jinlu; Lu, Jin

2017-01-01

Abstract Background Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. Methods Eighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. Results Forty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88�

Can and should value-based pricing be applied to molecular diagnostics?

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Garau, Martina; Towse, Adrian; Garrison, Louis; Housman, Laura; Ossa, Diego

2013-01-01

Current pricing and reimbursement systems for diagnostics are not efficient. Prices for diagnostics are often driven by administrative practices and expected production cost. The purpose of the paper is to discuss how a value-based pricing framework being used to ensure efficient use and price of medicines could also be applied to diagnostics. Diagnostics not only facilitates health gain and cost savings, but also information to guide patients' decisions on interventions and their future 'behaviors'. For value assessment processes we recommend a two-part approach. Companion diagnostics introduced at the launch of the drug should be assessed through new drug assessment processes considering a broad range of value elements and a balanced analysis of diagnostic impacts. A separate diagnostic-dedicated committee using value-based pricing principles should review other diagnostics lying outside the companion diagnostics-and-drug 'at-launch' situation.

Model Diagnostics for the Department of Energy's Accelerated Climate Modeling for Energy (ACME) Project

Science.gov (United States)

Smith, B.

2015-12-01

In 2014, eight Department of Energy (DOE) national laboratories, four academic institutions, one company, and the National Centre for Atmospheric Research combined forces in a project called Accelerated Climate Modeling for Energy (ACME) with the goal to speed Earth system model development for climate and energy. Over the planned 10-year span, the project will conduct simulations and modeling on DOE's most powerful high-performance computing systems at Oak Ridge, Argonne, and Lawrence Berkeley Leadership Compute Facilities. A key component of the ACME project is the development of an interactive test bed for the advanced Earth system model. Its execution infrastructure will accelerate model development and testing cycles. The ACME Workflow Group is leading the efforts to automate labor-intensive tasks, provide intelligent support for complex tasks and reduce duplication of effort through collaboration support. As part of this new workflow environment, we have created a diagnostic, metric, and intercomparison Python framework, called UVCMetrics, to aid in the testing-to-production execution of the ACME model. The framework exploits similarities among different diagnostics to compactly support diagnosis of new models. It presently focuses on atmosphere and land but is designed to support ocean and sea ice model components as well. This framework is built on top of the existing open-source software framework known as the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT). Because of its flexible framework design, scientists and modelers now can generate thousands of possible diagnostic outputs. These diagnostics can compare model runs, compare model vs. observation, or simply verify a model is physically realistic. Additional diagnostics are easily integrated into the framework, and our users have already added several. Diagnostics can be generated, viewed, and manipulated from the UV-CDAT graphical user interface, Python command line scripts and programs

An indicator cell assay for blood-based diagnostics.

Directory of Open Access Journals (Sweden)

Samuel A Danziger

Full Text Available We have established proof of principle for the Indicator Cell Assay Platform™ (iCAP™, a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patients' blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS with 94% accuracy (p-Value = 3.81E-6 and correctly identified samples from a murine Huntington's disease model as non-carriers of ALS. Beyond the mouse model, in a preliminary human disease study, the iCAP detected early stage Alzheimer's disease with 72% cross-validated accuracy (p-Value = 3.10E-3. For both assays, iCAP features were enriched for disease-related genes, supporting the assay's relevance for disease research.

Real-time Modelling, Diagnostics and Optimised MPPT for Residential PV Systems

DEFF Research Database (Denmark)

Sera, Dezso

responsible for yield-reduction of residential photovoltaic systems. Combining the model calculations with measurements, a method to detect changes in the panels’ series resistance based on the slope of the I − V curve in the vicinity of open-circuit conditions and scaled to Standard Test Conditions (STC......The work documented in the thesis has been focused into two main sections. The first part is centred around Maximum Power Point Tracking (MPPT) techniques for photovoltaic arrays, optimised for fast-changing environmental conditions, and is described in Chapter 2. The second part is dedicated...... to diagnostic functions as an additional tool to maximise the energy yield of photovoltaic arrays (Chapter 4). Furthermore, mathematical models of PV panels and arrays have been developed and built (detailed in Chapter 3) for testing MPPT algorithms, and for diagnostic purposes. In Chapter 2 an overview...

Final-year diagnostic radiography students' perception of role models within the profession.

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Conway, Alinya; Lewis, Sarah; Robinson, John

2008-01-01

Within a clinical education setting, the value of role models and prescribed mentors can be seen as an important influence in shaping the student's future as a diagnostic radiographer. A study was undertaken to create a new understanding of how diagnostic radiography students perceive role models and professional behavior in the workforce. The study aimed to determine the impact of clinical education in determining modeling expectations, role model identification and attributes, and the integration of academic education and "hands-on" clinical practice in preparing diagnostic radiography students to enter the workplace. Thirteen final-year (third-year) diagnostic radiography students completed an hour-long interview regarding their experiences and perceptions of role models while on clinical placement. The key concepts that emerged illustrated that students gravitate toward radiographers who enjoy sharing practical experiences with students and are good communicators. Unique to diagnostic radiography, students made distinctions about the presence of role models in private versus public service delivery. This study gives insight to clinical educators in diagnostic radiography and wider allied health into how students perceive role models, interact with preceptors, and combine real-life experiences with formal learning.

The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme.

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Yizhao, Chen; Jianyang, Xia; Zhengguo, Sun; Jianlong, Li; Yiqi, Luo; Chengcheng, Gang; Zhaoqi, Wang

2015-11-06

As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ'E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ'E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models.

Building a satellite climate diagnostics data base for real-time climate monitoring

International Nuclear Information System (INIS)

Ropelewski, C.F.

1991-01-01

The paper discusses the development of a data base, the Satellite Climate Diagnostic Data Base (SCDDB), for real time operational climate monitoring utilizing current satellite data. Special attention is given to the satellite-derived quantities useful for monitoring global climate changes, the requirements of SCDDB, and the use of conventional meteorological data and model assimilated data in developing the SCDDB. Examples of prototype SCDDB products are presented. 10 refs

Vision-based building energy diagnostics and retrofit analysis using 3D thermography and building information modeling

Science.gov (United States)

Ham, Youngjib

The emerging energy crisis in the building sector and the legislative measures on improving energy efficiency are steering the construction industry towards adopting new energy efficient design concepts and construction methods that decrease the overall energy loads. However, the problems of energy efficiency are not only limited to the design and construction of new buildings. Today, a significant amount of input energy in existing buildings is still being wasted during the operational phase. One primary source of the energy waste is attributed to unnecessary heat flows through building envelopes during hot and cold seasons. This inefficiency increases the operational frequency of heating and cooling systems to keep the desired thermal comfort of building occupants, and ultimately results in excessive energy use. Improving thermal performance of building envelopes can reduce the energy consumption required for space conditioning and in turn provide building occupants with an optimal thermal comfort at a lower energy cost. In this sense, energy diagnostics and retrofit analysis for existing building envelopes are key enablers for improving energy efficiency. Since proper retrofit decisions of existing buildings directly translate into energy cost saving in the future, building practitioners are increasingly interested in methods for reliable identification of potential performance problems so that they can take timely corrective actions. However, sensing what and where energy problems are emerging or are likely to emerge and then analyzing how the problems influence the energy consumption are not trivial tasks. The overarching goal of this dissertation focuses on understanding the gaps in knowledge in methods for building energy diagnostics and retrofit analysis, and filling these gaps by devising a new method for multi-modal visual sensing and analytics using thermography and Building Information Modeling (BIM). First, to address the challenges in scaling and

Simulation-based expert system for nuclear reactor control and diagnostics. Progress report

International Nuclear Information System (INIS)

Lee, J.C.; Martin, W.R.

1986-01-01

This research concerns the development of artificial intelligence (AI) techniques suitable for application to the diagnostics and control of nuclear power plant systems. The overall objective of the current effort is to build a prototype simulation-based expert system for diagnosing accidents in nuclear reactors. The system is being designed to analyze plant data heuristically using fuzzy logic to form a set of hypotheses about a particular transient. Hypothesis testing, fault magnitude estimation and transient analysis is performed using simulation programs to model plant behavior. An adaptive learning technique has been developed for achieving accurate simulations of plant dynamics using low-order physical models of plant components. The results of the diagnostics and simulation analysis of the plant transient are to be analyzed by an expert system for final diagnoses and control guidance. To date, significant progress has been made toward achieving the primary goals of this project. Based on a critical safety functions approach, an overall design for the nuclear plant expert system has been developed. The methodology for performing diagnostic reasoning on plant signals has been developed and the algorithms implemented and tested. A methodology for utilizing the information contained in the physical models of plant components has also been developed. This work included the derivation of a unique Kalman filtering algorithm for using power plant data to systematically improve on-line simulations through the judicious adjustment of key model parameters. A few simulation models of key plant components have been developed and implemented to demonstrate the method on a realistic accident scenario. The chosen transient is a loss of feed flow exasperated by a stuck open relief valve, similar to the initiating event of the Three Mile Island Unit 2 accident in 1979

A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means

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Polak, Marike; De Rooij, Mark; Heiser, Willem J.

2012-01-01

In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) "criterion…

Data mining approach to model the diagnostic service management.

Science.gov (United States)

Lee, Sun-Mi; Lee, Ae-Kyung; Park, Il-Su

2006-01-01

Korea has National Health Insurance Program operated by the government-owned National Health Insurance Corporation, and diagnostic services are provided every two year for the insured and their family members. Developing a customer relationship management (CRM) system using data mining technology would be useful to improve the performance of diagnostic service programs. Under these circumstances, this study developed a model for diagnostic service management taking into account the characteristics of subjects using a data mining approach. This study could be further used to develop an automated CRM system contributing to the increase in the rate of receiving diagnostic services.

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

Diagnosing Diagnostic Models: From Von Neumann's Elephant to Model Equivalencies and Network Psychometrics

Science.gov (United States)

von Davier, Matthias

2018-01-01

This article critically reviews how diagnostic models have been conceptualized and how they compare to other approaches used in educational measurement. In particular, certain assumptions that have been taken for granted and used as defining characteristics of diagnostic models are reviewed and it is questioned whether these assumptions are the…

OpenID connect as a security service in Cloud-based diagnostic imaging systems

Science.gov (United States)

Ma, Weina; Sartipi, Kamran; Sharghi, Hassan; Koff, David; Bak, Peter

2015-03-01

The evolution of cloud computing is driving the next generation of diagnostic imaging (DI) systems. Cloud-based DI systems are able to deliver better services to patients without constraining to their own physical facilities. However, privacy and security concerns have been consistently regarded as the major obstacle for adoption of cloud computing by healthcare domains. Furthermore, traditional computing models and interfaces employed by DI systems are not ready for accessing diagnostic images through mobile devices. RESTful is an ideal technology for provisioning both mobile services and cloud computing. OpenID Connect, combining OpenID and OAuth together, is an emerging REST-based federated identity solution. It is one of the most perspective open standards to potentially become the de-facto standard for securing cloud computing and mobile applications, which has ever been regarded as "Kerberos of Cloud". We introduce OpenID Connect as an identity and authentication service in cloud-based DI systems and propose enhancements that allow for incorporating this technology within distributed enterprise environment. The objective of this study is to offer solutions for secure radiology image sharing among DI-r (Diagnostic Imaging Repository) and heterogeneous PACS (Picture Archiving and Communication Systems) as well as mobile clients in the cloud ecosystem. Through using OpenID Connect as an open-source identity and authentication service, deploying DI-r and PACS to private or community clouds should obtain equivalent security level to traditional computing model.

Modeling of novel diagnostic strategies for active tuberculosis - a systematic review: current practices and recommendations.

Directory of Open Access Journals (Sweden)

Alice Zwerling

Full Text Available The field of diagnostics for active tuberculosis (TB is rapidly developing. TB diagnostic modeling can help to inform policy makers and support complicated decisions on diagnostic strategy, with important budgetary implications. Demand for TB diagnostic modeling is likely to increase, and an evaluation of current practice is important. We aimed to systematically review all studies employing mathematical modeling to evaluate cost-effectiveness or epidemiological impact of novel diagnostic strategies for active TB.Pubmed, personal libraries and reference lists were searched to identify eligible papers. We extracted data on a wide variety of model structure, parameter choices, sensitivity analyses and study conclusions, which were discussed during a meeting of content experts.From 5619 records a total of 36 papers were included in the analysis. Sixteen papers included population impact/transmission modeling, 5 were health systems models, and 24 included estimates of cost-effectiveness. Transmission and health systems models included specific structure to explore the importance of the diagnostic pathway (n = 4, key determinants of diagnostic delay (n = 5, operational context (n = 5, and the pre-diagnostic infectious period (n = 1. The majority of models implemented sensitivity analysis, although only 18 studies described multi-way sensitivity analysis of more than 2 parameters simultaneously. Among the models used to make cost-effectiveness estimates, most frequent diagnostic assays studied included Xpert MTB/RIF (n = 7, and alternative nucleic acid amplification tests (NAATs (n = 4. Most (n = 16 of the cost-effectiveness models compared new assays to an existing baseline and generated an incremental cost-effectiveness ratio (ICER.Although models have addressed a small number of important issues, many decisions regarding implementation of TB diagnostics are being made without the full benefits of insight from mathematical

On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations

Directory of Open Access Journals (Sweden)

Yongning Wang

2013-04-01

Full Text Available This paper focuses on the diagnostic checking of vector ARMA (VARMA models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This is different from the traditional approach that employs only the squared series of standardized residuals. We then study two portmanteau statistics, called Q1(M and Q2(M, for model checking. A residual-based bootstrap method is provided and demonstrated as an effective way to approximate the diagnostic checking statistics. Simulations are used to compare the performance of the proposed statistics with other methods available in the literature. In addition, we also investigate the effect of GARCH shocks on checking a fitted VARMA model. Empirical sizes and powers of the proposed statistics are investigated and the results suggest a procedure of using jointly Q1(M and Q2(M in diagnostic checking. The bivariate time series of FTSE 100 and DAX index returns is used to illustrate the performance of the proposed portmanteau statistics. The results show that it is important to consider the cross-product series of standardized residuals and GARCH effects in model checking.

A diagnostic model to estimate winds and small-scale drag from Mars Observer PMIRR data

Science.gov (United States)

Barnes, J. R.

1993-01-01

Theoretical and modeling studies indicate that small-scale drag due to breaking gravity waves is likely to be of considerable importance for the circulation in the middle atmospheric region (approximately 40-100 km altitude) on Mars. Recent earth-based spectroscopic observations have provided evidence for the existence of circulation features, in particular, a warm winter polar region, associated with gravity wave drag. Since the Mars Observer PMIRR experiment will obtain temperature profiles extending from the surface up to about 80 km altitude, it will be extensively sampling middle atmospheric regions in which gravity wave drag may play a dominant role. Estimating the drag then becomes crucial to the estimation of the atmospheric winds from the PMIRR-observed temperatures. An interative diagnostic model based upon one previously developed and tested with earth satellite temperature data will be applied to the PMIRR measurements to produce estimates of the small-scale zonal drag and three-dimensional wind fields in the Mars middle atmosphere. This model is based on the primitive equations, and can allow for time dependence (the time tendencies used may be based upon those computed in a Fast Fourier Mapping procedure). The small-scale zonal drag is estimated as the residual in the zonal momentum equation; the horizontal winds having first been estimated from the meridional momentum equation and the continuity equation. The scheme estimates the vertical motions from the thermodynamic equation, and thus needs estimates of the diabatic heating based upon the observed temperatures. The latter will be generated using a radiative model. It is hoped that the diagnostic scheme will be able to produce good estimates of the zonal gravity wave drag in the Mars middle atmosphere, estimates that can then be used in other diagnostic or assimilation efforts, as well as more theoretical studies.

Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information

NARCIS (Netherlands)

L.M. Lamers (Leida)

1999-01-01

textabstractOBJECTIVE: To evaluate the predictive accuracy of the Diagnostic Cost Group (DCG) model using health survey information. DATA SOURCES/STUDY SETTING: Longitudinal data collected for a sample of members of a Dutch sickness fund. In the Netherlands the sickness

An intelligent diagnostic aid (ida) based upon the simulated and operational experience

International Nuclear Information System (INIS)

Zuenkov, M.; Poletykin, A.; Marsiletti, M.

1999-01-01

A diagnostic system has been developed addressed to timely trouble shooting in process plants during all operational modes. The theory of this diagnostic system is related with the usage of learning methods for automatic generation of knowledge bases. This approach enables the conversion of 'cause to effect' relations into 'effect to possible causes' ones. As applied to technical diagnosis, this allows one to generate troubleshooting rules of the kind 'symptoms to possible diagnosis' through 'failure to symptoms' relations. The problem of diagnostic rule generation is, thus, reduced to obtaining samples of the symptoms, that is the well known problem of failure modelling. The diagnostic rules are derived from the operation of a simulator according to the following procedure: (a) identification of all initiating events and of the corresponding operational modes; (b) simulation of all the selected transients for a time sufficient to identify the uniqueness of the plant response under each of the initiating event; (c) automatic generation of the set of diagnostic rules that, through the use of temporal logic and a filtering Expert System, associate the evolution of the process parameters and their derivatives to the initial perturbation. The plant model running under the simulator is built-up by means of the LEGO code. The LEGO code is a modular package developed at the Research and Development Department of the Italian National Electricity Board (CRA-ENEL) to facilitate modelling of the dynamics of fossil-fuelled and nuclear power plants. The LEGO code consists of a library of pre-programmed, pre-tested and pre-validated modules, that represent power plant components and a master program which allows the user to build-up a model by automatically interconnecting the modules in the arrangement determined by the modeler. The reference plant is Sampierdarena power station, that is a combined cycle plant dedicated to produce both electrical and heat power. The following

Nucleic acid-based diagnostics for infectious diseases in public health affairs.

Science.gov (United States)

Yu, Albert Cheung-Hoi; Vatcher, Greg; Yue, Xin; Dong, Yan; Li, Mao Hua; Tam, Patrick H K; Tsang, Parker Y L; Wong, April K Y; Hui, Michael H K; Yang, Bin; Tang, Hao; Lau, Lok-Ting

2012-06-01

Infectious diseases, mostly caused by bacteria and viruses but also a result of fungal and parasitic infection, have been one of the most important public health concerns throughout human history. The first step in combating these pathogens is to get a timely and accurate diagnosis at an affordable cost. Many kinds of diagnostics have been developed, such as pathogen culture, biochemical tests and serological tests, to help detect and fight against the causative agents of diseases. However, these diagnostic tests are generally unsatisfactory because they are not particularly sensitive and specific and are unable to deliver speedy results. Nucleic acid-based diagnostics, detecting pathogens through the identification of their genomic sequences, have shown promise to overcome the above limitations and become more widely adopted in clinical tests. Here we review some of the most popular nucleic acid-based diagnostics and focus on their adaptability and applicability to routine clinical usage. We also compare and contrast the characteristics of different types of nucleic acid-based diagnostics.

Intelligent DNA-based molecular diagnostics using linked genetic markers

Energy Technology Data Exchange (ETDEWEB)

Pathak, D.K.; Perlin, M.W.; Hoffman, E.P.

1994-12-31

This paper describes a knowledge-based system for molecular diagnostics, and its application to fully automated diagnosis of X-linked genetic disorders. Molecular diagnostic information is used in clinical practice for determining genetic risks, such as carrier determination and prenatal diagnosis. Initially, blood samples are obtained from related individuals, and PCR amplification is performed. Linkage-based molecular diagnosis then entails three data analysis steps. First, for every individual, the alleles (i.e., DNA composition) are determined at specified chromosomal locations. Second, the flow of genetic material among the individuals is established. Third, the probability that a given individual is either a carrier of the disease or affected by the disease is determined. The current practice is to perform each of these three steps manually, which is costly, time consuming, labor-intensive, and error-prone. As such, the knowledge-intensive data analysis and interpretation supersede the actual experimentation effort as the major bottleneck in molecular diagnostics. By examining the human problem solving for the task, we have designed and implemented a prototype knowledge-based system capable of fully automating linkage-based molecular diagnostics in X-linked genetic disorders, including Duchenne Muscular Dystrophy (DMD). Our system uses knowledge-based interpretation of gel electrophoresis images to determine individual DNA marker labels, a constraint satisfaction search for consistent genetic flow among individuals, and a blackboard-style problem solver for risk assessment. We describe the system`s successful diagnosis of DMD carrier and affected individuals from raw clinical data.

Development of the Model of the System of Managerial Diagnostics of the Enterprise on the Basis of Improvement of Diagnostic Purposes

Directory of Open Access Journals (Sweden)

Grzegorz Pawlowski

2017-11-01

Full Text Available The purpose of the article is to develop a model of the system of managerial diagnostics of the enterprise on the basis of the improvement of diagnostic purposes. The developed model of the system of managerial diagnostics of the enterprise is a set of subjects (owners, managers, investors, specialists, etc., objects (management system, resources, technology, methods (a set of methods and means, business indicators and criteria (parameters that, when interacting, provide the achievement (efficient and effective of the diagnostic objectives of the system of the objectives of managerial diagnostics of the enterprise, taking into account the compliance of its competitive strategy of the state of the environment function of direct action (competitors, customers, suppliers, mediators, and other contact audiences in the context of improving the efficiency and developing the management. It is determined that the system of goals of the model of the system of managerial diagnostics of the enterprise (taking into account the ensuring of the compliance of the system of management with strategic goals and tactical tasks form the following key diagnostic objectives that require improvement on the basis of business indicators (parameters, namely: 1 diagnostics of the effectiveness of controlling the internal business processes of the enterprise; 2 diagnostics of the effectiveness of the typical organizational structure of enterprise management; 3 diagnostics of the efficiency of standardization of the work of linear and functional managers and specialists at the enterprise; 4 diagnostics of the enterprise in the areas of vocational education, labor activity and motivation, innovation work and social development; 5 diagnostics of the level of conflict in the team at the enterprise; 6 diagnostics of efficiency of use of information technologies in the management of the enterprise. The prospect of further research in this area is to improve the complex system of

Exploration of Serum Proteomic Profiling and Diagnostic Model That Differentiate Crohn's Disease and Intestinal Tuberculosis.

Directory of Open Access Journals (Sweden)

Fenming Zhang

Full Text Available To explore the diagnostic models of Crohn's disease (CD, Intestinal tuberculosis (ITB and the differential diagnostic model between CD and ITB by analyzing serum proteome profiles.Serum proteome profiles from 30 CD patients, 21 ITB patients and 30 healthy controls (HCs were analyzed by using weak cationic magnetic beads combined with MALDI-TOF-MS technique to detect the differentially expressed proteins of serum samples. Three groups were made and compared accordingly: group of CD patients and HCs, group of ITB patients and HCs, group of CD patients and ITB patients. Wilcoxon rank sum test was used to screen the ten most differentiated protein peaks (P < 0.05. Genetic algorithm combining with support vector machine (SVM was utilized to establish the optimal diagnostic models for CD, ITB and the optimal differential diagnostic model between CD and ITB. The predictive effects of these models were evaluated by Leave one out (LOO cross validation method.There were 236 protein peaks differently expressed between group of CD patients and HCs, 305 protein peaks differently expressed between group of ITB patients and HCs, 332 protein peaks differently expressed between group of CD patients and ITB patients. Ten most differentially expressed peaks were screened out between three groups respectively (P < 0.05 to establish diagnostic models and differential diagnostic model. A diagnostic model comprising of four protein peaks (M/Z 4964, 3029, 2833, 2900 can well distinguish CD patients and HCs, with a specificity and sensitivity of 96.7% and 96.7% respectively. A diagnostic model comprising four protein peaks (M/Z 3030, 2105, 2545, 4210 can well distinguish ITB patients and HCs, with a specificity and sensitivity of 93.3% and 95.2% respectively. A differential diagnostic model comprising three potential biomarkers protein peaks (M/Z 4267, 4223, 1541 can well distinguish CD patients and ITB patients, with a specificity and sensitivity of 76.2% and 80

Methodology, models and algorithms in thermographic diagnostics

CERN Document Server

Živčák, Jozef; Madarász, Ladislav; Rudas, Imre J

2013-01-01

This book presents  the methodology and techniques of  thermographic applications with focus primarily on medical thermography implemented for parametrizing the diagnostics of the human body. The first part of the book describes the basics of infrared thermography, the possibilities of thermographic diagnostics and the physical nature of thermography. The second half includes tools of intelligent engineering applied for the solving of selected applications and projects. Thermographic diagnostics was applied to problematics of paraplegia and tetraplegia and carpal tunnel syndrome (CTS). The results of the research activities were created with the cooperation of the four projects within the Ministry of Education, Science, Research and Sport of the Slovak Republic entitled Digital control of complex systems with two degrees of freedom, Progressive methods of education in the area of control and modeling of complex object oriented systems on aircraft turbocompressor engines, Center for research of control of te...

Simulation-based diagnostics and control for nuclear power plants. Final report, April 15, 1992--April 14, 1995

International Nuclear Information System (INIS)

Lee, J.C.

1995-07-01

The objective of the project was to develop and test a simulation-based diagnostics and control guidance system that can be used to diagnose and manage off-normal transient events in nuclear power plants. The research has focused on developing two diagnostic approaches suitable for detection and identification of faults involving multiple components, subject to uncertainties in system modeling and observations. The first approach is based on a fuzzy logic framework that can diagnose binary failures using a single-failure diagnostic knowledge base. Construction of the binary-failure knowledge base is accomplished through the use of macroscopic conservation relationships and a fuzzy inference structure is developed to determine the magnitude of faults and the associated certainty. In the second diagnostic approach, an adaptive Kalman filter algorithm is derived to yield information on the type and magnitude of feasible component transitions that can account for system observations. To obtain the likelihood of feasible component failures or degradations, a general probabilistic formulation is developed where statistical distributions associated with component reliability data are explicitly represented. Testing of the diagnostic algorithms has been performed through the analysis of simulated transient events for light water reactor systems. Preliminary studies have been conducted to develop Monte Carlo algorithms for flexible control of transient events

Development and validation of a new turbocharger simulation methodology for marine two stroke diesel engine modelling and diagnostic applications

International Nuclear Information System (INIS)

Sakellaridis, Nikolaos F.; Raptotasios, Spyridon I.; Antonopoulos, Antonis K.; Mavropoulos, Georgios C.; Hountalas, Dimitrios T.

2015-01-01

Engine cycle simulation models are increasingly used in diesel engine simulation and diagnostic applications, reducing experimental effort. Turbocharger simulation plays an important role in model's ability to accurately predict engine performance and emissions. The present work describes the development of a complete engine simulation model for marine Diesel engines based on a new methodology for turbocharger modelling utilizing physically based meanline models for compressor and turbine. Simulation accuracy is evaluated against engine bench measurements. The methodology was developed to overcome the problem of limited experimental maps availability for compressor and turbine, often encountered in large marine diesel engine simulation and diagnostic studies. Data from the engine bench are used to calibrate the models, as well as to estimate turbocharger shaft mechanical efficiency. Closed cycle and gas exchange are modelled using an existing multizone thermodynamic model. The proposed methodology is applied on a 2-stroke marine diesel engine and its evaluation is based on the comparison of predictions against measured engine data. It is demonstrated model's ability to predict engine response with load variation regarding both turbocharger performance and closed cycle parameters, as well as NOx emission trends, making it an effective tool for both engine diagnostic and optimization studies. - Highlights: • Marine two stroke diesel engine simulation model. • Turbine and compressor simulation using physical meanline models. • Methodology to derive T/C component efficiency and T/C shaft mechanical efficiency. • Extensive validation of predictions against experimental data.

Soft x-ray virtual diagnostics for tokamak simulations

Science.gov (United States)

Kim, J. S.; Zhao, L.; Bogatu, I. N.; In, Y.; Turnbull, A.; Osborne, T.; Maraschek, M.; Comer, K.

2009-11-01

The numerical toolset, FAR-TECH Virtual Diagnostic Utility, for generating virtual experimental data based on theoretical models and comparing it with experimental data, has been developed for soft x-ray diagnostics on DIII-D. The virtual (or synthetic) soft x-ray signals for a sample DIII-D discharge are compared with the experimental data. The plasma density and temperature radial profiles needed in the soft x-ray signal modeling are obtained from experimental data, i.e., from Thomson scattering and electron cyclotron emission. The virtual soft x-ray diagnostics for the equilibriums have a good agreement with the experimental data. The virtual diagnostics based on an ideal linear instability also agree reasonably well with the experimental data. The agreements are good enough to justify the methodology presented here for utilizing virtual diagnostics for routine comparison of experimental data. The agreements also motivate further detailed simulations with improved physical models such as the nonideal magnetohydrodynamics contributions (resistivity, viscosity, nonaxisymmetric error fields, etc.) and other nonlinear effects, which can be tested by virtual diagnostics with various stability modeling.

Soft x-ray virtual diagnostics for tokamak simulations

International Nuclear Information System (INIS)

Kim, J. S.; Zhao, L.; Bogatu, I. N.; In, Y.; Turnbull, A.; Osborne, T.; Maraschek, M.; Comer, K.

2009-01-01

The numerical toolset, FAR-TECH Virtual Diagnostic Utility, for generating virtual experimental data based on theoretical models and comparing it with experimental data, has been developed for soft x-ray diagnostics on DIII-D. The virtual (or synthetic) soft x-ray signals for a sample DIII-D discharge are compared with the experimental data. The plasma density and temperature radial profiles needed in the soft x-ray signal modeling are obtained from experimental data, i.e., from Thomson scattering and electron cyclotron emission. The virtual soft x-ray diagnostics for the equilibriums have a good agreement with the experimental data. The virtual diagnostics based on an ideal linear instability also agree reasonably well with the experimental data. The agreements are good enough to justify the methodology presented here for utilizing virtual diagnostics for routine comparison of experimental data. The agreements also motivate further detailed simulations with improved physical models such as the nonideal magnetohydrodynamics contributions (resistivity, viscosity, nonaxisymmetric error fields, etc.) and other nonlinear effects, which can be tested by virtual diagnostics with various stability modeling.

Dynamics model for real time diagnostics of Triga RC-1 system

International Nuclear Information System (INIS)

Gadomski, A.M.; Nanni, V.; Meo, G.

1988-01-01

This paper presents dynamics model of TRIGA RC-1 reactor system. The model is dedicated to the real-time early fault detection during a reactor operation in one week exploitation cycle. The algorithms are specially suited for real-time, long time and also accelerated simulations with assumed diagnostic oriented accuracy. The approximations, modular structure, numerical methods and validation are discussed. The elaborated model will be build in the TRIGA Supervisor System and TRIGA Diagnostic Simulator

Field-based systems and advanced diagnostics

International Nuclear Information System (INIS)

Eryurek, E.

1998-01-01

Detection and characterization of anomalies in an industrial plant provide improved plant availability and plant efficiency thus yielding increased economic efficiency. Traditionally, detection of process anomalies is done at a high-level control system through various signal validation methods. These signal validation techniques rely on data from transmitters, which measure related process variables. Correlating these signals and deducing anomalies often is a very time consuming and a difficult task. Delays in detecting these anomalies can be costly during plant operation. Conventional centralized approaches also suffer from their dependence on detailed mathematical models of the processes. Smart field devices have the advantage of providing the necessary information directly to the control system as anomalies develop during operation of the processes enabling operators to take necessary steps to either prevent an unnecessary shut down before the problem becomes serious or schedule maintenance on the problematic loop. Fisher-Rosemount's PlantWeb TM architecture addresses 'Enhanced Measurement, Advanced Diagnostics and Control in the Field'. PlantWeb TM builds open process management systems by networking intelligent field devices, scalable control and systems platforms, and integrated modular software. A description of PlantWeb TM and how it improves various process conditions and reduces operating cost of a plant as well as a high level description of 'Enhanced Measurement, Advanced Diagnostics and Control in the Field', will be provided in this paper. PlantWeb TM is the trademark for Fisher-Rosemount's new field-based architecture that uses emerging technologies to utilize the power of intelligent field devices and deliver critical process and equipment information to improve plant performance. (author)

ISHM-oriented adaptive fault diagnostics for avionics based on a distributed intelligent agent system

Science.gov (United States)

Xu, Jiuping; Zhong, Zhengqiang; Xu, Lei

2015-10-01

In this paper, an integrated system health management-oriented adaptive fault diagnostics and model for avionics is proposed. With avionics becoming increasingly complicated, precise and comprehensive avionics fault diagnostics has become an extremely complicated task. For the proposed fault diagnostic system, specific approaches, such as the artificial immune system, the intelligent agents system and the Dempster-Shafer evidence theory, are used to conduct deep fault avionics diagnostics. Through this proposed fault diagnostic system, efficient and accurate diagnostics can be achieved. A numerical example is conducted to apply the proposed hybrid diagnostics to a set of radar transmitters on an avionics system and to illustrate that the proposed system and model have the ability to achieve efficient and accurate fault diagnostics. By analyzing the diagnostic system's feasibility and pragmatics, the advantages of this system are demonstrated.

Experience with model based display for advanced diagnostics and control

International Nuclear Information System (INIS)

Staffon, J.D.; Lindsay, R.W.

1989-01-01

A full color, model based display system based on the Rankine thermodynamic cycle has been developed for use at the Experimental Breeder Reactor II by plant operators, engineers, and experimenters. The displays generate a real time thermodynamic model of the plant processes on computer screens to provide a direct indication of the plant performance. Operators and others who view the displays are no longer required to mentally ''construct'' a model of the process before acting. The model based display accurately depicts the plant states. It appears to effectively reduce the gulf of evaluation, which should result in a significant reduction in human operator errors if this plant display approach is adopted by the nuclear industry. Preliminary comments from users, including operators, indicate an overwhelming acceptance of the display approach. The displays incorporate alarm functions as well as levels of detail ''paging'' capability. The system is developed on a computer network which allows the easy addition of displays as well as extra computers. Constructing a complete console can be rapid and inexpensive. 1 ref., 2 figs

Advances in nucleic acid-based diagnostics of bacterial infections

DEFF Research Database (Denmark)

Barken, Kim Bundvig; Haagensen, Janus Anders Juul; Tolker-Nielsen, Tim

2007-01-01

Methods for rapid detection of infectious bacteria and antimicrobial-resistant pathogens have evolved significantly over the last decade. Many of the new procedures are nucleic acid-based and replace conventional diagnostic methods like culturing which is time consuming especially with fastidious...... of these pathogens is important to isolate patients and prevent further spreading of the diseases. Newly developed diagnostic procedures are superior with respect to turnaround time, sensitivity and specificity. Methods like multiplex real time PCR and different array-based technologies offer the possibility...

GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

Energy Technology Data Exchange (ETDEWEB)

Patel, Kiran, E-mail: kkpatel@ipr.res.in; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

2016-11-15

The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

International Nuclear Information System (INIS)

Patel, Kiran; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

2016-01-01

The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

A memory-based model of posttraumatic stress disorder

DEFF Research Database (Denmark)

Rubin, David C.; Berntsen, Dorthe; Johansen, Marlene Klindt

2008-01-01

In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the Diagnostic and Statistical Manual of Mental Disorders (4th ed......., text rev.; American Psychiatric Association, 2000). The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs...

Diagnostics and modeling of high pressure streamer induced discharges

International Nuclear Information System (INIS)

Marode, E.; Dessante, P.; Deschamps, N.; Deniset, C.

2001-01-01

A great variety of diagnostic has been applied to gain information on basic parameter governing high pressure nonthermal filamentary plasmas (and namely streamer induced filamentary discharges). Apart from electrical diagnostics, gas discharge, in contrast with solid state physics, can greatly benefit from all optical techniques owing to its ''transparent'' state. Emission and absorption spectroscopy, as well as LIF or CARS (talk are given during this meeting on these two techniques) are among such specific possibilities. The figures gained from these diagnostic measurements has generally no meaning by itself. They must be worked out, by means of calibrated former results, and/or by using them as input in high pressure plasma modeling. Mixing experimental and modeling approach is necessary for reaching relevant physical knowledge of the high pressure filamentary discharges processes. It is shown that diffusion, and thermal space and time distribution, must fully be taken into account

Impact of Diagnosticity on the Adequacy of Models for Cognitive Diagnosis under a Linear Attribute Structure: A Simulation Study

Science.gov (United States)

de La Torre, Jimmy; Karelitz, Tzur M.

2009-01-01

Compared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM-based data with a linear attribute structure. The study utilizes a procedure…

Dynamics model for real time diagnostics of TRIGA RC-1 system

International Nuclear Information System (INIS)

Gadomski, A.M.; Nanni, V.; Meo, G.B.

1986-01-01

This paper presents dynamics model of TRIGA RC-1 reactor system. The model is dedicated to the real-time early fault detection during a reactor operation in one week exploitation cycle. The algorithms are specially suited for real-time, long time and also accelerated simulations with assumed diagnostic oriented accuracy. The approximations, modular structure, numerical methods and validation are discussed. The elaborated model will be build in the TRIGA Supervisory System and TRIGA Diagnostic Simulator. (author)

A web-based test of residents' skills in diagnostic radiology

International Nuclear Information System (INIS)

Finlay, K.; Norman, G.R.; Keane, D.R.; Stolberg, H.

2006-01-01

To develop an objective, Web-based tool for evaluating residents' knowledge of diagnostic radiology. We developed and tested a Web-based evaluation tool (the Diagnostic Radiology Skills Test) that consists of 3 tests, one in each of 3 domains of diagnostic radiology: chest, gastrointestinal, and musculoskeletal imaging. Each test comprises 30 cases representing a range of difficulty in the domain, including normal states, normal variants, typical cases of common diagnoses, and cases with more subtle findings. Cases are presented with a long menu of domain-specific possible diagnoses (response options), each coded for diagnostic appropriateness. Our subjects were 21 residents in postgraduate year (PGY) 2 to 5 and 11 experts in diagnostic radiology. Subjects accessed the tool via a Web site on our Web server. Residents test results were compared for reliability and validity across domain, case, and training level. In addition, results were correlated with commonly used established and objective evaluation tools. The tool demonstrated consistent monotonic improvement in performance with training level. It showed acceptable reliability in discriminating between residents at different performance levels, both within and across training levels (r = 0.53 within level and 0.69 across levels). Test results also had concurrent validity against the American College of Radiology In-Training Examination, a widely accepted objective assessment tool (r = 0.65, P < 0.01), and 2 Objective Structured Clinical Examinations (OSCEs) focusing on diagnostic skills (r = 0.78 and r 0.69, P < 0.01, respectively). Our study demonstrates the feasibility of a Web-based, standardized, objective assessment method for evaluating residents' performance. (author)

A diagnostic tree model for polytomous responses with multiple strategies.

Science.gov (United States)

Ma, Wenchao

2018-04-23

Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students' problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students' partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM. © 2018 The British Psychological Society.

Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance

Directory of Open Access Journals (Sweden)

Detang Zeng

2018-01-01

Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.

Interlaboratory diagnostic accuracy of a Salmonella specific PCR-based method

DEFF Research Database (Denmark)

Malorny, B.; Hoorfar, Jeffrey; Hugas, M.

2003-01-01

A collaborative study involving four European laboratories was conducted to investigate the diagnostic accuracy of a Salmonella specific PCR-based method, which was evaluated within the European FOOD-PCR project (http://www.pcr.dk). Each laboratory analysed by the PCR a set of independent obtained...... presumably naturally contaminated samples and compared the results with the microbiological culture method. The PCR-based method comprised a preenrichment step in buffered peptone water followed by a thermal cell lysis using a closed tube resin-based method. Artificially contaminated minced beef and whole......-based diagnostic methods and is currently proposed as international standard document....

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Science.gov (United States)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.

Science.gov (United States)

Theurer, M E; White, B J; Larson, R L; Schroeder, T C

2015-03-01

Bovine respiratory disease is an economically important syndrome in the beef industry, and diagnostic accuracy is important for optimal disease management. The objective of this study was to determine whether improving diagnostic sensitivity or specificity was of greater economic value at varied levels of respiratory disease prevalence by using Monte Carlo simulation. Existing literature was used to populate model distributions of published sensitivity, specificity, and performance (ADG, carcass weight, yield grade, quality grade, and mortality risk) differences among calves based on clinical respiratory disease status. Data from multiple cattle feeding operations were used to generate true ranges of respiratory disease prevalence and associated mortality. Input variables were combined into a single model that calculated estimated net returns for animals by diagnostic category (true positive, false positive, false negative, and true negative) based on the prevalence, sensitivity, and specificity for each iteration. Net returns for each diagnostic category were multiplied by the proportion of animals in each diagnostic category to determine group profitability. Apparent prevalence was categorized into low (increasing specificity created more rapid, positive change in net returns than increasing sensitivity. Improvement of diagnostic specificity, perhaps through a confirmatory test interpreted in series or pen-level diagnostics, can increase diagnostic value more than improving sensitivity. Mortality risk was the primary driver for net returns. The results from this study are important for determining future research priorities to analyze diagnostic techniques for bovine respiratory disease and provide a novel way for modeling diagnostic tests.

Component vibration of VVER-reactors - diagnostics and modelling

International Nuclear Information System (INIS)

Altstadt, E.; Scheffler, M.; Weiss, F.-P.

1995-01-01

Flow induced vibrations of reactor pressure vessel (RPV) internals (control element and core barrel motions) at VVER-440 reactors have led to the development of dedicated methods for on-line monitoring. These methods need a certain developed stage of the faults to be detected. To achieve a real sensitive early detection of mechanical faults of RPV internals, a theoretical vibration model was developed based on finite elements. The model comprises the whole primary circuit including the steam generators (SG). By means of that model all eigenfrequencies up to 30 Hz and the corresponding mode shapes were calculated for the normal vibration behaviour. Moreover the shift of eigenfrequencies and of amplitudes due to the degradation or to the failure of internal clamping and spring elements could be investigated, showing that a recognition of such degradations even inside the RPV is possible by pure excore vibration measurements. A true diagnostic, that is the identification of the failed component, might become possible because different faults influence different and well separated eigenfrequencies. (author)

Diagnostics for Linear Models With Functional Responses

OpenAIRE

Xu, Hongquan; Shen, Qing

2005-01-01

Linear models where the response is a function and the predictors are vectors are useful in analyzing data from designed experiments and other situations with functional observations. Residual analysis and diagnostics are considered for such models. Studentized residuals are defined and their properties are studied. Chi-square quantile-quantile plots are proposed to check the assumption of Gaussian error process and outliers. Jackknife residuals and an associated test are proposed to det...

Case-based reasoning diagnostic technique based on multi-attribute similarity

Energy Technology Data Exchange (ETDEWEB)

Makoto, Takahashi [Tohoku University, Miyagi (Japan); Akio, Gofuku [Okayama University, Okayamaa (Japan)

2014-08-15

Case-based diagnostic technique has been developed based on the multi-attribute similarity. Specific feature of the developed system is to use multiple attributes of process signals for similarity evaluation to retrieve a similar case stored in a case base. The present technique has been applied to the measurement data from Monju with some simulated anomalies. The results of numerical experiments showed that the present technique can be utilizes as one of the methods for a hybrid-type diagnosis system.

Companion diagnostics

DEFF Research Database (Denmark)

Jørgensen, Jan Trøst; Hersom, Maria

2016-01-01

of disease mechanisms, things are slowly changing. Within the last few years, we have seen an increasing number of predictive biomarker assays being developed to guide the use of targeted cancer drugs. This type of assay is called companion diagnostics and is developed in parallel to the drug using the drug-diagnostic...... co-development model. The development of companion diagnostics is a relatively new discipline and in this review, different aspects will be discussed including clinical and regulatory issues. Furthermore, examples of drugs, such as the ALK and PD-1/PD-L1 inhibitors, that have been approved recently....... Despite having discussed personalized medicine for more than a decade, we still see that most drug prescriptions for severe chronic diseases are largely based on 'trial and error' and not on solid biomarker data. However, with the advance of molecular diagnostics and a subsequent increased understanding...

Turning the Page: Advancing Paper-Based Microfluidics for Broad Diagnostic Application.

Science.gov (United States)

Gong, Max M; Sinton, David

2017-06-28

Infectious diseases are a major global health issue. Diagnosis is a critical first step in effectively managing their spread. Paper-based microfluidic diagnostics first emerged in 2007 as a low-cost alternative to conventional laboratory testing, with the goal of improving accessibility to medical diagnostics in developing countries. In this review, we examine the advances in paper-based microfluidic diagnostics for medical diagnosis in the context of global health from 2007 to 2016. The theory of fluid transport in paper is first presented. The next section examines the strategies that have been employed to control fluid and analyte transport in paper-based assays. Tasks such as mixing, timing, and sequential fluid delivery have been achieved in paper and have enabled analytical capabilities comparable to those of conventional laboratory methods. The following section examines paper-based sample processing and analysis. The most impactful advancement here has been the translation of nucleic acid analysis to a paper-based format. Smartphone-based analysis is another exciting development with potential for wide dissemination. The last core section of the review highlights emerging health applications, such as male fertility testing and wearable diagnostics. We conclude the review with the future outlook, remaining challenges, and emerging opportunities.

Forward modeling of JET polarimetry diagnostic

International Nuclear Information System (INIS)

Ford, Oliver; Svensson, J.; Boboc, A.; McDonald, D. C.

2008-01-01

An analytical Bayesian inversion of the JET interferometry line integrated densities into density profiles and associated uncertainty information, is demonstrated. These are used, with a detailed model of plasma polarimetry, to predict the rotation and ellipticity for the JET polarimeter. This includes the lateral channels, for over 45,000 time points over 1313 JET pulses. Good agreement with measured values is shown for a number of channels. For the remaining channels, the requirement of a more detailed model of the diagnostic is demonstrated. A commonly used approximation for the Cotton-Mouton effect on the lateral channels is also evaluated.

Physical Modeling for Anomaly Diagnostics and Prognostics, Phase II

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...

Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

Directory of Open Access Journals (Sweden)

Anson Chui Yan Tang

2012-01-01

Full Text Available In view of lacking a quantifiable traditional Chinese medicine (TCM pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group. A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

Means-End based Functional Modeling for Intelligent Control: Modeling and Experiments with an Industrial Heat Pump System

DEFF Research Database (Denmark)

Saleem, Arshad

2007-01-01

The purpose of this paper is to present a Multilevel Flow Model (MFM) of an industrial heat pump system and its use for diagnostic reasoning. MFM is functional modeling language supporting an explicit means-ends intelligent control strategy for large industrial process plants. The model is used...... in several diagnostic experiments analyzing different fault scenarios. The model and results of the experiments are explained and it is shown how MFM based intelligent modeling and automated reasoning can improve the fault diagnosis process significantly....

Simulation-based education leads to decreased use of fluoroscopy in diagnostic coronary angiography.

Science.gov (United States)

Prenner, Stuart B; Wayne, Diane B; Sweis, Ranya N; Cohen, Elaine R; Feinglass, Joe M; Schimmel, Daniel R

2017-08-02

The aim of this study is to determine whether simulation-based education (SBE) translates into reduced procedure time, radiation, and contrast use in actual clinical care. As a high volume procedure often performed by novice cardiology fellows, diagnostic coronary angiography represents an excellent target for SBE. Reports of SBE in interventional cardiology are limited and there is little understanding of the potential downstream clinical impact of these interventions. All diagnostic coronary angiograms performed at a single center between January 1, 2011 and June 30, 2015 were analyzed. Random effects linear regression models were used to compare outcomes between procedures performed by 12 cardiology fellows who underwent simulation-based training and those performed by 20 traditionally trained fellows. Thirty-two cardiology fellows performed 2,783 diagnostic coronary angiograms. Procedures performed by fellows trained with SBE were shorter (mean of 23.98 min vs. 24.94 min, P = 0.034) and were performed with decreased radiation (mean of 56,348 mGycm 2 vs. 66,120 mGycm 2 , P < 0.001). After controlling for year in training, procedure year, access site, and supervising attending physician, training on the simulator was independently associated with 117 fewer seconds of fluoroscopy time per procedure (P = 0.04). Diagnostic coronary angiography SBE is associated with decreased use of fluoroscopy in downstream clinical care. SBE may be a useful tool to reduce radiation exposure in the cardiac catheterization laboratory. © 2017 Wiley Periodicals, Inc.

A Framework to Debug Diagnostic Matrices

Science.gov (United States)

Kodal, Anuradha; Robinson, Peter; Patterson-Hine, Ann

2013-01-01

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.

Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

Directory of Open Access Journals (Sweden)

E. Solazzo

2017-09-01

Full Text Available The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3 by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study, allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL dynamics is pivotal to both models. In particular, (i the fluctuations slower than ∼ 1.5 days account for 70–85 % of the mean square error of the full (undecomposed ozone time series; (ii a recursive, systematic error with daily periodicity is detected, responsible for 10–20 % of the quadratic total error; (iii errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network

Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

Science.gov (United States)

Solazzo, Efisio; Hogrefe, Christian; Colette, Augustin; Garcia-Vivanco, Marta; Galmarini, Stefano

2017-09-01

The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ˜ 1.5 days account for 70-85 % of the mean square error of the full (undecomposed) ozone time series; (ii) a recursive, systematic error with daily periodicity is detected, responsible for 10-20 % of the quadratic total error; (iii) errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in

Study of a high power hydrogen beam diagnostic based on secondary electron emission

Energy Technology Data Exchange (ETDEWEB)

Sartori, E., E-mail: emanuele.sartori@igi.cnr.it [Consorzio RFX (CNR, ENEA, INFN, UNIPD, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova (Italy); Department of Management and Engineering, University di Padova strad. S. Nicola 3, 36100 Vicenza (Italy); Panasenkov, A. [NRC, Kurchatov Institute, 1, Kurchatov Sq, Moscow 123182 (Russian Federation); Veltri, P. [Consorzio RFX (CNR, ENEA, INFN, UNIPD, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova (Italy); INFN-LNL, viale dell’Università n. 2, 35020 Legnaro (Italy); Serianni, G.; Pasqualotto, R. [Consorzio RFX (CNR, ENEA, INFN, UNIPD, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova (Italy)

2016-11-15

In high power neutral beams for fusion, beam uniformity is an important figure of merit. Knowing the transverse power profile is essential during the initial phases of beam source operation, such as those expected for the ITER heating neutral beam (HNB) test facility. To measure it a diagnostic technique is proposed, based on the collection of secondary electrons generated by beam-surface and beam-gas interactions, by an array of positively biased collectors placed behind the calorimeter tubes. This measurement showed in the IREK test stand good proportionality to the primary beam current. To investigate the diagnostic performances in different conditions, we developed a numerical model of secondary electron emission, induced by beam particle impact on the copper tubes, and reproducing the cascade of secondary emission caused by successive electron impacts. The model is first validated against IREK measurements. It is then applied to the HNB case, to assess the locality of the measurement, the proportionality to the beam current density, and the influence of beam plasma.

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Science.gov (United States)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Zhang, J.; Bao, Q.

2016-12-01

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA infrastructure and technology have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences for three years since 2014. In the summer school, the students work on group research projects where CMDA provide datasets and analysis tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. A provenance management system for CMDA is developed to keep track of students' usages of CMDA, and to recommend datasets and analysis tools for their research topic. The provenance system also allows students to revisit their analysis results and share them with their group. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each use case developed is described and listed in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered from the use case, an analysis result such as output plots and data files, and a link to the exact analysis service call with all the input arguments filled. For example, one science use case is the evaluation of NCAR CAM5 model with MODIS total cloud fraction. The analysis service used is Difference Plot Service of

Climate Model Diagnostic Analyzer Web Service System

Science.gov (United States)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

2014-12-01

We have developed a cloud-enabled web-service system that empowers physics-based, multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks. The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the observational datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation, (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs, and (3) ECMWF reanalysis outputs for several environmental variables in order to supplement observational datasets. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, (4) the calculation of difference between two variables, and (5) the conditional sampling of one physical variable with respect to another variable. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA will be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. In order to support 30+ simultaneous users during the school, we have deployed CMDA to the Amazon cloud environment. The cloud-enabled CMDA will provide each student with a virtual machine while the user interaction with the system will remain the same

An XML-based loose-schema approach to managing diagnostic data in heterogeneous formats

Energy Technology Data Exchange (ETDEWEB)

Naito, O., E-mail: naito.osamu@jaea.go.j [Japan Atomic Energy Agency, 801-1 Mukouyama, Naka, Ibaraki 311-0193 (Japan)

2010-07-15

An approach to managing diagnostic data in heterogenous formats by using XML-based (eXtensible Markup Language) tag files is discussed. The tag file functions like header information in ordinary data formats but it is separate from the main body of data, human readable, and self-descriptive. Thus all the necessary information for reading the contents of data can be obtained without prior information or reading the data body itself. In this paper, modeling of diagnostic data and its representation in XML are studied and a very primitive implementation of this approach in C++ is presented. The overhead of manipulating XML in a proof-of-principle code was found to be small. The merits, demerits, and possible extensions of this approach are also discussed.

An XML-based loose-schema approach to managing diagnostic data in heterogeneous formats

International Nuclear Information System (INIS)

Naito, O.

2010-01-01

An approach to managing diagnostic data in heterogenous formats by using XML-based (eXtensible Markup Language) tag files is discussed. The tag file functions like header information in ordinary data formats but it is separate from the main body of data, human readable, and self-descriptive. Thus all the necessary information for reading the contents of data can be obtained without prior information or reading the data body itself. In this paper, modeling of diagnostic data and its representation in XML are studied and a very primitive implementation of this approach in C++ is presented. The overhead of manipulating XML in a proof-of-principle code was found to be small. The merits, demerits, and possible extensions of this approach are also discussed.

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.

Evidence-based development of a diagnosis-dependent therapy planning system and its implementation in modern diagnostic software.

Science.gov (United States)

Ahlers, M O; Jakstat, H A

2005-07-01

The prerequisite for structured individual therapy of craniomandibular dysfunctions is differential diagnostics. Suggestions for the structured recording of findings and their structured evaluation beyond the global diagnosis of "craniomandibular disorders" have been published. Only this structured approach enables computerization of the diagnostic process. The respective software is available for use in practice (CMDcheck for CMD screening, CMDfact for the differential diagnostics). Based on this structured diagnostics, knowledge-based therapy planning is also conceivable. The prerequisite for this would be a model of achieving consensus on the indicated forms of therapy related to the diagnosis. Therefore, a procedure for evidence-based achievement of consensus on suitable forms of therapy in CMD was developed first in multicentric cooperation, and then implemented in corresponding software. The clinical knowledge of experienced specialists was included consciously for the consensus achievement process. At the same time, anonymized mathematical statistical evaluations were used for control and objectification. Different examiners form different departments of several universities working independently of one another assigned the theoretically conceiveable therapeutic alternatives to the already published diagnostic scheme. After anonymization, the correlation of these assignments was then calculated mathematically. For achieving consensus in those cases for which no agreement initally existed, agreement was subsequently arrived at in the course of a consensus conference on the basis of literature evaluations and the discussion of clinical case examples. This consensus in turn finally served as the basis of a therapy planner implemented in the above-mentioned diagnostic software CMDfact. Contributing to quality assurance, the principles of programming this assistant as well as the interface for linking into the diagnostic software are documented and also published

Development of class model based on blood biochemical parameters as a diagnostic tool of PSE meat.

Science.gov (United States)

Qu, Daofeng; Zhou, Xu; Yang, Feng; Tian, Shiyi; Zhang, Xiaojun; Ma, Lin; Han, Jianzhong

2017-06-01

A fast, sensitive and effective method based on the blood biochemical parameters for the detection of PSE meat was developed in this study. A total of 200 pigs were slaughtered in the same slaughterhouse. Meat quality was evaluated by measuring pH, electrical conductivity and color at 45min, 2h and 24h after slaughtering in M. longissimus thoracis et lumborum (LD). Blood biochemical parameters were determined in blood samples collected during carcass bleeding. Principal component analysis (PCA) biplot showed that high levels of exsanguination Creatine Kinase, Lactate Dehydrogenase, Aspertate aminotransferase, blood glucose and lactate were associated with the PSE meat, and the five biochemical parameters were found to be good indicators of PSE meat Discriminant function analysis (DFA) was able to clearly identify PSE meat using the five biochemical parameters as input data, and the class model is an effective diagnostic tool in pigs which can be used to detect the PSE meat and reduce economic loss for the company. Copyright © 2017 Elsevier Ltd. All rights reserved.

A diagnostic expert system for a boiling water reactor using a dynamic model

International Nuclear Information System (INIS)

Sonoda, Y.; Kanemoto, S.; Imaruoka, H.

1990-01-01

A diagnostic expert system for abnormal disturbances in a BWR (Boiling Water Reactor) plant has been developed. The peculiar feature of this system is a diagnostic method which combines artificial intelligence technique with numerical analysis technique. The system has three diagnostic functions, 1) identification of anomaly position (device or sensor), 2) identification of anomaly mode and 3) identification of anomaly cause. Function 1) is implemented as follows. First, a hypothesis about anomaly propagation paths is built up by qualitative reasoning, using knowledge of causal relations among observed signals. Next, the abnormal device or sensor is found by applying model reference method and fuzzy set theory to test the hypothesis, using knowledge of plant structure and function, heuristic strategy of diagnosis and module type dynamic simulator. This simulator is composed of basic transfer function modules. The simulation model for the testing region is built up automatically, according to the requirement from the diagnostic task. Function 2) means identification of dynamic characteristics for an anomaly. It is realized by tuning model parameters so as to reproduce the abnormal signal behavior using the non-linear programing method. Function 3) derives probable anomaly causes from heuristic rules between anomaly mode and cause. A basic plant dynamic model was built up and adjusted to dynamic characteristics for one BWR plant (1100MWe). In order to verify the diagnostic functions of this system, data for several abnormal events was compiled by modifying this model. The diagnostic functions were proved useful, through the simulated abnormal data

Model-based reasoning technology for the power industry

International Nuclear Information System (INIS)

Touchton, R.A.; Subramanyan, N.S.; Naser, J.A.

1991-01-01

This paper reports on model-based reasoning which refers to an expert system implementation methodology that uses a model of the system which is being reasoned about. Model-based representation and reasoning techniques offer many advantages and are highly suitable for domains where the individual components, their interconnection, and their behavior is well-known. Technology Applications, Inc. (TAI), under contract to the Electric Power Research Institute (EPRI), investigated the use of model-based reasoning in the power industry including the nuclear power industry. During this project, a model-based monitoring and diagnostic tool, called ProSys, was developed. Also, an alarm prioritization system was developed as a demonstration prototype

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Directory of Open Access Journals (Sweden)

Jianfeng Zhang

2017-01-01

Full Text Available Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM. Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA, while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA, the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM is of great value, indicating the feasibility of digitalized tongue diagnosis.

Rapid development of paper-based fluidic diagnostic devices

CSIR Research Space (South Africa)

Smith, S

2014-11-01

Full Text Available We present a method for rapid and low-cost development of microfluidic diagnostic devices using paper-based techniques. Specifically, the implementation of fluidic flow paths and electronics on paper are demonstrated, with the goal of producing...

Alternative models of DSM-5 PTSD: Examining diagnostic implications.

Science.gov (United States)

Murphy, Siobhan; Hansen, Maj; Elklit, Ask; Yong Chen, Yoke; Raudzah Ghazali, Siti; Shevlin, Mark

2018-04-01

The factor structure of DSM-5 posttraumatic stress disorder (PTSD) has been extensively debated with evidence supporting the recently proposed seven-factor Hybrid model. However, despite myriad studies examining PTSD symptom structure few have assessed the diagnostic implications of these proposed models. This study aimed to generate PTSD prevalence estimates derived from the 7 alternative factor models and assess whether pre-established risk factors associated with PTSD (e.g., transportation accidents and sexual victimisation) produce consistent risk estimates. Seven alternative models were estimated within a confirmatory factor analytic framework using the PTSD Checklist for DSM-5 (PCL-5). Data were analysed from a Malaysian adolescent community sample (n = 481) of which 61.7% were female, with a mean age of 17.03 years. The results indicated that all models provided satisfactory model fit with statistical superiority for the Externalising Behaviours and seven-factor Hybrid models. The PTSD prevalence estimates varied substantially ranging from 21.8% for the DSM-5 model to 10.0% for the Hybrid model. Estimates of risk associated with PTSD were inconsistent across the alternative models, with substantial variation emerging for sexual victimisation. These findings have important implications for research and practice and highlight that more research attention is needed to examine the diagnostic implications emerging from the alternative models of PTSD. Copyright © 2017 Elsevier B.V. All rights reserved.

Product qualification: a barrier to point-of-care microfluidic-based diagnostics?

Science.gov (United States)

Tantra, Ratna; van Heeren, Henne

2013-06-21

One of the most exciting applications of microfluidics-based diagnostics is its potential use in next generation point-of-care (POC) devices. Many prototypes are already in existence, but, as of yet, few have achieved commercialisation. In this article, we consider the issue surrounding product qualification as a potential barrier to market success. The study discusses, in the context of POC microfluidics-based diagnostics, what the generic issues are and potential solutions. Our findings underline the need for a community-based effort that is necessary to speed up the product qualification process.

IGENPRO knowledge-based digital system for process transient diagnostics and management

International Nuclear Information System (INIS)

Morman, J.A.; Reifman, J.; Vitela, J.E.; Wei, T.Y.C.; Applequist, C.A.; Hippely, P.; Kuk, W.; Tsoukalas, L.H.

1998-01-01

Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R and D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R and D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management. (author)

IGENPRO knowledge-based digital system for process transient diagnostics and management

International Nuclear Information System (INIS)

Morman, J.A.; Reifman, J.; Wei, T.Y.C.

1997-01-01

Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R ampersand D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R ampersand D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management

Structure induction in diagnostic causal reasoning.

Science.gov (United States)

Meder, Björn; Mayrhofer, Ralf; Waldmann, Michael R

2014-07-01

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data. Our structure induction model of diagnostic reasoning takes into account the uncertainty regarding the underlying causal structure. A key prediction of the model is that diagnostic judgments should not only reflect the empirical probability of cause given effect but should also depend on the reasoner's beliefs about the existence and strength of the link between cause and effect. We confirmed this prediction in 2 studies and showed that our theory better accounts for human judgments than alternative theories of diagnostic reasoning. Overall, our findings support the view that in diagnostic reasoning people go "beyond the information given" and use the available data to make inferences on the (unobserved) causal rather than on the (observed) data level. (c) 2014 APA, all rights reserved.

Fetal Implications of Diagnostic Radiation Exposure During Pregnancy: Evidence-based Recommendations.

Science.gov (United States)

Rimawi, Bassam H; Green, Victoria; Lindsay, Michael

2016-06-01

The purpose of this article is to review the fetal and long-term implications of diagnostic radiation exposure during pregnancy. Evidence-based recommendations for radiologic imaging modalities utilizing exposure of diagnostic radiation during pregnancy, including conventional screen-film mammography, digital mammography, tomosynthesis, and contrast-enhanced mammography are described.

Silicon drift detector based X-ray spectroscopy diagnostic system for the study of non-thermal electrons at Aditya tokamak.

Science.gov (United States)

Purohit, S; Joisa, Y S; Raval, J V; Ghosh, J; Tanna, R; Shukla, B K; Bhatt, S B

2014-11-01

Silicon drift detector based X-ray spectrometer diagnostic was developed to study the non-thermal electron for Aditya tokamak plasma. The diagnostic was mounted on a radial mid plane port at the Aditya. The objective of diagnostic includes the estimation of the non-thermal electron temperature for the ohmically heated plasma. Bi-Maxwellian plasma model was adopted for the temperature estimation. Along with that the study of high Z impurity line radiation from the ECR pre-ionization experiments was also aimed. The performance and first experimental results from the new X-ray spectrometer system are presented.

A qualitative model construction method of nuclear power plants for effective diagnostic knowledge generation

International Nuclear Information System (INIS)

Yoshikawa, Shinji; Endou, Akira; Kitamura, Yoshinobu; Sasajima, Munehiko; Ikeda, Mitsuru; Mizoguchi, Riichiro.

1994-01-01

This paper discusses a method to construct a qualitative model of a nuclear power plant, in order to generate effective diagnostic knowledge. The proposed method is to prepare deep knowledge to be provided to a knowledge compiler based upon qualitative reasoning (QR). Necessity of knowledge compilation for nuclear plant diagnosis will be explained first, and conventionally-experienced problems in qualitative reasoning and a proposed method to overcome this problem is shown next, then a sample procedure to build a qualitative nuclear plant model is demonstrated. (author)

A tool for model based diagnostics of the AGS Booster

International Nuclear Information System (INIS)

Luccio, A.

1993-01-01

A model-based algorithmic tool was developed to search for lattice errors by a systematic analysis of orbit data in the AGS Booster synchrotron. The algorithm employs transfer matrices calculated with MAD between points in the ring. Iterative model fitting of the data allows one to find and eventually correct magnet displacements and angles or field errors. The tool, implemented on a HP-Apollo workstation system, has proved very general and of immediate physical interpretation

Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard.

Science.gov (United States)

Wang, Xian-Hong; Zhou, Xiao-Nong; Vounatsou, Penelope; Chen, Zhao; Utzinger, Jürg; Yang, Kun; Steinmann, Peter; Wu, Xiao-Hua

2008-06-11

Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.

Ultrafast Laser Diagnostics for Energetic-Material Ignition Mechanisms: Tools for Physics-Based Model Development.

Energy Technology Data Exchange (ETDEWEB)

Kearney, Sean Patrick; Jilek, Brook Anton; Kohl, Ian Thomas; Farrow, Darcie; Urayama, Junji

2014-11-01

We present the results of an LDRD project to develop diagnostics to perform fundamental measurements of material properties during shock compression of condensed phase materials at micron spatial scales and picosecond time scales. The report is structured into three main chapters, which each focus on a different diagnostic devel opment effort. Direct picosecond laser drive is used to introduce shock waves into thin films of energetic and inert materials. The resulting laser - driven shock properties are probed via Ultrafast Time Domain Interferometry (UTDI), which can additionally be used to generate shock Hugoniot data in tabletop experiments. Stimulated Raman scattering (SRS) is developed as a temperature diagnostic. A transient absorption spectroscopy setup has been developed to probe shock - induced changes during shock compressio n. UTDI results are presented under dynamic, direct - laser - drive conditions and shock Hugoniots are estimated for inert polystyrene samples and for the explosive hexanitroazobenzene, with results from both Sandia and Lawrence Livermore presented here. SRS a nd transient absorption diagnostics are demonstrated on static thin - film samples, and paths forward to dynamic experiments are presented.

Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?

Science.gov (United States)

Lamers, L M

Adequate risk adjustment is critical to the success of market-oriented health care reforms in many countries. Currently used risk adjusters based on demographic and diagnostic cost groups (DCGs) do not reflect expected costs accurately. This study examines the simultaneous predictive accuracy of inpatient and outpatient morbidity measures and prior costs. DCGs, pharmacy cost groups (PCGs), and prior year's costs improve the predictive accuracy of the demographic model substantially. DCGs and PCGs seem complementary in their ability to predict future costs. However, this study shows that the combination of DCGs and PCGs still leaves room for cream skimming.

Computer-based diagnostic decisionmaking.

Science.gov (United States)

Miller, R A

1987-12-01

The three decisionmaking aids described by the authors attack the generic problem of "see no evil, hear no evil, speak no evil"--improving the detection, diagnosis, and therapy of psychiatric disorders in the primary care setting. The three systems represent interventions at different steps in the process of providing appropriate care to psychiatric patients. The DSPW system of Robins and Marcus offers the potential of increasing the recognition of psychiatric disease in the physician's office. Politser's IDS program is representative of the sort of sophisticated microcomputer-based decisionmaking support tools that will become available to physicians in the not-too-distant future. Erdman's study of the impact of explanation capabilities on the acceptability of therapy recommending systems points out the need for careful scientific evaluations of features added to diagnostic and therapeutic systems.

Designing an activity-based costing model for a non-admitted prisoner healthcare setting.

Science.gov (United States)

Cai, Xiao; Moore, Elizabeth; McNamara, Martin

2013-09-01

To design and deliver an activity-based costing model within a non-admitted prisoner healthcare setting. Key phases from the NSW Health clinical redesign methodology were utilised: diagnostic, solution design and implementation. The diagnostic phase utilised a range of strategies to identify issues requiring attention in the development of the costing model. The solution design phase conceptualised distinct 'building blocks' of activity and cost based on the speciality of clinicians providing care. These building blocks enabled the classification of activity and comparisons of costs between similar facilities. The implementation phase validated the model. The project generated an activity-based costing model based on actual activity performed, gained acceptability among clinicians and managers, and provided the basis for ongoing efficiency and benchmarking efforts.

Single-chip microcomputer based protection, diagnostic and recording system for longwall shearers

Energy Technology Data Exchange (ETDEWEB)

Heyduk, A.; Krasucki, F. (Politechnika Slaska, Gliwice (Poland). Katedra Elektryfikacji i Automatyzacji Gornictwa)

1993-05-01

Presents a concept of microcomputer-aided operation, protection, diagnostics and recording for shearer loaders. A two-stage mathematical model is suggested and explained. The model represents the thermal processes that determine the overcurrent protection of drive motors. Circuits for monitoring fuses, supply voltages, contacts, relays, contactors and electro-hydraulic distributors with the use of transoptors are shown. Recording characteristic operation parameters of a shearer loader during the 5 minutes before a failure is proposed. Protection, diagnosis and control functions are suggested as additional functions to the microcomputer-aided system of shearer loader control being developed at the Silesian Technical University. The system is based on the NECmicroPD 78310 microprocessor. 10 refs.

Plasmon-Based Colorimetric Nanosensors for Ultrasensitive Molecular Diagnostics.

Science.gov (United States)

Tang, Longhua; Li, Jinghong

2017-07-28

Colorimetric detection of target analytes with high specificity and sensitivity is of fundamental importance to clinical and personalized point-of-care diagnostics. Because of their extraordinary optical properties, plasmonic nanomaterials have been introduced into colorimetric sensing systems, which provide significantly improved sensitivity in various biosensing applications. Here we review the recent progress on these plasmonic nanoparticles-based colorimetric nanosensors for ultrasensitive molecular diagnostics. According to their different colorimetric signal generation mechanisms, these plasmonic nanosensors are classified into two categories: (1) interparticle distance-dependent colorimetric assay based on target-induced forming cross-linking assembly/aggregate of plasmonic nanoparticles; and (2) size/morphology-dependent colorimetric assay by target-controlled growth/etching of the plasmonic nanoparticles. The sensing fundamentals and cutting-edge applications will be provided for each of them, particularly focusing on signal generation and/or amplification mechanisms that realize ultrasensitive molecular detection. Finally, we also discuss the challenge and give our future perspective in this emerging field.

Symptom based diagnostic system using artificial neural networks

International Nuclear Information System (INIS)

Santosh; Vinod, Gopika; Saraf, R.K.

2003-01-01

Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

Solar Prominence Modelling and Plasma Diagnostics at ALMA Wavelengths

Science.gov (United States)

Rodger, Andrew; Labrosse, Nicolas

2017-09-01

Our aim is to test potential solar prominence plasma diagnostics as obtained with the new solar capability of the Atacama Large Millimeter/submillimeter Array (ALMA). We investigate the thermal and plasma diagnostic potential of ALMA for solar prominences through the computation of brightness temperatures at ALMA wavelengths. The brightness temperature, for a chosen line of sight, is calculated using the densities of electrons, hydrogen, and helium obtained from a radiative transfer code under non-local thermodynamic equilibrium (non-LTE) conditions, as well as the input internal parameters of the prominence model in consideration. Two distinct sets of prominence models were used: isothermal-isobaric fine-structure threads, and large-scale structures with radially increasing temperature distributions representing the prominence-to-corona transition region. We compute brightness temperatures over the range of wavelengths in which ALMA is capable of observing (0.32 - 9.6 mm), however, we particularly focus on the bands available to solar observers in ALMA cycles 4 and 5, namely 2.6 - 3.6 mm (Band 3) and 1.1 - 1.4 mm (Band 6). We show how the computed brightness temperatures and optical thicknesses in our models vary with the plasma parameters (temperature and pressure) and the wavelength of observation. We then study how ALMA observables such as the ratio of brightness temperatures at two frequencies can be used to estimate the optical thickness and the emission measure for isothermal and non-isothermal prominences. From this study we conclude that for both sets of models, ALMA presents a strong thermal diagnostic capability, provided that the interpretation of observations is supported by the use of non-LTE simulation results.

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer.

Science.gov (United States)

Pu, Xia; Ye, Yuanqing; Wu, Xifeng

2014-01-01

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation. © 2013 American Cancer Society.

Development of electromagnetic induction diagnostics technology for condition based maintenance

International Nuclear Information System (INIS)

Mawatari, Shingo; Oeda, Kaoru; Yatogi, Hideo; Fukuchi, Taira; Ueno, Tadashi

2008-01-01

In ROKKASHO Reprocessing Plant (below, called 'RRP'), we have applied Condition Based Maintenance to rotating equipment with vibration diagnostics technology. However, a few rotating equipment are difficult to diagnose definitely, because have structural problems which exercise vibrational noise to peripheral and be impossible to install vibratory sensor. Electromagnetic induction diagnostics technology which measure magnetic fields to eddy current which is induced to rotary through static magnetic field, diagnose deterioration behavior such as abrasion and crack. As a result, it has possibilities to clear above problems. Therefore, we started our basic researches with this technology for Condition Based Maintenance. In this paper, it introduces basic data about 'Non-seal pump' that have installed in RRP. As a result, this technology is a possibility that be able to detect Condition Based Maintenance. (author)

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

Science.gov (United States)

Wang, W.; Rinke, A.; Moore, J. C.; Cui, X.; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D. M.; McGuire, A. D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

2016-02-01

We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for

Diagnostic markers of urothelial cancer based on DNA methylation analysis

International Nuclear Information System (INIS)

Chihara, Yoshitomo; Hirao, Yoshihiko; Kanai, Yae; Fujimoto, Hiroyuki; Sugano, Kokichi; Kawashima, Kiyotaka; Liang, Gangning; Jones, Peter A; Fujimoto, Kiyohide; Kuniyasu, Hiroki

2013-01-01

Early detection and risk assessment are crucial for treating urothelial cancer (UC), which is characterized by a high recurrence rate, and necessitates frequent and invasive monitoring. We aimed to establish diagnostic markers for UC based on DNA methylation. In this multi-center study, three independent sample sets were prepared. First, DNA methylation levels at CpG loci were measured in the training sets (tumor samples from 91 UC patients, corresponding normal-appearing tissue from these patients, and 12 normal tissues from age-matched bladder cancer-free patients) using the Illumina Golden Gate methylation assay to identify differentially methylated loci. Next, these methylated loci were validated by quantitative DNA methylation by pyrosequencing, using another cohort of tissue samples (Tissue validation set). Lastly, methylation of these markers was analyzed in the independent urine samples (Urine validation set). ROC analysis was performed to evaluate the diagnostic accuracy of these 12 selected markers. Of the 1303 CpG sites, 158 were hyper ethylated and 356 were hypo ethylated in tumor tissues compared to normal tissues. In the panel analysis, 12 loci showed remarkable alterations between tumor and normal samples, with 94.3% sensitivity and 97.8% specificity. Similarly, corresponding normal tissue could be distinguished from normal tissues with 76.0% sensitivity and 100% specificity. Furthermore, the diagnostic accuracy for UC of these markers determined in urine samples was high, with 100% sensitivity and 100% specificity. Based on these preliminary findings, diagnostic markers based on differential DNA methylation at specific loci can be useful for non-invasive and reliable detection of UC and epigenetic field defect

Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard.

Directory of Open Access Journals (Sweden)

Xian-Hong Wang

Full Text Available BACKGROUND: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. METHODOLOGY/PRINCIPAL FINDINGS: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA, from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. CONCLUSION/SIGNIFICANCE: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken

Comparison of adaptive statistical iterative reconstruction (ASiRTM) and model-based iterative reconstruction (VeoTM) for paediatric abdominal CT examinations: an observer performance study of diagnostic image quality

International Nuclear Information System (INIS)

Hultenmo, Maria; Caisander, Haakan; Mack, Karsten; Thilander-Klang, Anne

2016-01-01

The diagnostic image quality of 75 paediatric abdominal computed tomography (CT) examinations reconstructed with two different iterative reconstruction (IR) algorithms-adaptive statistical IR (ASiR TM ) and model-based IR (Veo TM )-was compared. Axial and coronal images were reconstructed with 70 % ASiR with the Soft TM convolution kernel and with the Veo algorithm. The thickness of the reconstructed images was 2.5 or 5 mm depending on the scanning protocol used. Four radiologists graded the delineation of six abdominal structures and the diagnostic usefulness of the image quality. The Veo reconstruction significantly improved the visibility of most of the structures compared with ASiR in all subgroups of images. For coronal images, the Veo reconstruction resulted in significantly improved ratings of the diagnostic use of the image quality compared with the ASiR reconstruction. This was not seen for the axial images. The greatest improvement using Veo reconstruction was observed for the 2.5 mm coronal slices. (authors)

A diagnostic expert system for NPP based on hybrid knowledge approach

International Nuclear Information System (INIS)

Yang, Joon On; Chang, Soon Heung

1989-01-01

This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. The structural and functional knowledge is represented by three fundamental primitives and five types of functions respectively. The behavioral knowledge is represented using constraints. The inference procedure is based on the human problem solving behavior modeled in HYPOSS. For the validation of HYPOSS, several tests have been performed based on the data produced by a plant simulator. The results of validation studies showed a good applicability of HYPOSS to the anomaly diagnosis of nuclear power plant

Vibration Based Diagnosis for Planetary Gearboxes Using an Analytical Model

Directory of Open Access Journals (Sweden)

Liu Hong

2016-01-01

Full Text Available The application of conventional vibration based diagnostic techniques to planetary gearboxes is a challenge because of the complexity of frequency components in the measured spectrum, which is the result of relative motions between the rotary planets and the fixed accelerometer. In practice, since the fault signatures are usually contaminated by noises and vibrations from other mechanical components of gearboxes, the diagnostic efficacy may further deteriorate. Thus, it is essential to develop a novel vibration based scheme to diagnose gear failures for planetary gearboxes. Following a brief literature review, the paper begins with the introduction of an analytical model of planetary gear-sets developed by the authors in previous works, which can predict the distinct behaviors of fault introduced sidebands. This analytical model is easy to implement because the only prerequisite information is the basic geometry of the planetary gear-set. Afterwards, an automated diagnostic scheme is proposed to cope with the challenges associated with the characteristic configuration of planetary gearboxes. The proposed vibration based scheme integrates the analytical model, a denoising algorithm, and frequency domain indicators into one synergistic system for the detection and identification of damaged gear teeth in planetary gearboxes. Its performance is validated with the dynamic simulations and the experimental data from a planetary gearbox test rig.

MRI-based diagnostic imaging of the intratemporal facial nerve

International Nuclear Information System (INIS)

Kress, B.; Baehren, W.

2001-01-01

Detailed imaging of the five sections of the full intratemporal course of the facial nerve can be achieved by MRI and using thin tomographic section techniques and surface coils. Contrast media are required for tomographic imaging of pathological processes. Established methods are available for diagnostic evaluation of cerebellopontine angle tumors and chronic Bell's palsy, as well as hemifacial spasms. A method still under discussion is MRI for diagnostic evaluation of Bell's palsy in the presence of fractures of the petrous bone, when blood volumes in the petrous bone make evaluation even more difficult. MRI-based diagnostic evaluation of the idiopatic facial paralysis currently is subject to change. Its usual application cannot be recommended for routine evaluation at present. However, a quantitative analysis of contrast medium uptake of the nerve may be an approach to improve the prognostic value of MRI in acute phases of Bell's palsy. (orig./CB) [de

Adaptive Technology Application for Vibration-Based Diagnostics of Roller Bearings on Industrial Plants

Directory of Open Access Journals (Sweden)

Mironov Aleksey

2014-09-01

Full Text Available Roller bearings are widely used in equipment of different applications; therefore, the issues related to the assessment of bearing technical state and localization of bearing faults are quite important and relevant. The reason is that technical state of a bearing is a critical component, which determines efficiency of a mechanism or equipment. For bearings inspection and diagnostics, various methods of vibration-based diagnostics are used. The adaptive technology for vibration-based diagnostics developed in „D un D centrs” is an effective tool for evaluation of technical state of bearings in operation compared to the existing SKF method.

A temporally and spatially resolved electron density diagnostic method for the edge plasma based on Stark broadening

Energy Technology Data Exchange (ETDEWEB)

Zafar, A., E-mail: zafara@ornl.gov [Department of Nuclear Engineering, North Carolina State University, Raleigh, North Carolina 27695 (United States); Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830 (United States); Martin, E. H.; Isler, R. C.; Caughman, J. B. O. [Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830 (United States); Shannon, S. C. [Department of Nuclear Engineering, North Carolina State University, Raleigh, North Carolina 27695 (United States)

2016-11-15

An electron density diagnostic (≥10{sup 10} cm{sup −3}) capable of high temporal (ms) and spatial (mm) resolution is currently under development at Oak Ridge National Laboratory. The diagnostic is based on measuring the Stark broadened, Doppler-free spectral line profile of the n = 6–2 hydrogen Balmer series transition. The profile is then fit to a fully quantum mechanical model including the appropriate electric and magnetic field operators. The quasi-static approach used to calculate the Doppler-free spectral line profile is outlined here and the results from the model are presented for H-δ spectra for electron densities of 10{sup 10}–10{sup 13} cm{sup −3}. The profile shows complex behavior due to the interaction between the magnetic substates of the atom.

A WAO - ARIA - GA²LEN consensus document on molecular-based allergy diagnostics.

Science.gov (United States)

Canonica, Giorgio Walter; Ansotegui, Ignacio J; Pawankar, Ruby; Schmid-Grendelmeier, Peter; van Hage, Marianne; Baena-Cagnani, Carlos E; Melioli, Giovanni; Nunes, Carlos; Passalacqua, Giovanni; Rosenwasser, Lanny; Sampson, Hugh; Sastre, Joaquin; Bousquet, Jean; Zuberbier, Torsten

2013-10-03

Molecular-based allergy (MA) diagnostics is an approach used to map the allergen sensitization of a patient at a molecular level, using purified natural or recombinant allergenic molecules (allergen components) instead of allergen extracts. Since its introduction, MA diagnostics has increasingly entered routine care, with currently more than 130 allergenic molecules commercially available for in vitro specific IgE (sIgE) testing.MA diagnostics allows for an increased accuracy in allergy diagnosis and prognosis and plays an important role in three key aspects of allergy diagnosis: (1) resolving genuine versus cross-reactive sensitization in poly-sensitized patients, thereby improving the understanding of triggering allergens; (2) assessing, in selected cases, the risk of severe, systemic versus mild, local reactions in food allergy, thereby reducing unnecessary anxiety for the patient and the need for food challenge testing; and (3) identifying patients and triggering allergens for specific immunotherapy (SIT).Singleplex and multiplex measurement platforms are available for MA diagnostics. The Immuno-Solid phase Allergen Chip (ISAC) is the most comprehensive platform currently available, which involves a biochip technology to measure sIgE antibodies against more than one hundred allergenic molecules in a single assay. As the field of MA diagnostics advances, future work needs to focus on large-scale, population-based studies involving practical applications, elucidation and expansion of additional allergenic molecules, and support for appropriate test interpretation. With the rapidly expanding evidence-base for MA diagnosis, there is a need for allergists to keep abreast of the latest information. The aim of this consensus document is to provide a practical guide for the indications, determination, and interpretation of MA diagnostics for clinicians trained in allergology.

Cancer physics: diagnostics based on damped cellular elastoelectrical vibrations in microtubules.

Science.gov (United States)

Pokorný, Jiří; Vedruccio, Clarbruno; Cifra, Michal; Kučera, Ondřej

2011-06-01

This paper describes a proposed biophysical mechanism of a novel diagnostic method for cancer detection developed recently by Vedruccio. The diagnostic method is based on frequency selective absorption of electromagnetic waves by malignant tumors. Cancer is connected with mitochondrial malfunction (the Warburg effect) suggesting disrupted physical mechanisms. In addition to decreased energy conversion and nonutilized energy efflux, mitochondrial malfunction is accompanied by other negative effects in the cell. Diminished proton space charge layer and the static electric field around the outer membrane result in a lowered ordering level of cellular water and increased damping of microtubule-based cellular elastoelectrical vibration states. These changes manifest themselves in a dip in the amplitude of the signal with the fundamental frequency of the nonlinear microwave oscillator-the core of the diagnostic device-when coupled to the investigated cancerous tissue via the near-field. The dip is not present in the case of healthy tissue.

DIFFERENTIAL DIAGNOSTICS MODEL RESEARCH BY MEANS OF THE POTENTIAL FUNCTIONS METHOD FOR NEUROLOGY DISEASES CLASSIFICATION

Directory of Open Access Journals (Sweden)

V. Z. Stetsyuk

2016-10-01

Full Text Available Informatization in medicine offers a lot of opportunities to enhance quality of medical support, accuracy of diagnosis and provides the use of accumulated experience. Modern program systems are utilized now as additional tools to get appropriate advice. This article offers the way to provide help for neurology department doctor of NCSH «OKHMATDYT» during diagnosis determining. It was decided to design the program system for this purpose based on differential diagnostic model. The key problems in differential diagnosis are symptoms similarity between each other in one disease group and the absence of key symptom. Therefore the differential diagnostic model is needed. It is constructed using the potential function method in characteristics space. This characteristics space is formed by 100-200 points - patients with their symptoms. The main feature of this method here is that the decision function is building during recognition step united with learning that became possible with the help of modern powerful computers.

Newborn Congenital Cytomegalovirus Screening Based on Clinical Manifestations and Evaluation of DNA-based Assays for In Vitro Diagnostics.

Science.gov (United States)

Fujii, Tomoyuki; Oka, Akira; Morioka, Ichiro; Moriuchi, Hiroyuki; Koyano, Shin; Yamada, Hideto; Saito, Shigeru; Sameshima, Hiroshi; Nagamatsu, Takeshi; Tsuchida, Shinya; Inoue, Naoki

2017-10-01

To establish a strategy for congenital cytomegalovirus (cCMV) screening and to establish confirmatory assays approved as in vitro diagnostics by the regulatory authorities, we evaluated the clinical risks and performance of diagnostic assays developed by commercial companies, since cCMV infection has significant clinical consequences. Newborns with clinical manifestations considered to be consequences of cCMV infection (n = 575) were screened for the presence of cytomegalovirus (CMV) DNA in urine specimens collected onto filter paper placed in their diapers using the polymerase chain reaction-based assay reported previously. Liquid urine specimens were obtained from all of 20 CMV-positive newborns and 107 of the CMV-negative newborns identified in the screening. We used these 127 specimens, as well as 12 from cCMV cases identified in a previous study and 41 from healthy newborns, to compare the performance of 2 commercial assays and 1 in-house assay. The risk-based screening allowed the identification of cCMV cases at least 10-fold more efficiently than our previous universal screening, although there appears to be a limit to the identification of asymptomatically infected newborns. Although CMV-specific IgM during pregnancy was found frequently in mothers of cCMV newborns, CMV-IgM alone is not an effective diagnostic marker. The urine-filter-based assay and the 3 diagnostic assays yielded identical results. Although risk-based and universal newborn screening strategies for cCMV infection each have their respective advantages and disadvantages, urine-filter-based assay followed by confirmatory in vitro diagnostics assays is able to identify cCMV cases efficiently.

Model-based sensor diagnosis

International Nuclear Information System (INIS)

Milgram, J.; Dormoy, J.L.

1994-09-01

Running a nuclear power plant involves monitoring data provided by the installation's sensors. Operators and computerized systems then use these data to establish a diagnostic of the plant. However, the instrumentation system is complex, and is not immune to faults and failures. This paper presents a system for detecting sensor failures using a topological description of the installation and a set of component models. This model of the plant implicitly contains relations between sensor data. These relations must always be checked if all the components are functioning correctly. The failure detection task thus consists of checking these constraints. The constraints are extracted in two stages. Firstly, a qualitative model of their existence is built using structural analysis. Secondly, the models are formally handled according to the results of the structural analysis, in order to establish the constraints on the sensor data. This work constitutes an initial step in extending model-based diagnosis, as the information on which it is based is suspect. This work will be followed by surveillance of the detection system. When the instrumentation is assumed to be sound, the unverified constraints indicate errors on the plant model. (authors). 8 refs., 4 figs

Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems

Energy Technology Data Exchange (ETDEWEB)

Najafi, Massieh; Auslander, David M.; Bartlett, Peter L.; Haves, Philip; Sohn, Michael D.

2010-05-30

Many studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However, in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models are imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.

Learning Diagnostic Diagrams in Transport-Based Data-Collection Systems

DEFF Research Database (Denmark)

Tran, Vu The; Eklund, Peter; Cook, Chris

2014-01-01

Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural...

Beam diagnostics based on virtual instrument technology for HLS

International Nuclear Information System (INIS)

Sun Baogen; Lu Ping; Wang Xiaohui; Wang Baoyun; Wang Junhua; Gu Liming; Fang Jia; Ma Tianji

2009-01-01

The paper introduce the beam diagnostics system using virtual instrument technology for Hefei Light Source (HLS), which includes a GPIB bus-based DCCT measurement system to measure the beam DC current and beam life, a VXIbus-based closed orbit measurement system to measure the beam position, a PCIbus-based beam profile measurement system to measure the beam profile and emittance, a GPIB-LAN based bunch length system using photoelectric method, and a Ethernet-based photon beam position measurement system. The software is programmed by LabVIEW, which reduces much developing work. (authors)

Analysis of Chronic Temporomandibular Disorders Based on the Latest Diagnostic Criteria

Directory of Open Access Journals (Sweden)

Svechtarov V.

2015-05-01

Full Text Available The objective of this study is to analyze the distribution of the most common diagnoses observed in patients with chronic temporomandibular disorders, based on the new diagnostic criteria (DC/TMD adopted in 2014. The previous Research Diagnostic Criteria (RDC/TMD adopted in 1992, consisted of three main groups of eight diagnostic subgroups and is currently transformed into two main groups and twelve subgroups, respectively. All subgroups correspond to the nomenclature of the ICD-10. The new clinical diagnostic indices are also modified. The analysis showed a prevalence of Pain-Related TMD compared with that of intra-articular disorders in ratio 57.89% to 42.10%. In Pain-Related TMD arthralgia was represented in 55% of cases; local myalgia - in 12%, myofascial pain - in 18%, myofascial pain with referral - in 14%, headache attributed to TMD - in 1%. In Intra-articular TMD disc displacement with reduction was found in 23% of the cases, disc displacement with reduction with intermittent locking - in 3%, disc displacement without reduction with limited opening - in 25%, disc displacement without reduction and without limited opening - in 8%. Degenerative diseases were found in 14.28%, and hypermobility and subluxations - in 26.98%. These analyzes differ and can only partly be compared with previous analyzes based on RDC system. The changes in the diagnostic criteria require new clinical studies in order to refine the picture of temporomandibular pathology in accordance with the modern views on the matter.

A WAO - ARIA - GA2LEN consensus document on molecular-based allergy diagnostics

DEFF Research Database (Denmark)

Canonica, Giorgio Walter; Ansotegui, Ignacio J; Pawankar, Ruby

2013-01-01

Molecular-based allergy (MA) diagnostics is an approach used to map the allergen sensitization of a patient at a molecular level, using purified natural or recombinant allergenic molecules (allergen components) instead of allergen extracts. Since its introduction, MA diagnostics has increasingly ...

Proposed Diagnostic Criteria for Smartphone Addiction.

Science.gov (United States)

Lin, Yu-Hsuan; Chiang, Chih-Lin; Lin, Po-Hsien; Chang, Li-Ren; Ko, Chih-Hung; Lee, Yang-Han; Lin, Sheng-Hsuan

2016-01-01

Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist's structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists' clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. The diagnostic criteria of smartphone addiction demonstrated the core symptoms "impaired control" paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment.

Proposed Diagnostic Criteria for Smartphone Addiction.

Directory of Open Access Journals (Sweden)

Yu-Hsuan Lin

Full Text Available Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria.We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist's structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists' clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy.Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1 six symptom criteria, (2 four functional impairment criteria and (3 exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%, while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use.The diagnostic criteria of smartphone addiction demonstrated the core symptoms "impaired control" paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment.

Prosthetic joint infection development of an evidence-based diagnostic algorithm.

Science.gov (United States)

Mühlhofer, Heinrich M L; Pohlig, Florian; Kanz, Karl-Georg; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; Kelch, Sarah; Harrasser, Norbert; von Eisenhart-Rothe, Rüdiger; Schauwecker, Johannes

2017-03-09

Increasing rates of prosthetic joint infection (PJI) have presented challenges for general practitioners, orthopedic surgeons and the health care system in the recent years. The diagnosis of PJI is complex; multiple diagnostic tools are used in the attempt to correctly diagnose PJI. Evidence-based algorithms can help to identify PJI using standardized diagnostic steps. We reviewed relevant publications between 1990 and 2015 using a systematic literature search in MEDLINE and PUBMED. The selected search results were then classified into levels of evidence. The keywords were prosthetic joint infection, biofilm, diagnosis, sonication, antibiotic treatment, implant-associated infection, Staph. aureus, rifampicin, implant retention, pcr, maldi-tof, serology, synovial fluid, c-reactive protein level, total hip arthroplasty (THA), total knee arthroplasty (TKA) and combinations of these terms. From an initial 768 publications, 156 publications were stringently reviewed. Publications with class I-III recommendations (EAST) were considered. We developed an algorithm for the diagnostic approach to display the complex diagnosis of PJI in a clear and logically structured process according to ISO 5807. The evidence-based standardized algorithm combines modern clinical requirements and evidence-based treatment principles. The algorithm provides a detailed transparent standard operating procedure (SOP) for diagnosing PJI. Thus, consistently high, examiner-independent process quality is assured to meet the demands of modern quality management in PJI diagnosis.

A pilot study using laser-based technique for non-invasive diagnostics of hypertensive conditions in mice

Science.gov (United States)

Litvinova, Karina S.; Ahmad, Shakil; Wang, Keqing; Rafailov, Ilya E.; Sokolovski, Sergei G.; Zhang, Lin; Rafailov, Edik U.; Ahmed, Asif

2016-02-01

Endothelial dysfunction is directly linked to preeclampsia, a maternal hypertensive condition that is life threating for both the mother and the baby. Epidemiological studies show that women with a history of pre-eclampsia have an elevated risk for cardiovascular disease. Here we report a new non-invasive diagnostic test for preeclampsia in mice that allows us to non-invasively assess the condition of the animals during the experiment and treatment in established models of preeclampsia. A laser-based multifunctional diagnostics system (LAKK-M) was chosen to carry out non-invasive analysis of multiple parameters. The device was used to simultaneously record the microcirculatory blood flow and oxygen saturation, as well as fluorescence levels of endogenous fluorophores. Preliminary experiments were conducted on adenoviral (Ad-)- mediated overexpression of sFlt-1 (Ad-sFlt-1) to mimic preeclampsialike symptoms in mice. The recorded data displayed the ability of the LAKK-M diagnostics device to detect significant differences in perfusion measurements between the control and Ad-sFlt-1 treatment. Preliminary results provide a potential avenue to employ these diagnostics technology to monitor and aid in maintaining control of live animal conditions throughout the experiment and treatment.

Design and development of AXUV-based soft X-ray diagnostic camera for Aditya Tokamak

International Nuclear Information System (INIS)

Raval, Jayesh V.; Purohit, Shishir; Joisa, Y. Shankara

2015-01-01

The hot tokamak plasma emits Soft X-rays (SXR) in accordance with the temperature and density which are important to be studied. A silicon photo diode array (AXUV16ELG, Opto-diode, USA) based prototype SXR diagnostics is designed and developed for ADITYA tokamak for the study of SXR radial intensity profile, internal disruption (Saw-tooth crash), MHD instabilities. The diagnostic is having an array of 16 detector of millimeter dimension in a linear configuration. Absolute Extreme Ultra Violate (AXUV) detector offers compact size, improved time response with considerably good quantum efficiency in the soft X-ray range (200 eV to 10 keV). The diagnostic is designed in competence with the ADITYA tokamak protocol. The diagnostic design geometry allows detector view the plasma through a slot hole (0.5 cm X 0.05 cm), 10 μm Beryllium foil filter window, cutting off energies below 750 eV. The diagnostic was installed on Aditya vacuum vessel at radial port no 7 enabling the diagnostics to view the core plasma. The spatial resolution designed for diagnostic configuration is 1.3 cm at plasma centre. The signal generated from SXR detector is acquired with a dedicated single board computer based data acquisition system at 50 kHz. The diagnostic took observation for the ohmically heated plasma. The data was then processed to construct spatial and temporal profile of SXR intensity for Aditya plasma. This information was complimentary to the Silicon surface barrier detector (SBD) based array for the same plasma discharge. The cross calibration between the two was considerably satisfactory under the assumptions considered. (author)

Smartphone-Based Mobile Detection Platform for Molecular Diagnostics and Spatiotemporal Disease Mapping.

Science.gov (United States)

Song, Jinzhao; Pandian, Vikram; Mauk, Michael G; Bau, Haim H; Cherry, Sara; Tisi, Laurence C; Liu, Changchun

2018-04-03

Rapid and quantitative molecular diagnostics in the field, at home, and at remote clinics is essential for evidence-based disease management, control, and prevention. Conventional molecular diagnostics requires extensive sample preparation, relatively sophisticated instruments, and trained personnel, restricting its use to centralized laboratories. To overcome these limitations, we designed a simple, inexpensive, hand-held, smartphone-based mobile detection platform, dubbed "smart-connected cup" (SCC), for rapid, connected, and quantitative molecular diagnostics. Our platform combines bioluminescent assay in real-time and loop-mediated isothermal amplification (BART-LAMP) technology with smartphone-based detection, eliminating the need for an excitation source and optical filters that are essential in fluorescent-based detection. The incubation heating for the isothermal amplification is provided, electricity-free, with an exothermic chemical reaction, and incubation temperature is regulated with a phase change material. A custom Android App was developed for bioluminescent signal monitoring and analysis, target quantification, data sharing, and spatiotemporal mapping of disease. SCC's utility is demonstrated by quantitative detection of Zika virus (ZIKV) in urine and saliva and HIV in blood within 45 min. We demonstrate SCC's connectivity for disease spatiotemporal mapping with a custom-designed website. Such a smart- and connected-diagnostic system does not require any lab facilities and is suitable for use at home, in the field, in the clinic, and particularly in resource-limited settings in the context of Internet of Medical Things (IoMT).

Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A.; Chasensky, T.M.

1996-01-01

Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use thermal hydraulic (T-H) signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance.To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. A full-scope operator training simulator representing the Commonwealth Edison Braidwood nuclear power plant is being used both as the source of development data and as the means to evaluate the advantages of the proposed diagnostic system. This is an ongoing multi-year project and this paper presents the results to date of the CRADA phase

The graphics-based human interface to the DISYS diagnostic/control guidance system at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Chavez, C.; Kamarthi, S.; Dharap, S.; Lindsay, R.W.; Staffon, J.

1990-01-01

An initial graphics based interface to the real-time DISYS diagnostic system has been developed using the multi-tasking capabilities of the UNIX operating system and X-Windows 11 Xlib graphics library. This system is interfaced to live plant data at the Experimental Breeder Reactor (EBR-2) for the Argon Cooling System of fuel handling operations and the steam plant. The interface includes an intelligent process schematic which highlights problematic components and sensors based on the results of the diagnostic computations. If further explanation of a faulted component is required, the user can call up a display of the diagnostic computations presented in a tree-like diagram. Numerical data on the process schematic and optional diagnostic tree are updated as new real-time data becomes available. The initial X-Windows 11 based interface will be further enhanced using VI Corporation DATAVIEWS graphical data base software. 5 refs., 6 figs

High-Throughput Array Instrument for DNA-Based Breast Cancer Diagnostics

National Research Council Canada - National Science Library

Swerdlow, Harold

2000-01-01

...) for breast-cancer diagnostics. These methods are based upon large numbers of discrete DNA spots placed on glass microscope slides typically, and hybridized to a probe derived from a tIssue or blood sample...

Development of network based control and data acquisition systems for diagnostics using CCD detectors. Application to LHD experiments

International Nuclear Information System (INIS)

Kado, Shinichiro; Nakanishi, Hideya; Ida, Katsumi; Kojima, Mamoru

2000-01-01

The needs of CCD detectors as a plasma diagnostic tool have recently been increased. However, many CCD providers have developed their own controlling systems, and it is difficult to customized the usages in order to make them applicable to the network-based data acquisition clusters which consist of various sorts of diagnostics. This paper presents the development of systems in which CCD detectors are controlled and the data are acquired through networks. By making use of the Client/Server (C/S) model in the Windows NT operating system and block transfer method via shared memory relevant to the model, the dependence on the hardware is hidden by the server service, CCD list sequencer. The client program is designed for the LHD (Large Helical Device) discharge operation sequences and the data acquisition system. (author)

Prioritic directions of inculcation of diagnostic equipment at NPP

International Nuclear Information System (INIS)

Morozov, V.I.

2000-01-01

The diagnostic provision creates the conditions for increasing the safety and reliability of the NPP functioning, technical service and maintenance by the actual state. With an account of the large number of the NPP equipment elements, limitedness of financial resources, different technical-economical effect from diagnostics determination of the priority directions for introduction of technical diagnostic means into the operational practice is one of the main factors. The method for determining the above-mentioned priorities is proposed. The main aspects of the method and mathematical models, based on the logical-probabilistic modeling, are presented. The essence of the method consists in ranging the technical-economical effect from introduction of various factors of the diagnostic equipment [ru

Systematic construction of qualitative physics-based rules for process diagnostics

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.

1995-01-01

A novel first-principles-based expert system is proposed for on-line detection and identification of faulty component candidates during incipient off-normal process operations. The system performs function-oriented diagnostics and can be reused for diagnosing single-component failures in different processes and different plants through the provision of the appropriate process schematics information. The function-oriented and process-independent diagnostic features of the proposed expert system are achieved by constructing a knowledge base containing three distinct types of information, qualitative balance equation rules, functional classification of process components, and the process piping and instrumentation diagram. The various types of qualitative balance equation rules for processes utilizing single-phase liquids are derived and their usage is illustrated through simulation results of a realistic process in a nuclear power plant

Residual heat removal system diagnostic advisor

International Nuclear Information System (INIS)

Tripp, L.

1991-01-01

This paper reports on the Residual Heat Removal System (RHRS) Diagnostic Advisor which is an expert system designed to alert the operators to abnormal conditions that exits in the RHRS and offer advice about the cause of the abnormal conditions. The Advisor uses a combination of rule-based and model-based diagnostic techniques to perform its functions. This diagnostic approach leads to a deeper understanding of the RHRS by the Advisor and consequently makes it more robust to unexpected conditions. The main window of the interactive graphic display is a schematic diagram of the RHRS piping system. When a conclusion about a failed component can be reached, the operator can bring up windows that describe the failure mode of the component and a brief explanation about how the Advisor arrived at its conclusion

Coronary Heart Disease Preoperative Gesture Interactive Diagnostic System Based on Augmented Reality.

Science.gov (United States)

Zou, Yi-Bo; Chen, Yi-Min; Gao, Ming-Ke; Liu, Quan; Jiang, Si-Yu; Lu, Jia-Hui; Huang, Chen; Li, Ze-Yu; Zhang, Dian-Hua

2017-08-01

Coronary heart disease preoperative diagnosis plays an important role in the treatment of vascular interventional surgery. Actually, most doctors are used to diagnosing the position of the vascular stenosis and then empirically estimating vascular stenosis by selective coronary angiography images instead of using mouse, keyboard and computer during preoperative diagnosis. The invasive diagnostic modality is short of intuitive and natural interaction and the results are not accurate enough. Aiming at above problems, the coronary heart disease preoperative gesture interactive diagnostic system based on Augmented Reality is proposed. The system uses Leap Motion Controller to capture hand gesture video sequences and extract the features which that are the position and orientation vector of the gesture motion trajectory and the change of the hand shape. The training planet is determined by K-means algorithm and then the effect of gesture training is improved by multi-features and multi-observation sequences for gesture training. The reusability of gesture is improved by establishing the state transition model. The algorithm efficiency is improved by gesture prejudgment which is used by threshold discriminating before recognition. The integrity of the trajectory is preserved and the gesture motion space is extended by employing space rotation transformation of gesture manipulation plane. Ultimately, the gesture recognition based on SRT-HMM is realized. The diagnosis and measurement of the vascular stenosis are intuitively and naturally realized by operating and measuring the coronary artery model with augmented reality and gesture interaction techniques. All of the gesture recognition experiments show the distinguish ability and generalization ability of the algorithm and gesture interaction experiments prove the availability and reliability of the system.

Nuclear based diagnostics in high-power laser applications

Energy Technology Data Exchange (ETDEWEB)

Guenther, Marc; Sonnabend, Kerstin; Harres, Knut; Otten, Anke; Roth, Markus [TU Darmstadt, Institut fuer Kernphysik, Darmstadt (Germany); Vogt, Karsten; Bagnoud, Vincent [GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt (Germany)

2010-07-01

High-power lasers allow focused intensities of >10{sup 18} W/cm{sup 2}. During the laser-solid interaction, an intense relativistic electron current is injected from the plasma into the target. One challenge is to characterize the electron dynamic close to the interaction region. Moreover, next generation high-power laser proton acceleration leads to high proton fluxes, which require novel, nuclear diagnostic techniques. We present an activation-based nuclear pyrometry for the investigation of electrons generated in relativistic laser-solid interactions. We use novel activation targets consisting of several isotopes with different photo-neutron disintegration thresholds. The electrons are decelerated inside the target via bremsstrahlung processes. The high-energy bremsstrahlung induces photo-nuclear reactions. In this energy range no disturbing low energy effects are important. Via the pyrometry the Reconstruction of the absolute yield, spectral and spatial distribution of the electrons is possible. For the characterization of proton beams we present a nuclear activation imaging spectroscopy (NAIS). The diagnostic is based on proton-neutron disintegration reactions of copper stacked in consecutive layers. An autoradiography of copper layers leads to spectrally and spatially reconstruction of the beam profile.

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

Science.gov (United States)

Wang, A.; Moore, J.C.; Cui, Xingquan; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D.M.; McGuire, A.D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

2016-01-01

 We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future

Maximum likelihood estimation of signal detection model parameters for the assessment of two-stage diagnostic strategies.

Science.gov (United States)

Lirio, R B; Dondériz, I C; Pérez Abalo, M C

1992-08-01

The methodology of Receiver Operating Characteristic curves based on the signal detection model is extended to evaluate the accuracy of two-stage diagnostic strategies. A computer program is developed for the maximum likelihood estimation of parameters that characterize the sensitivity and specificity of two-stage classifiers according to this extended methodology. Its use is briefly illustrated with data collected in a two-stage screening for auditory defects.

STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models

DEFF Research Database (Denmark)

Kostoulas, Polychronis; Nielsen, Søren S.; Branscum, Adam J.

2017-01-01

The Standards for the Reporting of Diagnostic Accuracy (STARD) statement, which was recently updated to the STARD2015 statement, was developed to encourage complete and transparent reporting of test accuracy studies. Although STARD principles apply broadly, the checklist is limited to studies......-BLCM (Standards for Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models), will facilitate improved quality of reporting on the design, conduct and results of diagnostic accuracy studies that use Bayesian latent class models....

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

Science.gov (United States)

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Advanced Laser-Based Techniques for Gas-Phase Diagnostics in Combustion and Aerospace Engineering.

Science.gov (United States)

Ehn, Andreas; Zhu, Jiajian; Li, Xuesong; Kiefer, Johannes

2017-03-01

Gaining information of species, temperature, and velocity distributions in turbulent combustion and high-speed reactive flows is challenging, particularly for conducting measurements without influencing the experimental object itself. The use of optical and spectroscopic techniques, and in particular laser-based diagnostics, has shown outstanding abilities for performing non-intrusive in situ diagnostics. The development of instrumentation, such as robust lasers with high pulse energy, ultra-short pulse duration, and high repetition rate along with digitized cameras exhibiting high sensitivity, large dynamic range, and frame rates on the order of MHz, has opened up for temporally and spatially resolved volumetric measurements of extreme dynamics and complexities. The aim of this article is to present selected important laser-based techniques for gas-phase diagnostics focusing on their applications in combustion and aerospace engineering. Applicable laser-based techniques for investigations of turbulent flows and combustion such as planar laser-induced fluorescence, Raman and Rayleigh scattering, coherent anti-Stokes Raman scattering, laser-induced grating scattering, particle image velocimetry, laser Doppler anemometry, and tomographic imaging are reviewed and described with some background physics. In addition, demands on instrumentation are further discussed to give insight in the possibilities that are offered by laser flow diagnostics.

Array-based sensing using nanoparticles: an alternative approach for cancer diagnostics.

Science.gov (United States)

Le, Ngoc D B; Yazdani, Mahdieh; Rotello, Vincent M

2014-07-01

Array-based sensing using nanoparticles (NPs) provides an attractive alternative to specific biomarker-focused strategies for cancer diagnosis. The physical and chemical properties of NPs provide both the recognition and transduction capabilities required for biosensing. Array-based sensors utilize a combined response from the interactions between sensors and analytes to generate a distinct pattern (fingerprint) for each analyte. These interactions can be the result of either the combination of multiple specific biomarker recognition (specific binding) or multiple selective binding responses, known as chemical nose sensing. The versatility of the latter array-based sensing using NPs can facilitate the development of new personalized diagnostic methodologies in cancer diagnostics, a necessary evolution in the current healthcare system to better provide personalized treatments. This review will describe the basic principle of array-based sensors, along with providing examples of both invasive and noninvasive samples used in cancer diagnosis.

How Preclinical Models Evolved to Resemble the Diagnostic Criteria of Drug Addiction.

Science.gov (United States)

Belin-Rauscent, Aude; Fouyssac, Maxime; Bonci, Antonello; Belin, David

2016-01-01

Drug addiction is a complex neuropsychiatric disorder that affects a subset of the individuals who take drugs. It is characterized by maladaptive drug-seeking habits that are maintained despite adverse consequences and intense drug craving. The pathophysiology and etiology of addiction is only partially understood despite extensive research because of the gap between current preclinical models of addiction and the clinical criteria of the disorder. This review presents a brief overview, based on selected methodologies, of how behavioral models have evolved over the last 50 years to the development of recent preclinical models of addiction that more closely mimic diagnostic criteria of addiction. It is hoped that these new models will increase our understanding of the complex neurobiological mechanisms whereby some individuals switch from controlled drug use to compulsive drug-seeking habits and relapse to these maladaptive habits. Additionally, by paving the way to bridge the gap that exists between biobehavioral research on addiction and the human situation, these models may provide new perspectives for the development of novel and effective therapeutic strategies for drug addiction. Published by Elsevier Inc.

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment.

Science.gov (United States)

Yamaguchi, Kazuhiro; Okada, Kensuke

2018-01-01

A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students' achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed.

Paper based diagnostics for personalized health care: Emerging technologies and commercial aspects.

Science.gov (United States)

Mahato, Kuldeep; Srivastava, Ananya; Chandra, Pranjal

2017-10-15

Personalized health care (PHC) is being appreciated globally to combat clinical complexities underlying various metabolic or infectious disorders including diabetes, cardiovascular, communicable diseases etc. Effective diagnoses majorly depend on initial identification of the causes which are nowadays being practiced in disease-oriented approach, where personal health profile is often overlooked. The adoption of PHC has shown significantly improved diagnoses in various conditions including emergency, ambulatory, and remote area. PHC includes personalized health monitoring (PHM), which is its integral part and may provide valuable information's on various clinical conditions. In PHC, bio-fluids are analyzed using various diagnostic devices including lab based equipment and biosensors. Among all types of biosensing systems, paper based biosensors are commercially attracted due to its portability, easy availability, cheaper manufacturing cost, and transportability. Not only these, various intrinsic properties of paper has facilitated the development of paper based miniaturized sensors, which has recently gained ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment free, Deliverable to all end-users) status for point of care diagnosis in miniaturized settings. In this review, importance of paper based biosensors and their compatibility for affordable and low cost diagnostics has been elaborated with various examples. Limitations and strategies to overcome the challenges of paper biosensor have also been discussed. We have provided elaborated tables which describe the types, model specifications, sensing mechanisms, target biomarkers, and analytical performance of the paper biosensors with their respective applications in real sample matrices. Different commercial aspects of paper biosensor have also been explained using SWOT (Strength, Weakness, Opportunities, Threats) analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting the Uncertain Future of Aptamer-Based Diagnostics and Therapeutics.

Science.gov (United States)

Bruno, John G

2015-04-16

Despite the great promise of nucleic acid aptamers in the areas of diagnostics and therapeutics for their facile in vitro development, lack of immunogenicity and other desirable properties, few truly successful aptamer-based products exist in the clinical or other markets. Core reasons for these commercial deficiencies probably stem from industrial commitment to antibodies including a huge financial investment in humanized monoclonal antibodies and a general ignorance about aptamers and their performance among the research and development community. Given the early failures of some strong commercial efforts to gain government approval and bring aptamer-based products to market, it may seem that aptamers are doomed to take a backseat to antibodies forever. However, the key advantages of aptamers over antibodies coupled with niche market needs that only aptamers can fill and more recent published data still point to a bright commercial future for aptamers in areas such as infectious disease and cancer diagnostics and therapeutics. As more researchers and entrepreneurs become familiar with aptamers, it seems inevitable that aptamers will at least be considered for expanded roles in diagnostics and therapeutics. This review also examines new aptamer modifications and attempts to predict new aptamer applications that could revolutionize biomedical technology in the future and lead to marketed products.

Implementing an ultrasound-based protocol for diagnosingappendicitis while maintaining diagnostic accuracy

International Nuclear Information System (INIS)

Van Atta, Angela J.; Baskin, Henry J.; Maves, Connie K.; Dansie, David M.; Rollins, Michael D.; Bolte, Robert G.; Mundorff, Michael B.; Andrews, Seth P.

2015-01-01

The use of ultrasound to diagnose appendicitis in children is well-documented but not universally employed outside of pediatric academic centers, especially in the United States. Various obstacles make it difficult for institutions and radiologists to abandon a successful and accurate CT-based imaging protocol in favor of a US-based protocol. To describe how we overcame barriers to implementing a US-based appendicitis protocol among a large group of nonacademic private-practice pediatric radiologists while maintaining diagnostic accuracy and decreasing medical costs. A multidisciplinary team of physicians (pediatric surgery, pediatric emergency medicine and pediatric radiology) approved an imaging protocol using US as the primary modality to evaluate suspected appendicitis with CT for equivocal cases. The protocol addressed potential bias against US and accommodated for institutional limitations of radiologist and sonographer experience and availability. Radiologists coded US reports according to the probability of appendicitis. Radiology reports were compared with clinical outcomes to assess diagnostic accuracy. During the study period, physicians from each group were apprised of the interim US protocol accuracy results. Problematic cases were discussed openly. A total of 512 children were enrolled and underwent US for evaluation of appendicitis over a 30-month period. Diagnostic accuracy was comparable to published results for combined US/CT protocols. Comparing the first 12 months to the last 12 months of the study period, the proportion of children achieving an unequivocal US result increased from 30% (51/169) to 53% (149/282) and the proportion of children undergoing surgery based solely on US findings increased from 55% (23/42) to 84% (92/109). Overall, 63% (325/512) of patients in the protocol did not require a CT. Total patient costs were reduced by $30,182 annually. We overcame several barriers to implementing a US protocol. During the study period our

Implementing an ultrasound-based protocol for diagnosingappendicitis while maintaining diagnostic accuracy

Energy Technology Data Exchange (ETDEWEB)

Van Atta, Angela J. [University of Utah School of Medicine, Salt Lake City, UT (United States); Baskin, Henry J.; Maves, Connie K.; Dansie, David M. [Primary Children' s Hospital, Department of Radiology, Salt Lake City, UT (United States); Rollins, Michael D. [University of Utah School of Medicine, Department of Surgery, Division of Pediatric Surgery, Salt Lake City, UT (United States); Bolte, Robert G. [University of Utah School of Medicine, Department of Pediatrics, Division of Pediatric Emergency Medicine, Salt Lake City, UT (United States); Mundorff, Michael B.; Andrews, Seth P. [Primary Children' s Hospital, Systems Improvement, Salt Lake City, UT (United States)

2015-05-01

The use of ultrasound to diagnose appendicitis in children is well-documented but not universally employed outside of pediatric academic centers, especially in the United States. Various obstacles make it difficult for institutions and radiologists to abandon a successful and accurate CT-based imaging protocol in favor of a US-based protocol. To describe how we overcame barriers to implementing a US-based appendicitis protocol among a large group of nonacademic private-practice pediatric radiologists while maintaining diagnostic accuracy and decreasing medical costs. A multidisciplinary team of physicians (pediatric surgery, pediatric emergency medicine and pediatric radiology) approved an imaging protocol using US as the primary modality to evaluate suspected appendicitis with CT for equivocal cases. The protocol addressed potential bias against US and accommodated for institutional limitations of radiologist and sonographer experience and availability. Radiologists coded US reports according to the probability of appendicitis. Radiology reports were compared with clinical outcomes to assess diagnostic accuracy. During the study period, physicians from each group were apprised of the interim US protocol accuracy results. Problematic cases were discussed openly. A total of 512 children were enrolled and underwent US for evaluation of appendicitis over a 30-month period. Diagnostic accuracy was comparable to published results for combined US/CT protocols. Comparing the first 12 months to the last 12 months of the study period, the proportion of children achieving an unequivocal US result increased from 30% (51/169) to 53% (149/282) and the proportion of children undergoing surgery based solely on US findings increased from 55% (23/42) to 84% (92/109). Overall, 63% (325/512) of patients in the protocol did not require a CT. Total patient costs were reduced by $30,182 annually. We overcame several barriers to implementing a US protocol. During the study period our

Proteinuria: The diagnostic strategy based on urine proteins differentiation

Directory of Open Access Journals (Sweden)

Stojimirović Biljana B.

2004-01-01

Full Text Available Basal glomerular membrane represents mechanical and electrical barrier for passing of the plasma proteins. Mechanical barrier is composed of cylindrical pores and filtration fissure, and negative layer charge in exterior and interior side of basal glomerular membrane, made of heparan sulphate and sialoglicoproteine, provides certain electrical barrier. Diagnostic strategy based on different serum and urine proteins enables the differentiation of various types of proteinuria. Depending on etiology of proteinuria it can be prerenal, renal and postrenal. By analyzing albumin, armicroglobulin, immunoglobulin G and armacroglobulin, together with total protein in urine, it is possible to detect and differentiate causes of prerenal, renal (glomerular, tubular, glomerulo-tubular and postrenal proteinuria. The adequate and early differentiation of proteinuria type is of an immense diagnostic and therapeutic importance.

An informatics model for guiding assembly of telemicrobiology workstations for malaria collaborative diagnostics using commodity products and open-source software

Directory of Open Access Journals (Sweden)

Crandall Ian

2009-07-01

Full Text Available Abstract Background Deficits in clinical microbiology infrastructure exacerbate global infectious disease burdens. This paper examines how commodity computation, communication, and measurement products combined with open-source analysis and communication applications can be incorporated into laboratory medicine microbiology protocols. Those commodity components are all now sourceable globally. An informatics model is presented for guiding the use of low-cost commodity components and free software in the assembly of clinically useful and usable telemicrobiology workstations. Methods The model incorporates two general principles: 1 collaborative diagnostics, where free and open communication and networking applications are used to link distributed collaborators for reciprocal assistance in organizing and interpreting digital diagnostic data; and 2 commodity engineering, which leverages globally available consumer electronics and open-source informatics applications, to build generic open systems that measure needed information in ways substantially equivalent to more complex proprietary systems. Routine microscopic examination of Giemsa and fluorescently stained blood smears for diagnosing malaria is used as an example to validate the model. Results The model is used as a constraint-based guide for the design, assembly, and testing of a functioning, open, and commoditized telemicroscopy system that supports distributed acquisition, exploration, analysis, interpretation, and reporting of digital microscopy images of stained malarial blood smears while also supporting remote diagnostic tracking, quality assessment and diagnostic process development. Conclusion The open telemicroscopy workstation design and use-process described here can address clinical microbiology infrastructure deficits in an economically sound and sustainable manner. It can boost capacity to deal with comprehensive measurement of disease and care outcomes in individuals and

An informatics model for guiding assembly of telemicrobiology workstations for malaria collaborative diagnostics using commodity products and open-source software.

Science.gov (United States)

Suhanic, West; Crandall, Ian; Pennefather, Peter

2009-07-17

Deficits in clinical microbiology infrastructure exacerbate global infectious disease burdens. This paper examines how commodity computation, communication, and measurement products combined with open-source analysis and communication applications can be incorporated into laboratory medicine microbiology protocols. Those commodity components are all now sourceable globally. An informatics model is presented for guiding the use of low-cost commodity components and free software in the assembly of clinically useful and usable telemicrobiology workstations. The model incorporates two general principles: 1) collaborative diagnostics, where free and open communication and networking applications are used to link distributed collaborators for reciprocal assistance in organizing and interpreting digital diagnostic data; and 2) commodity engineering, which leverages globally available consumer electronics and open-source informatics applications, to build generic open systems that measure needed information in ways substantially equivalent to more complex proprietary systems. Routine microscopic examination of Giemsa and fluorescently stained blood smears for diagnosing malaria is used as an example to validate the model. The model is used as a constraint-based guide for the design, assembly, and testing of a functioning, open, and commoditized telemicroscopy system that supports distributed acquisition, exploration, analysis, interpretation, and reporting of digital microscopy images of stained malarial blood smears while also supporting remote diagnostic tracking, quality assessment and diagnostic process development. The open telemicroscopy workstation design and use-process described here can address clinical microbiology infrastructure deficits in an economically sound and sustainable manner. It can boost capacity to deal with comprehensive measurement of disease and care outcomes in individuals and groups in a distributed and collaborative fashion. The workstation

Software architecture and design of the web services facilitating climate model diagnostic analysis

Science.gov (United States)

Pan, L.; Lee, S.; Zhang, J.; Tang, B.; Zhai, C.; Jiang, J. H.; Wang, W.; Bao, Q.; Qi, M.; Kubar, T. L.; Teixeira, J.

2015-12-01

Climate model diagnostic analysis is a computationally- and data-intensive task because it involves multiple numerical model outputs and satellite observation data that can both be high resolution. We have built an online tool that facilitates this process. The tool is called Climate Model Diagnostic Analyzer (CMDA). It employs the web service technology and provides a web-based user interface. The benefits of these choices include: (1) No installation of any software other than a browser, hence it is platform compatable; (2) Co-location of computation and big data on the server side, and small results and plots to be downloaded on the client side, hence high data efficiency; (3) multi-threaded implementation to achieve parallel performance on multi-core servers; and (4) cloud deployment so each user has a dedicated virtual machine. In this presentation, we will focus on the computer science aspects of this tool, namely the architectural design, the infrastructure of the web services, the implementation of the web-based user interface, the mechanism of provenance collection, the approach to virtualization, and the Amazon Cloud deployment. As an example, We will describe our methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). Another example is the use of Docker, a light-weight virtualization container, to distribute and deploy CMDA onto an Amazon EC2 instance. Our tool of CMDA has been successfully used in the 2014 Summer School hosted by the JPL Center for Climate Science. Students had positive feedbacks in general and we will report their comments. An enhanced version of CMDA with several new features, some requested by the 2014 students, will be used in the 2015 Summer School soon.

Electronics and Algorithms for HOM Based Beam Diagnostics

Science.gov (United States)

Frisch, Josef; Baboi, Nicoleta; Eddy, Nathan; Nagaitsev, Sergei; Hensler, Olaf; McCormick, Douglas; May, Justin; Molloy, Stephen; Napoly, Olivier; Paparella, Rita; Petrosyan, Lyudvig; Ross, Marc; Simon, Claire; Smith, Tonee

2006-11-01

The signals from the Higher Order Mode (HOM) ports on superconducting cavities can be used as beam position monitors and to do survey structure alignment. A HOM-based diagnostic system has been installed to instrument both couplers on each of the 40 cryogenic accelerating structures in the DESY TTF2 Linac. The electronics uses a single stage down conversion from the 1.7 GHz HOM spectral line to a 20MHz IF which has been digitized. The electronics is based on low cost surface mount components suitable for large scale production. The analysis of the HOM data is based on Singular Value Decomposition. The response of the OM modes is calibrated using conventional BPMs.

A systematic review on diagnostic accuracy of CT-based detection of significant coronary artery disease

International Nuclear Information System (INIS)

Janne d'Othee, Bertrand; Siebert, Uwe; Cury, Ricardo; Jadvar, Hossein; Dunn, Edward J.; Hoffmann, Udo

2008-01-01

Objectives: Systematic review of diagnostic accuracy of contrast enhanced coronary computed tomography (CE-CCT). Background: Noninvasive detection of coronary artery stenosis (CAS) by CE-CCT as an alternative to catheter-based coronary angiography (CCA) may improve patient management. Methods: Forty-one articles published between 1997 and 2006 were included that evaluated native coronary arteries for significant stenosis and used CE-CCT as diagnostic test and CCA as reference standard. Study group characteristics, study methodology and diagnostic outcomes were extracted. Pooled summary sensitivity and specificity of CE-CCT were calculated using a random effects model (1) for all coronary segments, (2) assessable segments, and (3) per patient. Results: The 41 studies totaled 2515 patients (75% males; mean age: 59 years, CAS prevalence: 59%). Analysis of all coronary segments yielded a sensitivity of 95% (80%, 89%, 86%, 98% for electron beam CT, 4/8-slice, 16-slice and 64-slice MDCT, respectively) for a specificity of 85% (77%, 84%, 95%, 91%). Analysis limited to segments deemed assessable by CT showed sensitivity of 96% (86%, 85%, 98%, 97%) for a specificity of 95% (90%, 96%, 96%, 96%). Per patient, sensitivity was 99% (90%, 97%, 99%, 98%) and specificity was 76% (59%, 81%, 83%, 92%). Heterogeneity was quantitatively important but not explainable by patient group characteristics or study methodology. Conclusions: Current diagnostic accuracy of CE-CCT is high. Advances in CT technology have resulted in increases in diagnostic accuracy and proportion of assessable coronary segments. However, per patient, accuracy may be lower and CT may have more limited clinical utility in populations at high risk for CAD

Cooperative research and development for artificial intelligence based reactor diagnostic system

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Abboud, R.G.; Chasensky, T.M.

1994-01-01

Artificial Intelligence (AI) techniques in the form of knowledge-based Expert Systems (ESs) have been proposed to provide on-line decision-making support for plant operators during both normal and emergency conditions. However, in spite of the great interest in these advanced techniques, their application in the diagnosis of large-scale processes has not yet reached its full potential because of limitations of the knowledge base. These limitations include problems with knowledge acquisition and the use of an event-oriented approach for process diagnosis. To investigate the capabilities of this two-level hierarchical knowledge structure, Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL)are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA) project to perform feasibility studies on the proposed diagnostic system. Investigations are being performed in the construction of a physics-based plant level process diagnostic ES and the characterization of component-level fault project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use T-H signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance. To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. This is an ongoing multi-year project and the remainder of this paper presents a mid-term status report

Observability analysis for model-based fault detection and sensor selection in induction motors

International Nuclear Information System (INIS)

Nakhaeinejad, Mohsen; Bryant, Michael D

2011-01-01

Sensors in different types and configurations provide information on the dynamics of a system. For a specific task, the question is whether measurements have enough information or whether the sensor configuration can be changed to improve the performance or to reduce costs. Observability analysis may answer the questions. This paper presents a general algorithm of nonlinear observability analysis with application to model-based diagnostics and sensor selection in three-phase induction motors. A bond graph model of the motor is developed and verified with experiments. A nonlinear observability matrix based on Lie derivatives is obtained from state equations. An observability index based on the singular value decomposition of the observability matrix is obtained. Singular values and singular vectors are used to identify the most and least observable configurations of sensors and parameters. A complex step derivative technique is used in the calculation of Jacobians to improve the computational performance of the observability analysis. The proposed algorithm of observability analysis can be applied to any nonlinear system to select the best configuration of sensors for applications of model-based diagnostics, observer-based controller, or to determine the level of sensor redundancy. Observability analysis on induction motors provides various sensor configurations with corresponding observability indices. Results show the redundancy levels for different sensors, and provide a sensor selection guideline for model-based diagnostics, and for observer-based controllers. The results can also be used for sensor fault detection and to improve the reliability of the system by increasing the redundancy level in measurements

Advanced DNA-Based Point-of-Care Diagnostic Methods for Plant Diseases Detection

OpenAIRE

Lau, Han Yih; Botella, Jose R.

2017-01-01

Diagnostic technologies for the detection of plant pathogens with point-of-care capability and high multiplexing ability are an essential tool in the fight to reduce the large agricultural production losses caused by plant diseases. The main desirable characteristics for such diagnostic assays are high specificity, sensitivity, reproducibility, quickness, cost efficiency and high-throughput multiplex detection capability. This article describes and discusses various DNA-based point-of care di...

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Directory of Open Access Journals (Sweden)

Issouf Fofana

2016-08-01

Full Text Available The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment.

Directory of Open Access Journals (Sweden)

Kazuhiro Yamaguchi

Full Text Available A variety of cognitive diagnostic models (CDMs have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students' achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed.

Diagnosis and Model Based Identification of a Coupling Misalignment

Directory of Open Access Journals (Sweden)

P. Pennacchi

2005-01-01

Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

Event-based model diagnosis of rainfall-runoff model structures

International Nuclear Information System (INIS)

Stanzel, P.

2012-01-01

The objective of this research is a comparative evaluation of different rainfall-runoff model structures. Comparative model diagnostics facilitate the assessment of strengths and weaknesses of each model. The application of multiple models allows an analysis of simulation uncertainties arising from the selection of model structure, as compared with effects of uncertain parameters and precipitation input. Four different model structures, including conceptual and physically based approaches, are compared. In addition to runoff simulations, results for soil moisture and the runoff components of overland flow, interflow and base flow are analysed. Catchment runoff is simulated satisfactorily by all four model structures and shows only minor differences. Systematic deviations from runoff observations provide insight into model structural deficiencies. While physically based model structures capture some single runoff events better, they do not generally outperform conceptual model structures. Contributions to uncertainty in runoff simulations stemming from the choice of model structure show similar dimensions to those arising from parameter selection and the representation of precipitation input. Variations in precipitation mainly affect the general level and peaks of runoff, while different model structures lead to different simulated runoff dynamics. Large differences between the four analysed models are detected for simulations of soil moisture and, even more pronounced, runoff components. Soil moisture changes are more dynamical in the physically based model structures, which is in better agreement with observations. Streamflow contributions of overland flow are considerably lower in these models than in the more conceptual approaches. Observations of runoff components are rarely made and are not available in this study, but are shown to have high potential for an effective selection of appropriate model structures (author) [de

Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses

Science.gov (United States)

Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik; Park, Hyun

2016-01-01

Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination. PMID:26877781

Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses.

Science.gov (United States)

Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik; Park, Hyun

2016-01-01

Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

Science.gov (United States)

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

The SUCCESS model for laboratory performance and execution of rapid molecular diagnostics in patients with sepsis.

Science.gov (United States)

Dekmezian, Mhair; Beal, Stacy G; Damashek, Mary Jane; Benavides, Raul; Dhiman, Neelam

2015-04-01

Successful performance and execution of rapid diagnostics in a clinical laboratory hinges heavily on careful validation, accurate and timely communication of results, and real-time quality monitoring. Laboratories must develop strategies to integrate diagnostics with stewardship and evidence-based clinical practice guidelines. We present a collaborative SUCCESS model for execution and monitoring of rapid sepsis diagnostics to facilitate timely treatment. Six months after execution of the Verigene Gram-Positive Blood Culture (BC-GP) and the AdvanDx PNA-FISH assays, data were collected on 579 and 28 episodes of bacteremia and fungemia, respectively. Clinical testing was executed using a SUCCESS model comprising the following components: stewardship, utilization of resources, core strategies, concierge services, education, support, and surveillance. Stewardship needs were identified by evaluating the specialty services benefiting from new testing. Utilization of resources was optimized by reviewing current treatment strategies and antibiogram and formulary options. Core strategies consisted of input from infectious disease leadership, pharmacy, and laboratory staff. Concierge services included automated Micro-eUpdate and physician-friendly actionable reports. Education modules were user-specific, and support was provided through a dedicated 24/7 microbiology hotline. Surveillance was performed by daily audit by the director. Using the SUCCESS model, the turnaround time for the detailed report with actionable guidelines to the physician was ∼3 hours from the time of culture positivity. The overall correlation between rapid methods and culture was 94% (546/579). Discrepant results were predominantly contaminants such as a coagulase-negative staphylococci or viridans streptococci in mixed cultures. SUCCESS is a cost-effective and easily adaptable model for clinical laboratories with limited stewardship resources.

Cost Implications of Value-Based Pricing for Companion Diagnostic Tests in Precision Medicine.

Science.gov (United States)

Zaric, Gregory S

2016-07-01

Many interpretations of personalized medicine, also referred to as precision medicine, include discussions of companion diagnostic tests that allow drugs to be targeted to those individuals who are most likely to benefit or that allow treatment to be designed in a way such that individuals who are unlikely to benefit do not receive treatment. Many authors have commented on the clinical and competitive implications of companion diagnostics, but there has been relatively little formal analysis of the cost implications of companion diagnostics, although cost reduction is often cited as a significant benefit of precision medicine. We investigate the potential impact on costs of precision medicine implemented through the use of companion diagnostics. We develop a framework in which the costs of companion diagnostic tests are determined by considerations of profit maximization and cost effectiveness. We analyze four scenarios that are defined by the incremental cost-effectiveness ratio of the new drug in the absence of a companion diagnostic test. We find that, in most scenarios, precision medicine strategies based on companion diagnostics should be expected to lead to increases in costs in the short term and that costs would fall only in a limited number of situations.

Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

DEFF Research Database (Denmark)

Asgarpour, Masoud; Sørensen, John Dalsgaard

2018-01-01

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 prediction 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...

Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

DEFF Research Database (Denmark)

Asgarpour, Masoud; Sørensen, John Dalsgaard

2018-01-01

monitoring, fault prediction 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......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...

Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

DEFF Research Database (Denmark)

Asgarpour, Masoud

2017-01-01

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......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...

Diagnostic method for photovoltaic systems based on light I-V measurements

DEFF Research Database (Denmark)

Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

2015-01-01

, be it external, such as shading or soiling, or degradation or failure of the PV modules and balance-of-system components. This allows for performing preventive and/or reparative maintenance, thus minimizing further losses and costs. This article proposes a complete diagnostic method for detecting shading...... and analysis of the diagnostic parameters and logic was performed based on module level tests on standard crystalline silicon PV modules, and were optimized to detect even small partial shading and increase series-resistance losses. To demonstrate the practical application and operation of this method...

A Laser-Based Diagnostic Suite for Hypersonic Test Facilities, Phase I

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National Aeronautics and Space Administration — In this SBIR effort, Los Gatos Research (LGR) proposes to develop a suite of laser-based diagnostics for the study of reactive and non-reactive hypersonic flows....

Diagnostic radiography exposure increases the risk for thyroid microcarcinoma: a population-based case-control study.

Science.gov (United States)

Zhang, Yawei; Chen, Yingtai; Huang, Huang; Sandler, Jason; Dai, Min; Ma, Shuangge; Udelsman, Robert

2015-09-01

Thyroid cancer incidence and diagnostic radiography exposures, particularly computed tomography (CT) scanning and nuclear medicine examinations, have increased substantially in the USA. However, very few epidemiologic studies have directly investigated their associations. A population-based case-control study was conducted in Connecticut in 2010-2011, including 462 histologically confirmed incident thyroid cancer cases and 498 population-based controls. Multivariate unconditional logistic regression models were used to estimate the associations between diagnostic radiography and the risk of thyroid cancer, controlling for potential confounding factors. Exposure to any form of diagnostic radiography was associated with an increased risk of well-differentiated thyroid microcarcinoma [tumor size≤10 mm, odds ratio (OR)=2.76, 95% confidence interval (CI): 1.31-5.81]. The highest risk increase occurred with nuclear medicine examinations (excluding cardiology tests and thyroid uptake studies; OR=5.47, 95% CI: 2.10-14.23), followed by chest CT scanning (OR=4.30, 95% CI: 1.66-11.14), head and neck CT scanning (OR=3.88, 95% CI: 1.75-8.63), upper gastrointestinal series (OR=3.56, 95% CI: 1.54-8.21), lower gastrointestinal series (OR=3.29, 95% CI: 1.41-7.66), kidney radiography involving dye injection into a vein or artery (OR=3.21, 95% CI: 1.20-8.54), mammography (OR=2.95, 95% CI: 1.14-7.61), chest radiography (OR=2.93, 95% CI: 1.37-6.29), and abdomen CT scanning (OR=2.54, 95% CI: 1.02-6.30). No significant associations were found between these imaging modalities and thyroid tumors larger than 10 mm. This study provides the first direct evidence that CT scanning and nuclear medicine examinations are associated with an increased risk of thyroid cancer. The novel finding that an array of diagnostic radiography procedures are associated with thyroid microcarcinomas warrants further investigation.

Model-Independent Evaluation of Tumor Markers and a Logistic-Tree Approach to Diagnostic Decision Support

Directory of Open Access Journals (Sweden)

Weizeng Ni

2014-01-01

Full Text Available Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.

Diagnostics of a crack in a load coupling of a gas turbine using the machine model and the analysis of the shaft vibrations

Science.gov (United States)

Pennacchi, Paolo; Vania, Andrea

2008-07-01

The diagnostics of malfunctions that can cause catastrophic failures has to be made in early stage in the industrial environment. Often flexible couplings are employed in industrial rotating machines when gearboxes and heavy thermal gradients are present. The hot and cold alignment of these couplings can be very different. Severe misalignments can generate cracks in the stub shafts, which can propagate in operating condition. Owing to the flexural flexibility of the load coupling, the shaft vibrations may be not noticeably affected by some typical symptoms that usually point out the presence of a crack, like twice per revolution harmonics in the vibration spectrum. Anyhow, suitable diagnostic strategies can detect clear fault symptoms, while model-based methods can confirm the occurrence of the shaft bow induced by the progressive yielding of a load coupling due to a crack. This paper shows as a model-based diagnostic methodology would have allowed a crack in a load coupling of a gas turbine to be identified before a serious failure happened by means of the shaft vibration analysis under operating conditions and rated speed. Finally, the vibrations caused by the shaft bow due to the propagation of a crack in the stub shaft of the coupling have been simulated using suitable equivalent excitations, the magnitude and phase of which have been estimated by means of a model-based identification method.

Evaluation of observed blast loading effects on NIF x-ray diagnostic collimators.

Science.gov (United States)

Masters, N D; Fisher, A; Kalantar, D; Prasad, R; Stölken, J S; Wlodarczyk, C

2014-11-01

We present the "debris wind" models used to estimate the impulsive load to which x-ray diagnostics and other structures are subject during National Ignition Facility experiments. These models are used as part of the engineering design process. Isotropic models, based on simulations or simplified "expanding shell" models, are augmented by debris wind multipliers to account for directional anisotropy. We present improvements to these multipliers based on measurements of the permanent deflections of diagnostic components: 4× for the polar direction and 2× within the equatorial plane-the latter relaxing the previous heuristic debris wind multiplier.

Application of support vector machine model for enhancing the diagnostic value of tumor markers in gastric cancer

International Nuclear Information System (INIS)

Wang Hui; Huang Gang

2010-01-01

Objective: To evaluate the early diagnostic value of tumor markers for gastric cancer using support vector machine (SVM) model. Methods: Subjects involved in the study consisted of 262 cases with gastric cancer, 156 cases with benign gastric diseases and 149 healthy controls. From those subjects, five tumor markers, carcinoembryonic antigen (CEA), carbohydrate (CA) 125, CA19-9, alphafetoprotein (AFP) and CA50, were assayed and collected to make the datasets. To modify SVM model to fit the diagnostic classifiers, radial basis function was adopted and kernel function was optimized and validated by grid search and cross validation. For comparative study, methods of combination tests of five markers, Logistic regression, and decision tree were also used. Results: For gastric cancer, the diagnostic accuracy of the combination tests, Logistic regression, decision tree and SVM model were 46.2%, 64.5%, 63.9% and 95.1% respectively. SVM model significantly elevated the diagnostic value comparing with other three methods. Conclusion: The application of SVM model is of high value in enhancing the tumor marker for the diagnosis of gastric cancer. (authors)

Design and implementation of a Macintosh-CAMAC based system for neutral beam diagnostics

International Nuclear Information System (INIS)

Wight, J.; Hong, R.M.; Phillips, J.C.; Lee, R.L.; Colleraine, A.P.; Kim, J.

1989-12-01

An automated personal computer based CAMAC data acquisition system is being implemented on the DIII-D neutral beamlines for certain diagnostics. The waterflow calorimetry (WFC) diagnostic is the first system to be upgraded. It includes data acquisition by a Macintosh II computer containing a National Instruments IEEE-488 card, and running their LabView software. Macintosh to CAMAC communications are carried out through an IEEE-488 crate controller. The Doppler shift spectroscopy, residual gas analysis, and armor tile infrared image diagnostics will be modified in similar ways. To reduce the demand for Macintosh CPU time, the extensive serial high-way data activity is performed by means of a new Kinetic Systems 3982 List sequencing Crate Controller dedicated to these operations. A simple Local Area Network file server is used to store data from all diagnostics together, and in a format readable by a standard commercial database. This reduces the problem of redundant data storage and allows simpler inter-diagnostic analysis. 3 refs., 4 figs

An efficient diagnostic technique for distribution systems based on under fault voltages and currents

Energy Technology Data Exchange (ETDEWEB)

Campoccia, A.; Di Silvestre, M.L.; Incontrera, I.; Riva Sanseverino, E. [Dipartimento di Ingegneria Elettrica elettronica e delle Telecomunicazioni, Universita degli Studi di Palermo, viale delle Scienze, 90128 Palermo (Italy); Spoto, G. [Centro per la Ricerca Elettronica in Sicilia, Monreale, Via Regione Siciliana 49, 90046 Palermo (Italy)

2010-10-15

Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental components rms values taken from the transient measures of line currents and voltages at the MV/LV substations. The fault location and identification algorithm, proposed by the authors and suitably restated, has been implemented on a microprocessor-based device that can be installed at each MV/LV substation. The speed and precision of the algorithm have been tested against the errors deriving from the fundamental extraction within the prescribed fault clearing times and against the inherent precision of the electronic device used for computation. The tests have been carried out using Matlab Simulink for simulating the faulted system. (author)

Data driven approaches for diagnostics and optimization of NPP operation

International Nuclear Information System (INIS)

Pliska, J.; Machat, Z.

2014-01-01

The efficiency and heat rate is an important indicator of both the health of the power plant equipment and the quality of power plant operation. To achieve this challenges powerful tool is a statistical data processing of large data sets which are stored in data historians. These large data sets contain useful information about process quality and equipment and sensor health. The paper discusses data-driven approaches for model building of main power plant equipment such as condenser, cooling tower and the overall thermal cycle as well using multivariate regression techniques based on so called a regression triplet - data, model and method. Regression models comprise a base for diagnostics and optimization tasks. Diagnostics and optimization tasks are demonstrated on practical cases - diagnostics of main power plant equipment to early identify equipment fault, and optimization task of cooling circuit by cooling water flow control to achieve for a given boundary conditions the highest power output. (authors)

Virtual standards of vibration-based defects diagnostics in railway industry

Directory of Open Access Journals (Sweden)

Vladimir TETTER

2009-01-01

Full Text Available The issues related to testing the functionality stated by producers of vibration-based diagnostic equipment have been considered. The introduction of virtual standards of defects found in bearing and geared assemblies of rolling stock is offered. The variants of virtual standards realization have been considered.

Spectroscopic Challenges in the Modelling and Diagnostics of High Temperature Air Plasma Radiation for Aerospace Applications

International Nuclear Information System (INIS)

Laux, Christophe O.

2007-01-01

State-of-the-art spectroscopic models of the radiative transitions of interest for Earth re-entry and ground-based diagnostic facilities for aerospace applications are reviewed. The spectral range considered extends from the vacuum ultraviolet to the mid-infrared range (80 nm to 5.5 μm). The modeling results are compared with absolute intensity measurements of the ultraviolet-visible-infrared emission of a well-characterized high-temperature air plasma produced with a 50 kW inductively coupled radio-frequency plasma torch, and with high-resolution absorption spectra from the Center for Astrophysics in the vacuum ultraviolet. The Spectroscopic data required to better model the spectral features of interest for aerospace applications are discussed

Innovation in diagnostic imaging services: assessing the potential for value-based reimbursement.

Science.gov (United States)

Garrison, Louis P; Bresnahan, Brian W; Higashi, Mitchell K; Hollingworth, William; Jarvik, Jeffrey G

2011-09-01

Innovation in the field of diagnostic imaging is based primarily on the availability of new and improved equipment that opens the door for new clinical applications. Payments for these imaging procedures are subject to complex Medicare price control schemes, affecting incentives for appropriate use and innovation. Achieving a "dynamically efficient" health care system-one that elicits a socially optimal amount of innovation-requires that innovators be rewarded in relation to the value they add and can demonstrate with evidence. The authors examine how and whether value-based reimbursement for diagnostic imaging services might better reward innovation explicitly for expected improvements in health and economic outcomes. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

Advanced DNA-Based Point-of-Care Diagnostic Methods for Plant Diseases Detection

Directory of Open Access Journals (Sweden)

Han Yih Lau

2017-12-01

Full Text Available Diagnostic technologies for the detection of plant pathogens with point-of-care capability and high multiplexing ability are an essential tool in the fight to reduce the large agricultural production losses caused by plant diseases. The main desirable characteristics for such diagnostic assays are high specificity, sensitivity, reproducibility, quickness, cost efficiency and high-throughput multiplex detection capability. This article describes and discusses various DNA-based point-of care diagnostic methods for applications in plant disease detection. Polymerase chain reaction (PCR is the most common DNA amplification technology used for detecting various plant and animal pathogens. However, subsequent to PCR based assays, several types of nucleic acid amplification technologies have been developed to achieve higher sensitivity, rapid detection as well as suitable for field applications such as loop-mediated isothermal amplification, helicase-dependent amplification, rolling circle amplification, recombinase polymerase amplification, and molecular inversion probe. The principle behind these technologies has been thoroughly discussed in several review papers; herein we emphasize the application of these technologies to detect plant pathogens by outlining the advantages and disadvantages of each technology in detail.

A Ribeiroia spp. (Class: Trematoda) - Specific PCR-based diagnostic

Science.gov (United States)

Reinitz, David M.; Yoshino, T.P.; Cole, Rebecca A.

2007-01-01

Increased reporting of amphibian malformations in North America has been noted with concern in light of reports that amphibian numbers and species are declining worldwide. Ribeiroia ondatrae has been shown to cause a variety of types of malformations in amphibians. However, little is known about the prevalence of R. ondatrae in North America. To aid in conducting field studies of Ribeiroia spp., we have developed a polymerase chain reaction (PCR)-based diagnostic. Herein, we describe the development of an accurate, rapid, simple, and cost-effective diagnostic for detection of Ribeiroia spp. infection in snails (Planorbella trivolvis). Candidate oligonucleotide primers for PCR were designed via DNA sequence analyses of multiple ribosomal internal transcribed spacer-2 regions from Ribeiroia spp. and Echinostoma spp. Comparison of consensus sequences determined from both genera identified areas of sequence potentially unique to Ribeiroia spp. The PCR reliably produced a diagnostic 290-base pair (bp) product in the presence of a wide concentration range of snail or frog DNA. Sensitivity was examined with DNA extracted from single R. ondatrae cercaria. The single-tube PCR could routinely detect less than 1 cercariae equivalent, because DNA isolated from a single cercaria could be diluted at least 1:50 and still yield a positive result via gel electrophoresis. An even more sensitive nested PCR also was developed that routinely detected 100 fg of the 290-bp fragment. The assay did not detect furcocercous cercariae of certain Schistosomatidae, Echinostoma sp., or Sphaeridiotrema globulus nor adults of Clinostomum sp. or Cyathocotyle bushiensis. Field testing of 137 P. trivolvis identified 3 positives with no overt environmental cross-reactivity, and results concurred with microscopic examinations in all cases. ?? American Society of Parasitologists 2007.

Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics.

Science.gov (United States)

Kirwan, Alan; Utratna, Marta; O'Dwyer, Michael E; Joshi, Lokesh; Kilcoyne, Michelle

2015-01-01

Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.

New advanced netted ground based and topside radio diagnostics for Space Weather Program

Science.gov (United States)

Rothkaehl, Hanna; Krankowski, Andrzej; Morawski, Marek; Atamaniuk, Barbara; Zakharenkova, Irina; Cherniak, Iurii

2014-05-01

To give a more detailed and complete understanding of physical plasma processes that govern the solar-terrestrial space, and to develop qualitative and quantitative models of the magnetosphere-ionosphere-thermosphere coupling, it is necessary to design and build the next generation of instruments for space diagnostics and monitoring. Novel ground- based wide-area sensor networks, such as the LOFAR (Low Frequency Array) radar facility, comprising wide band, and vector-sensing radio receivers and multi-spacecraft plasma diagnostics should help solve outstanding problems of space physics and describe long-term environmental changes. The LOw Frequency ARray - LOFAR - is a new fully digital radio telescope designed for frequencies between 30 MHz and 240 MHz located in Europe. The three new LOFAR stations will be installed until summer 2015 in Poland. The LOFAR facilities in Poland will be distributed among three sites: Lazy (East of Krakow), Borowiec near Poznan and Baldy near Olsztyn. All they will be connected via PIONIER dedicated links to Poznan. Each site will host one LOFAR station (96 high-band+96 low-band antennas). They will most time work as a part of European network, however, when less charged, they can operate as a national network The new digital radio frequency analyzer (RFA) on board the low-orbiting RELEC satellite was designed to monitor and investigate the ionospheric plasma properties. This two-point ground-based and topside ionosphere-located space plasma diagnostic can be a useful new tool for monitoring and diagnosing turbulent plasma properties. The RFA on board the RELEC satellite is the first in a series of experiments which is planned to be launched into the near-Earth environment. In order to improve and validate the large scales and small scales ionospheric structures we will used the GPS observations collected at IGS/EPN network employed to reconstruct diurnal variations of TEC using all satellite passes over individual GPS stations and the

The Impact of Gas Turbine Component Leakage Fault on GPA Performance Diagnostics

Directory of Open Access Journals (Sweden)

E. L. Ntantis

2016-01-01

Full Text Available The leakage analysis is a key factor in determining energy loss from a gas turbine. Once the components assembly fails, air leakage through the opening increases resulting in a performance loss. Therefore, the performance efficiency of the engine cannot be reliably determined, without good estimates and analysis of leakage faults. Consequently, the implementation of a leakage fault within a gas turbine engine model is necessary for any performance diagnostic technique that can expand its diagnostics capabilities for more accurate predictions. This paper explores the impact of gas turbine component leakage fault on GPA (Gas Path Analysis Performance Diagnostics. The analysis is demonstrated with a test case where gas turbine performance simulation and diagnostics code TURBOMATCH is used to build a performance model of a model engine similar to Rolls-Royce Trent 500 turbofan engine, and carry out the diagnostic analysis with the presence of different component fault cases. Conclusively, to improve the reliability of the diagnostic results, a leakage fault analysis of the implemented faults is made. The diagnostic tool used to deal with the analysis of the gas turbine component implemented faults is a model-based method utilizing a non-linear GPA.

Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Model-based machinery diagnostic and prognostic techniques depend upon high-quality mathematical models of the plant. Modeling uncertainties and errors decrease...

Nonparametric predictive inference for combining diagnostic tests with parametric copula

Science.gov (United States)

Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

2017-09-01

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

Study of methodology diversification in diagnostics

International Nuclear Information System (INIS)

Suda, Kazunori; Yonekawa, Tsuyoshi; Yoshikawa, Shinji; Hasegawa, Makoto

1999-03-01

There are several research activities to enhance safety and reliability of nuclear power plant operation and maintenance. We are developing a concept of an autonomous operation system where the role of operators is replaced with artificial intelligence. The purpose of the study described in this report is to develop a operator support system in abnormal plant situations. Conventionally, diagnostic modules based on individual methodology such as expert system have been developed and verified. In this report, methodology diversification is considered to integrate diagnostic modules which performance are confirmed using information processing technique. Technical issues to be considered in diagnostic methodology diversification are; 1)reliability of input data, 2)diversification of knowledge models, algorithms and reasoning schemes, 3)mutual complement and robustness. The diagnostic module utilizing the different approaches defined along with strategy of diversification was evaluated using fast breeder plant simulator. As a result, we confirmed that any singular diagnostic module can not meet accuracy criteria for the entire set of anomaly events. In contrast with this, we confirmed that every abnormality could be precisely diagnosed by a mutual combination. In other words, legitimacy of approach selected by strategy of diversification was shown, and methodology diversification attained clear efficiency for abnormal diagnosis. It has been also confirmed that the diversified diagnostic system implemented in this study is able to maintain its accuracy even in case that encountered scale of abnormality is different from reference cases embedded in the knowledge base. (author)

Opto-electronic DNA chip-based integrated card for clinical diagnostics.

Science.gov (United States)

Marchand, Gilles; Broyer, Patrick; Lanet, Véronique; Delattre, Cyril; Foucault, Frédéric; Menou, Lionel; Calvas, Bernard; Roller, Denis; Ginot, Frédéric; Campagnolo, Raymond; Mallard, Frédéric

2008-02-01

Clinical diagnostics is one of the most promising applications for microfluidic lab-on-a-chip or lab-on-card systems. DNA chips, which provide multiparametric data, are privileged tools for genomic analysis. However, automation of molecular biology protocol and use of these DNA chips in fully integrated systems remains a great challenge. Simplicity of chip and/or card/instrument interfaces is amongst the most critical issues to be addressed. Indeed, current detection systems for DNA chip reading are often complex, expensive, bulky and even limited in terms of sensitivity or accuracy. Furthermore, for liquid handling in the lab-on-cards, many devices use complex and bulky systems, either to directly manipulate fluids, or to ensure pneumatic or mechanical control of integrated valves. All these drawbacks prevent or limit the use of DNA-chip-based integrated systems, for point-of-care testing or as a routine diagnostics tool. We present here a DNA-chip-based protocol integration on a plastic card for clinical diagnostics applications including: (1) an opto-electronic DNA-chip, (2) fluid handling using electrically activated embedded pyrotechnic microvalves with closing/opening functions. We demonstrate both fluidic and electric packaging of the optoelectronic DNA chip without major alteration of its electronical and biological functionalities, and fluid control using novel electrically activable pyrotechnic microvalves. Finally, we suggest a complete design of a card dedicated to automation of a complex biological protocol with a fully electrical fluid handling and DNA chip reading.

Entropy-Based Clutter Rejection for Intrawall Diagnostics

Directory of Open Access Journals (Sweden)

Raffaele Solimene

2012-01-01

Full Text Available The intrawall diagnostic problem of detecting localized inhomogeneities possibly present within the wall is addressed. As well known, clutter arising from masonry structure can impair detection of embedded scatterers due to high amplitude reflections that wall front face introduces. Moreover, internal multiple reflections also can make it difficult ground penetrating radar images (radargramms interpretation. To counteract these drawbacks, a clutter rejection method, properly tailored on the wall features, is mandatory. To this end, here we employ a windowing strategy based on entropy measures of temporal traces “similarity.” Accordingly, instants of time for which radargramms exhibit entropy values greater than a prescribed threshold are “silenced.” Numerical results are presented in order to show the effectiveness of the entropy-based clutter rejection algorithm. Moreover, a comparison with the standard average trace subtraction is also included.

Flow diagnostics downstream of a tribladed rotor model

DEFF Research Database (Denmark)

Naumov, I. V.; Rahmanov, V. V.; Okulov, Valery

2012-01-01

This paper presents results of a study of vortex wake structures and measurements of instantaneous 3D velocity fields downstream of a triblade turbine model. Two operation modes of flow around the rotor with different tip speed ratios were tested. Initially the wake structures were visualized...... and subsequently quantitative data were recorded through velocity field restoration from particle tracks using a stereo PIV system.The study supplied flow diagnostics and recovered the instantaneous 3D velocity fields in the longitudinal cross section behind a tribladed rotor at different values of tip speed ratio...

Pre-examination factors affecting molecular diagnostic test results and interpretation: A case-based approach.

Science.gov (United States)

Payne, Deborah A; Baluchova, Katarina; Peoc'h, Katell H; van Schaik, Ron H N; Chan, K C Allen; Maekawa, Masato; Mamotte, Cyril; Russomando, Graciela; Rousseau, François; Ahmad-Nejad, Parviz

2017-04-01

Multiple organizations produce guidance documents that provide opportunities to harmonize quality practices for diagnostic testing. The International Organization for Standardization ISO 15189 standard addresses requirements for quality in management and technical aspects of the clinical laboratory. One technical aspect addresses the complexities of the pre-examination phase prior to diagnostic testing. The Committee for Molecular Diagnostics of the International Federation for Clinical Chemistry and Laboratory Medicine (also known as, IFCC C-MD) conducted a survey of international molecular laboratories and determined ISO 15189 to be the most referenced guidance document. In this review, the IFCC C-MD provides case-based examples illustrating the value of select pre-examination processes as these processes relate to molecular diagnostic testing. Case-based examples in infectious disease, oncology, inherited disease and pharmacogenomics address the utility of: 1) providing information to patients and users, 2) designing requisition forms, 3) obtaining informed consent and 4) maintaining sample integrity prior to testing. The pre-examination phase requires extensive and consistent communication between the laboratory, the healthcare provider and the end user. The clinical vignettes presented in this paper illustrate the value of applying select ISO 15189 recommendations for general laboratory to the more specialized area of Molecular Diagnostics. Copyright © 2016 Elsevier B.V. All rights reserved.

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

Science.gov (United States)

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Design and relevant sample calculations for a neutral particle energy diagnostic based on time of flight

Energy Technology Data Exchange (ETDEWEB)

Cecconello, M

1999-05-01

Extrap T2 will be equipped with a neutral particles energy diagnostic based on time of flight technique. In this report, the expected neutral fluxes for Extrap T2 are estimated and discussed in order to determine the feasibility and the limits of such diagnostic. These estimates are based on a 1D model of the plasma. The input parameters of such model are the density and temperature radial profiles of electrons and ions and the density of neutrals at the edge and in the centre of the plasma. The atomic processes included in the model are the charge-exchange and the electron-impact ionization processes. The results indicate that the plasma attenuation length varies from a/5 to a, a being the minor radius. Differential neutral fluxes, as well as the estimated power losses due to CX processes (2 % of the input power), are in agreement with experimental results obtained in similar devices. The expected impurity influxes vary from 10{sup 14} to 10{sup 11} cm{sup -2}s{sup -1}. The neutral particles detection and acquisition systems are discussed. The maximum detectable energy varies from 1 to 3 keV depending on the flight distances d. The time resolution is 0.5 ms. Output signals from the waveform recorder are foreseen in the range 0-200 mV. An 8-bit waveform recorder having 2 MHz sampling frequency and 100K sample of memory capacity is the minimum requirement for the acquisition system 20 refs, 19 figs.

Model-based failure detection for cylindrical shells from noisy vibration measurements.

Science.gov (United States)

Candy, J V; Fisher, K A; Guidry, B L; Chambers, D H

2014-12-01

Model-based processing is a theoretically sound methodology to address difficult objectives in complex physical problems involving multi-channel sensor measurement systems. It involves the incorporation of analytical models of both physical phenomenology (complex vibrating structures, noisy operating environment, etc.) and the measurement processes (sensor networks and including noise) into the processor to extract the desired information. In this paper, a model-based methodology is developed to accomplish the task of online failure monitoring of a vibrating cylindrical shell externally excited by controlled excitations. A model-based processor is formulated to monitor system performance and detect potential failure conditions. The objective of this paper is to develop a real-time, model-based monitoring scheme for online diagnostics in a representative structural vibrational system based on controlled experimental data.

Diagnostic Perspectives on the Family: Process, Structural and Historical Contextual Models.

Science.gov (United States)

Levant, Ronald F.

1983-01-01

Describes diagnostic perspectives for viewing dysfunctional families. Presents three general types of models (process, structural, and historical) and organized them along a continuum from most descriptive to most inferential. Presented at the 39th Annual Conference of the American Association for Marriage and Family Therapy, October-November…

American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: Evidence-Based Report on Diagnostic Guidelines

Science.gov (United States)

Conwell, Darwin L.; Lee, Linda S.; Yadav, Dhiraj; Longnecker, Daniel S.; Miller, Frank H.; Mortele, Koenraad J.; Levy, Michael J.; Kwon, Richard; Lieb, John G.; Stevens, Tyler; Toskes, Philip P.; Gardner, Timothy B.; Gelrud, Andres; Wu, Bechien U.; Forsmark, Christopher E.; Vege, Santhi S.

2016-01-01

The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed and evidence based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable or insufficient evidence. A diagnostic (STEP-wise; S-survey, T-tomography, E-endoscopy and P-pancreas function testing) algorithm is proposed that proceeds from a non-invasive to a more invasive approach. This algorithm maximizes specificity (low false positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Futhermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (T-toxic, I-idiopathic, G-genetic, A- autoimmune, R-recurrent and O-obstructive) etiology, gland morphology (Cambridge criteria) and physiologic state (exocrine, endocrine function) for uniformity across future multi-center research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves. PMID:25333398

American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: evidence-based report on diagnostic guidelines.

Science.gov (United States)

Conwell, Darwin L; Lee, Linda S; Yadav, Dhiraj; Longnecker, Daniel S; Miller, Frank H; Mortele, Koenraad J; Levy, Michael J; Kwon, Richard; Lieb, John G; Stevens, Tyler; Toskes, Phillip P; Gardner, Timothy B; Gelrud, Andres; Wu, Bechien U; Forsmark, Christopher E; Vege, Santhi S

2014-11-01

The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed, and evidence-based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable, or insufficient evidence. A diagnostic (STEP-wise; survey, tomography, endoscopy, and pancreas function testing) algorithm is proposed that proceeds from a noninvasive to a more invasive approach. This algorithm maximizes specificity (low false-positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Furthermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (toxic, idiopathic, genetic, autoimmune, recurrent, and obstructive) etiology, gland morphology (Cambridge criteria), and physiologic state (exocrine, endocrine function) for uniformity across future multicenter research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves.

Characteristics determination of Tanka X-ray Diagnostic Machine Model RTO-125

International Nuclear Information System (INIS)

Trijoko, Susetyo; Nasukha; Suyati; Nugroho, Agung.

1993-01-01

Characteristics determination of Tanka X-ray diagnostic machine model RTO-125. The characteristics of X-ray machine used for examining patient should be known. The characteristics studied in this paper include : X-ray beam profile, coincidence of the light field with radiation field, peak voltage, radiation quality, stability of exposures, and linearity of exposures against time. Beam profile and radiation-field alignment were determined using X-ray film. Winconsin kVp test cassette was used to measure peak voltage. The quality of the radiation, represented by half-value layer (HVL), was measured using aluminium step-wedge. Stability and linearity of exposures were measured using ionization chamber detector having an air volume of 40 cc. The results of this study were documented for the TANKA X-ray machine model RTO-125 of PSPKR BATAN, and the method of this study could be applied for X-ray diagnostic machine in general. (authors). 6 refs., 2 tabs., 6 figs

Bayesian analysis of longitudinal Johne's disease diagnostic data without a gold standard test

DEFF Research Database (Denmark)

Wang, C.; Turnbull, B.W.; Nielsen, Søren Saxmose

2011-01-01

the posterior estimates of the model parameters that provide the basis for inference concerning the accuracy of the diagnostic procedure. Based on the Bayesian approach, the posterior probability distribution of the change-point onset time can be obtained and used as a criterion for infection diagnosis......-point process with a Weibull survival hazard function was used to model the progression of the hidden disease status. The model adjusted for the fixed effects of covariate variables and random effects of subject on the diagnostic testing procedure. Markov chain Monte Carlo methods were used to compute....... An application is presented to an analysis of ELISA and fecal culture test outcomes in the diagnostic testing of paratuberculosis (Johne's disease) for a Danish longitudinal study from January 2000 to March 2003. The posterior probability criterion based on the Bayesian model with 4 repeated observations has...

Diagnosing Appendicitis: Evidence-Based Review of the Diagnostic Approach in 2014

Science.gov (United States)

Shogilev, Daniel J.; Duus, Nicolaj; Odom, Stephen R.; Shapiro, Nathan I.

2014-01-01

Introduction Acute appendicitis is the most common abdominal emergency requiring emergency surgery. However, the diagnosis is often challenging and the decision to operate, observe or further work-up a patient is often unclear. The utility of clinical scoring systems (namely the Alvarado score), laboratory markers, and the development of novel markers in the diagnosis of appendicitis remains controversial. This article presents an update on the diagnostic approach to appendicitis through an evidence-based review. Methods We performed a broad Medline search of radiological imaging, the Alvarado score, common laboratory markers, and novel markers in patients with suspected appendicitis. Results Computed tomography (CT) is the most accurate mode of imaging for suspected cases of appendicitis, but the associated increase in radiation exposure is problematic. The Alvarado score is a clinical scoring system that is used to predict the likelihood of appendicitis based on signs, symptoms and laboratory data. It can help risk stratify patients with suspected appendicitis and potentially decrease the use of CT imaging in patients with certain Alvarado scores. White blood cell (WBC), C-reactive protein (CRP), granulocyte count and proportion of polymorphonuclear (PMN) cells are frequently elevated in patients with appendicitis, but are insufficient on their own as a diagnostic modality. When multiple markers are used in combination their diagnostic utility is greatly increased. Several novel markers have been proposed to aid in the diagnosis of appendicitis; however, while promising, most are only in the preliminary stages of being studied. Conclusion While CT is the most accurate mode of imaging in suspected appendicitis, the accompanying radiation is a concern. Ultrasound may help in the diagnosis while decreasing the need for CT in certain circumstances. The Alvarado Score has good diagnostic utility at specific cutoff points. Laboratory markers have very limited

Cognitive diagnostic assessment via Bayesian evaluation of informative diagnostic hypotheses.

NARCIS (Netherlands)

Hoijtink, Herbert; Béland, Sébastien; Vermeulen, Jorine A.

2014-01-01

There exist diverse approaches that can be used for cognitive diagnostic assessment, such as mastery testing, constrained latent class analysis, rule space methodology, diagnostic cognitive modeling, and person-fit analysis. Each of these approaches can be used within 1 of the 4 psychometric

Cognitive Diagnostic Assessment via Bayesian Evaluation of Informative Diagnostic Hypotheses

NARCIS (Netherlands)

Hoitink, Herbert; Beland, Sebastien; Vermeulen, Jorine

2014-01-01

There exist diverse approaches that can be used for cognitive diagnostic assessment, such as mastery testing, constrained latent class analysis, rule space methodology, diagnostic cognitive modeling, and person-fit analysis. Each of these approaches can be used within 1 of the 4 psychometric

Optimization of a middle atmosphere diagnostic scheme

Science.gov (United States)

Akmaev, Rashid A.

1997-06-01

A new assimilative diagnostic scheme based on the use of a spectral model was recently tested on the CIRA-86 empirical model. It reproduced the observed climatology with an annual global rms temperature deviation of 3.2 K in the 15-110 km layer. The most important new component of the scheme is that the zonal forcing necessary to maintain the observed climatology is diagnosed from empirical data and subsequently substituted into the simulation model at the prognostic stage of the calculation in an annual cycle mode. The simulation results are then quantitatively compared with the empirical model, and the above mentioned rms temperature deviation provides an objective measure of the `distance' between the two climatologies. This quantitative criterion makes it possible to apply standard optimization procedures to the whole diagnostic scheme and/or the model itself. The estimates of the zonal drag have been improved in this study by introducing a nudging (Newtonian-cooling) term into the thermodynamic equation at the diagnostic stage. A proper optimal adjustment of the strength of this term makes it possible to further reduce the rms temperature deviation of simulations down to approximately 2.7 K. These results suggest that direct optimization can successfully be applied to atmospheric model parameter identification problems of moderate dimensionality.

Systematic review of proposed definitions of nocturnal polyuria and population-based evidence of their diagnostic accuracy.

Science.gov (United States)

Olesen, Tine Kold; Denys, Marie-Astrid; Vande Walle, Johan; Everaert, Karel

2018-02-06

Background Evidence of diagnostic accuracy for proposed definitions of nocturnal polyuria is currently unclear. Purpose Systematic review to determine population-based evidence of the diagnostic accuracy of proposed definitions of nocturnal polyuria based on data from frequency-volume charts. Methods Seventeen pre-specified search terms identified 351 unique investigations published from 1990 to 2016 in BIOSIS, Embase, Embase Alerts, International Pharmaceutical Abstract, Medline, and Cochrane. Thirteen original communications were included in this review based on pre-specified exclusion criteria. Data were extracted from each paper regarding subject age, sex, ethnicity, health status, sample size, data collection methods, and diagnostic discrimination of proposed definitions including sensitivity, specificity, positive and negative predictive value. Results The sample size of study cohorts, participant age, sex, ethnicity, and health status varied considerably in 13 studies reporting on the diagnostic performance of seven different definitions of nocturnal polyuria using frequency-volume chart data from 4968 participants. Most study cohorts were small, mono-ethnic, including only Caucasian males aged 50 or higher with primary or secondary polyuria that were compared to a control group of healthy men without nocturia in prospective or retrospective settings. Proposed definitions had poor discriminatory accuracy in evaluations based on data from subjects independent from the original study cohorts with findings being similar regarding the most widely evaluated definition endorsed by ICS. Conclusions Diagnostic performance characteristics for proposed definitions of nocturnal polyuria show poor to modest discrimination and are not based on sufficient level of evidence from representative, multi-ethnic population-based data from both females and males of all adult ages.

Large biases in regression-based constituent flux estimates: causes and diagnostic tools

Science.gov (United States)

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

Medical applications of model-based dynamic thermography

Science.gov (United States)

Nowakowski, Antoni; Kaczmarek, Mariusz; Ruminski, Jacek; Hryciuk, Marcin; Renkielska, Alicja; Grudzinski, Jacek; Siebert, Janusz; Jagielak, Dariusz; Rogowski, Jan; Roszak, Krzysztof; Stojek, Wojciech

2001-03-01

The proposal to use active thermography in medical diagnostics is promising in some applications concerning investigation of directly accessible parts of the human body. The combination of dynamic thermograms with thermal models of investigated structures gives attractive possibility to make internal structure reconstruction basing on different thermal properties of biological tissues. Measurements of temperature distribution synchronized with external light excitation allow registration of dynamic changes of local temperature dependent on heat exchange conditions. Preliminary results of active thermography applications in medicine are discussed. For skin and under- skin tissues an equivalent thermal model may be determined. For the assumed model its effective parameters may be reconstructed basing on the results of transient thermal processes. For known thermal diffusivity and conductivity of specific tissues the local thickness of a two or three layer structure may be calculated. Results of some medical cases as well as reference data of in vivo study on animals are presented. The method was also applied to evaluate the state of the human heart during the open chest cardio-surgical interventions. Reference studies of evoked heart infarct in pigs are referred, too. We see the proposed new in medical applications technique as a promising diagnostic tool. It is a fully non-invasive, clean, handy, fast and affordable method giving not only qualitative view of investigated surfaces but also an objective quantitative measurement result, accurate enough for many applications including fast screening of affected tissues.

The Diagnostic Efficacy of Cone-beam Computed Tomography in Endodontics: A Systematic Review and Analysis by a Hierarchical Model of Efficacy.

Science.gov (United States)

Rosen, Eyal; Taschieri, Silvio; Del Fabbro, Massimo; Beitlitum, Ilan; Tsesis, Igor

2015-07-01

The aim of this study was to evaluate the diagnostic efficacy of cone-beam computed tomographic (CBCT) imaging in endodontics based on a systematic search and analysis of the literature using an efficacy model. A systematic search of the literature was performed to identify studies evaluating the use of CBCT imaging in endodontics. The identified studies were subjected to strict inclusion criteria followed by an analysis using a hierarchical model of efficacy (model) designed for appraisal of the literature on the levels of efficacy of a diagnostic imaging modality. Initially, 485 possible relevant articles were identified. After title and abstract screening and a full-text evaluation, 58 articles (12%) that met the inclusion criteria were analyzed and allocated to levels of efficacy. Most eligible articles (n = 52, 90%) evaluated technical characteristics or the accuracy of CBCT imaging, which was defined in this model as low levels of efficacy. Only 6 articles (10%) proclaimed to evaluate the efficacy of CBCT imaging to support the practitioner's decision making; treatment planning; and, ultimately, the treatment outcome, which was defined as higher levels of efficacy. The expected ultimate benefit of CBCT imaging to the endodontic patient as evaluated by its level of diagnostic efficacy is unclear and is mainly limited to its technical and diagnostic accuracy efficacies. Even for these low levels of efficacy, current knowledge is limited. Therefore, a cautious and rational approach is advised when considering CBCT imaging for endodontic purposes. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Activity and longevity of antibody in paper-based blood typing diagnostics

Science.gov (United States)

Henderson, Clare A.; McLiesh, Heather; Then, Whui L.; Garnier, Gil

2018-05-01

Paper-based diagnostics provide a low-cost, reliable and easy to use mode of blood typing. The shelf-life of such products, however, can be limited due to the reduced activity of reagent antibodies sorbed on the paper cellulose fibres. This study explores the effects of ageing on antibody activity for periods up to twelve months on paper and in solution under different ageing and drying conditions - air-dried, lyophilised and kept as a liquid. Paper kept wet with undiluted antibody is shown to have the longest shelf-life and the clearest negatives. Antibody diluted with bovine serum albumin (BSA) protects against the lyophilisation process, however, beyond nine months ageing, false positives are seen. Paper with air-dried antibodies is not suitable for use after one month ageing. These results inform preparation and storage conditions for the development of long shelf-life blood grouping paper-based diagnostics.

Activity and Longevity of Antibody in Paper-Based Blood Typing Diagnostics

Directory of Open Access Journals (Sweden)

Clare A. Henderson

2018-05-01

Full Text Available Paper-based diagnostics provide a low-cost, reliable and easy to use mode of blood typing. The shelf-life of such products, however, can be limited due to the reduced activity of reagent antibodies sorbed on the paper cellulose fibers. This study explores the effects of aging on antibody activity for periods up to 12 months on paper and in solution under different aging and drying conditions—air-dried, lyophilized, and kept as a liquid. Paper kept wet with undiluted antibody is shown to have the longest shelf-life and the clearest negatives. Antibody diluted with bovine serum albumin (BSA protects against the lyophilization process, however, beyond 9 months aging, false positives are seen. Paper with air-dried antibodies is not suitable for use after 1 month aging. These results inform preparation and storage conditions for the development of long shelf-life blood grouping paper-based diagnostics.

Applying the Weisbord model as a diagnostic framework for organizational analysis

Directory of Open Access Journals (Sweden)

Kontić Ljiljana

2012-06-01

Full Text Available This study investigates the effectiveness of the Weisbord's Six Box Model as a diagnostic framework for assessing the factors affecting organizational development. The research area consisted of an international bank which operates in Serbia. In order to identify strengths and weaknesses in the bank, Weisbord's diagnostic questionnaire has been used. Respondents were 137 middle managers in the selected bank. The research results revealed that the bank has strengths in the areas of leadership, relations, purpose and helpful mechanisms. The weaker aspects were organizational structure and rewards. The options for improving structure, as well as rewards system, are suggested. The findings add to the existing literature on organizational diagnosis in cross-cultural contexts.

On Feature Relevance in Image-Based Prediction Models: An Empirical Study

DEFF Research Database (Denmark)

Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

2013-01-01

Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

Improving plasma shaping accuracy through consolidation of control model maintenance, diagnostic calibration, and hardware change control

International Nuclear Information System (INIS)

Baggest, D.S.; Rothweil, D.A.; Pang, S.

1995-12-01

With the advent of more sophisticated techniques for control of tokamak plasmas comes the requirement for increasingly more accurate models of plasma processes and tokamak systems. Development of accurate models for DIII-D power systems, vessel, and poloidal coils is already complete, while work continues in development of general plasma response modeling techniques. Increased accuracy in estimates of parameters to be controlled is also required. It is important to ensure that errors in supporting systems such as diagnostic and command circuits do not limit the accuracy of plasma parameter estimates or inhibit the ability to derive accurate plasma/tokamak system models. To address this issue, we have developed more formal power systems change control and power system/magnetic diagnostics calibration procedures. This paper discusses our approach to consolidating the tasks in these closely related areas. This includes, for example, defining criteria for when diagnostics should be re-calibrated along with required calibration tolerances, and implementing methods for tracking power systems hardware modifications and the resultant changes to control models

Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses

OpenAIRE

Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik

2016-01-01

Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflect...

Incremental Validity of Multidimensional Proficiency Scores from Diagnostic Classification Models: An Illustration for Elementary School Mathematics

Science.gov (United States)

Kunina-Habenicht, Olga; Rupp, André A.; Wilhelm, Oliver

2017-01-01

Diagnostic classification models (DCMs) hold great potential for applications in summative and formative assessment by providing discrete multivariate proficiency scores that yield statistically driven classifications of students. Using data from a newly developed diagnostic arithmetic assessment that was administered to 2032 fourth-grade students…

Atomic Models for Motional Stark Effects Diagnostics

Energy Technology Data Exchange (ETDEWEB)

Gu, M F; Holcomb, C; Jayakuma, J; Allen, S; Pablant, N A; Burrell, K

2007-07-26

We present detailed atomic physics models for motional Stark effects (MSE) diagnostic on magnetic fusion devices. Excitation and ionization cross sections of the hydrogen or deuterium beam traveling in a magnetic field in collisions with electrons, ions, and neutral gas are calculated in the first Born approximation. The density matrices and polarization states of individual Stark-Zeeman components of the Balmer {alpha} line are obtained for both beam into plasma and beam into gas models. A detailed comparison of the model calculations and the MSE polarimetry and spectral intensity measurements obtained at the DIII-D tokamak is carried out. Although our beam into gas models provide a qualitative explanation for the larger {pi}/{sigma} intensity ratios and represent significant improvements over the statistical population models, empirical adjustment factors ranging from 1.0-2.0 must still be applied to individual line intensities to bring the calculations into full agreement with the observations. Nevertheless, we demonstrate that beam into gas measurements can be used successfully as calibration procedures for measuring the magnetic pitch angle through {pi}/{sigma} intensity ratios. The analyses of the filter-scan polarization spectra from the DIII-D MSE polarimetry system indicate unknown channel and time dependent light contaminations in the beam into gas measurements. Such contaminations may be the main reason for the failure of beam into gas calibration on MSE polarimetry systems.

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

Energy Technology Data Exchange (ETDEWEB)

Tourassi, Georgia [ORNL; Voisin, Sophie [ORNL; Paquit, Vincent C [ORNL; Krupinski, Elizabeth [University of Arizona

2013-01-01

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By pooling the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.

Diagnostics of nitrogen deficiency in mini-cucumber plant by near ...

African Journals Online (AJOL)

K-nearest neighbors (KNN) and artificial neural network (ANN) were applied to build diagnostics models, respectively. Some parameters of the model were optimized by cross-validation. The performance of the KNN model and the ANN model based on NIRS data was compared. Experiment results showed that the ANN ...

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

Science.gov (United States)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

Investigating the link between radiologists’ gaze, diagnostic decision, and image content

Science.gov (United States)

Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent; Krupinski, Elizabeth

2013-01-01

Objective To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods Gaze data and diagnostic decisions were collected from three breast imaging radiologists and three radiology residents who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Image analysis was performed in mammographic regions that attracted radiologists’ attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results By pooling the data from all readers, machine learning produced highly accurate predictive models linking image content, gaze, and cognition. Potential linking of those with diagnostic error was also supported to some extent. Merging readers’ gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the readers’ diagnostic errors while confirming 97.3% of their correct diagnoses. The readers’ individual perceptual and cognitive behaviors could be adequately predicted by modeling the behavior of others. However, personalized tuning was in many cases beneficial for capturing more accurately individual behavior. Conclusions There is clearly an interaction between radiologists’ gaze, diagnostic decision, and image content which can be modeled with machine learning algorithms. PMID:23788627

Screen printed paper-based diagnostic devices with polymeric inks.

Science.gov (United States)

Sun, Ju-Yen; Cheng, Chao-Min; Liao, Ying-Chih

2015-01-01

A simple and low-cost fabrication method for paper-based diagnostic devices (PBDDs) is described in this study. Street-available polymer solutions were screen printed onto filter papers to create hydrophobic patterns for fluidic channels. In order to obtain fully functional hydrophobic patterns for fluids, the original polymer solutions were diluted with butyl acetate to yield a suitable viscosity range between 30-200 cP for complete patterning on paper. Typical pH and glucose tests with color indicators were performed on the screen printed PBDDs. Images of the PBDDs were analyzed by computers to obtain calibration curves for pH between 2 and 12 and glucose concentration ranging from 10-1000 mmol dm(-3). Detection of formaldehyde in acetone was also carried out to show the possibility of using this PBBD for analytical detection with organic solvents. An exemplar PBDD with simultaneous pH and glucose detection was also used to demonstrate the feasibility of applying this technique for realistic diagnostic applications.

Extracellular gadolinium-based contrast media: Differences in diagnostic efficacy

Energy Technology Data Exchange (ETDEWEB)

Molen, Aart J. van der [Department of Radiology C-2S, Leiden University Medical Centre, Albinusdreef 2, NL-2333 ZA Leiden (Netherlands)], E-mail: molen@lumc.nl; Bellin, Marie-France [Universite Paris-Sud XI, AP-HP, Service de Radiologie, Hopital Paul Brousse, 12-14 Avenue Paul Vaillant Couturier, F-94804 Villejuif Cedex (France)

2008-05-15

Since the introduction of the first gadolinium-based contrast agent (Gd-CA) in 1988 it has become clear that these agents significantly improve the diagnostic efficacy of MRI. Studies on single agents have shown that, in comparison to unenhanced sequences, all agents help to improve the detection and delineation of lesions which can alter diagnosis in up to 40% of patients. Doubling or tripling the standard dose of 0.1 mmol/kg body weight may be beneficial for selected indications (e.g. brain perfusion, equivocal single dose study in MRI for brain metastasis, small vessel MR angiography). A more limited number of studies have compared the various agents. These studies do not show clinically significant differences in diagnostic efficacy between the various extracellular Gd-CA. Agents with higher concentration or protein binding may be relatively better suitable for selected applications (e.g. perfusion MRI). The higher relaxivity agents may be used in somewhat lower doses than the extracellular agents.

Extracellular gadolinium-based contrast media: Differences in diagnostic efficacy

International Nuclear Information System (INIS)

Molen, Aart J. van der; Bellin, Marie-France

2008-01-01

Since the introduction of the first gadolinium-based contrast agent (Gd-CA) in 1988 it has become clear that these agents significantly improve the diagnostic efficacy of MRI. Studies on single agents have shown that, in comparison to unenhanced sequences, all agents help to improve the detection and delineation of lesions which can alter diagnosis in up to 40% of patients. Doubling or tripling the standard dose of 0.1 mmol/kg body weight may be beneficial for selected indications (e.g. brain perfusion, equivocal single dose study in MRI for brain metastasis, small vessel MR angiography). A more limited number of studies have compared the various agents. These studies do not show clinically significant differences in diagnostic efficacy between the various extracellular Gd-CA. Agents with higher concentration or protein binding may be relatively better suitable for selected applications (e.g. perfusion MRI). The higher relaxivity agents may be used in somewhat lower doses than the extracellular agents

Monte Carlo model of diagnostic X-ray dosimetry

International Nuclear Information System (INIS)

Khrutchinsky, Arkady; Kutsen, Semion; Gatskevich, George

2008-01-01

Full text: A Monte Carlo simulation of absorbed dose distribution in patient's tissues is often used in a dosimetry assessment of X-ray examinations. The results of such simulations in Belarus are presented in the report based on an anthropomorphic tissue-equivalent Rando-like physical phantom. The phantom corresponds to an adult 173 cm high and of 73 kg and consists of a torso and a head made of tissue-equivalent plastics which model soft (muscular), bone, and lung tissues. It consists of 39 layers (each 25 mm thick), including 10 head and neck ones, 16 chest and 13 pelvis ones. A tomographic model of the phantom has been developed from its CT-scan images with a voxel size of 0.88 x 0.88 x 4 mm 3 . A necessary pixelization in Mathematics-based in-house program was carried out for the phantom to be used in the radiation transport code MCNP-4b. The final voxel size of 14.2 x 14.2 x 8 mm 3 was used for the reasonable computer consuming calculations of absorbed dose in tissues and organs in various diagnostic X-ray examinations. MCNP point detectors allocated through body slices obtained as a result of the pixelization were used to calculate the absorbed dose. X-ray spectra generated by the empirical TASMIP model were verified on the X-ray units MEVASIM and SIREGRAPH CF. Absorbed dose distributions in the phantom volume were determined by the corresponding Monte Carlo simulations with a set of point detectors. Doses in organs of the adult phantom computed from the absorbed dose distributions by another Mathematics-based in-house program were estimated for 22 standard organs for various standard X-ray examinations. The results of Monte Carlo simulations were compared with the results of direct measurements of the absorbed dose in the phantom on the X-ray unit SIREGRAPH CF with the calibrated thermo-luminescent dosimeter DTU-01. The measurements were carried out in specified locations of different layers in heart, lungs, liver, pancreas, and stomach at high voltage of

Power System Transient Diagnostics Based on Novel Traveling Wave Detection

Science.gov (United States)

Hamidi, Reza Jalilzadeh

Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma

ORNL diagnostic and modeling development for LAPD ICRF experiments

Science.gov (United States)

Isler, R. C.; Caughman, J. B. O.; Lau, C.; Martin, E. H.; Perkins, R. J.; Compernolle, B. Van; Vincena, S.; Tripathi, S. K. P.; Gekelman, W.

2017-10-01

PPPL, UCLA, and ORNL scientists have recently collaborated on a three week ICRF campaign at the upgraded LAPD device to study near field-plasma interactions associated with a single strap antenna driven at 2.38 MHz with 100 kW of RF power. This poster highlights ORNL involvement through implementation of the following diagnostics: an optical emission probe to measure neutral density, a retarding field energy analyzer to measure fast ions, phase locked imaging to measure line integrated RF-driven optical emission fluctuations, and an RF compensated triple Langmuir probe to measure density and temperature. To interpret the results of the experimental campaign a 3D cold plasma finite element model with realistic antenna and vacuum vessel geometry was developed in COMSOL. A summary of these results will be discussed. Highlights include a proof of principle localized and spatially resolved measurement of the neutral density, a strong increase in RF-driven optical emission fluctuations directly in front of the RF antenna strap, a shift in fast ion energies near the plasma edge, and qualitative agreement between the COMSOL cold plasma model with the various diagnostics. Funded by the DOE OFES (DE-AC05-00OR22725, DE-AC02-09CH11466, and DE-FC02-07ER54918) and the Univ. of California (12-LR-237124).

Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

Directory of Open Access Journals (Sweden)

Turgay Ayer

2013-01-01

Full Text Available Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs, in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.

Science.gov (United States)

Petricoin, Emanuel F; Liotta, Lance A

2004-02-01

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.

Integrating nursing diagnostic concepts into the medical entities dictionary using the ISO Reference Terminology Model for Nursing Diagnosis.

Science.gov (United States)

Hwang, Jee-In; Cimino, James J; Bakken, Suzanne

2003-01-01

The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED. The authors dissected nursing diagnostic terms from two source terminologies (Home Health Care Classification and the Omaha System) into the semantic categories of the ISO model. Consistent with the ISO model, they selected Focus and Judgment as required semantic categories for creating intensional definitions of nursing diagnostic concepts in the MED. Because the MED does not include Focus and Judgment hierarchies, the authors developed them to define the nursing diagnostic concepts. The ISO model was sufficient for dissecting the source terminologies into atomic terms. The authors identified 162 unique focus concepts from the 266 nursing diagnosis terms for inclusion in the Focus hierarchy. For the Judgment hierarchy, the authors precoordinated Judgment and Potentiality instead of using Potentiality as a qualifier of Judgment as in the ISO model. Impairment and Alteration were the most frequently occurring judgments. Nursing care represents a large proportion of health care activities; thus, it is vital that terms used by nurses are integrated into concept-oriented terminologies that provide broad coverage for the domain of health care. This study supports the utility of the ISO Reference Terminology Model for Nursing Diagnoses as a facilitator for the integration process.

Beam-Based Diagnostics of RF-Breakdown in the Two-Beam Test-Stand in CTF3

CERN Document Server

Johnson, M

2007-01-01

The general outline of a beam-based diagnostic method of RF-breakdown, using BPMs, at the two-beam test-stand in CTF3 is discussed. The basic components of the set-up and their functions in the diagnostic are described. Estimations of the expected error in the measured parameters are performed.

Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection.

Science.gov (United States)

Tchesnokova, Veronika; Avagyan, Hovhannes; Rechkina, Elena; Chan, Diana; Muradova, Mariya; Haile, Helen Ghirmai; Radey, Matthew; Weissman, Scott; Riddell, Kim; Scholes, Delia; Johnson, James R; Sokurenko, Evgeni V

2017-01-01

Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (pre-culture) antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs), which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25-35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by ≥ 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.

Development of a Diagnostic Prediction Model for Conductive Conditions in Neonates Using Wideband Acoustic Immittance.

Science.gov (United States)

Myers, Joshua; Kei, Joseph; Aithal, Sreedevi; Aithal, Venkatesh; Driscoll, Carlie; Khan, Asaduzzaman; Manuel, Alehandrea; Joseph, Anjali; Malicka, Alicja N

2018-03-03

Wideband acoustic immittance (WAI) is an emerging test of middle-ear function with potential applications for neonates in screening and diagnostic settings. Previous large-scale diagnostic accuracy studies have assessed the performance of WAI against evoked otoacoustic emissions, but further research is needed using a more stringent reference standard. Research into suitable quantitative techniques to analyze the large volume of data produced by WAI is still in its infancy. Prediction models are an attractive method for analysis of multivariate data because they provide individualized probabilities that a subject has the condition. A clinically useful prediction model must accurately discriminate between normal and abnormal cases and be well calibrated (i.e., give accurate predictions). The present study aimed to develop a diagnostic prediction model for detecting conductive conditions in neonates using WAI. A stringent reference standard was created by combining results of high-frequency tympanometry and distortion product otoacoustic emissions. High-frequency tympanometry and distortion product otoacoustic emissions were performed on both ears of 629 healthy neonates to assess outer- and middle-ear function. Wideband absorbance and complex admittance (magnitude and phase) were measured at frequencies ranging from 226 to 8000 Hz in each neonate at ambient pressure using a click stimulus. Results from one ear of each neonate were used to develop the prediction model. WAI results were used as logistic regression predictors to model the probability that an ear had outer/middle-ear dysfunction. WAI variables were modeled both linearly and nonlinearly, to test whether allowing nonlinearity improved model fit and thus calibration. The best-fitting model was validated using the opposite ears and with bootstrap resampling. The best-fitting model used absorbance at 1000 and 2000 Hz, admittance magnitude at 1000 and 2000 Hz, and admittance phase at 1000 and 4000 Hz modeled

Final Report for 'Modeling Electron Cloud Diagnostics for High-Intensity Proton Accelerators'

International Nuclear Information System (INIS)

Veitzer, Seth A.

2009-01-01

Electron clouds in accelerators such as the ILC degrade beam quality and limit operating efficiency. The need to mitigate electron clouds has a direct impact on the design and operation of these accelerators, translating into increased cost and reduced performance. Diagnostic techniques for measuring electron clouds in accelerating cavities are needed to provide an assessment of electron cloud evolution and mitigation. Accurate numerical modeling of these diagnostics is needed to validate the experimental techniques. In this Phase I, we developed detailed numerical models of microwave propagation through electron clouds in accelerating cavities with geometries relevant to existing and future high-intensity proton accelerators such as Project X and the ILC. Our numerical techniques and simulation results from the Phase I showed that there was a high probability of success in measuring both the evolution of electron clouds and the effects of non-uniform electron density distributions in Phase II.

Meta-analysis for diagnostic accuracy studies: a new statistical model using beta-binomial distributions and bivariate copulas.

Science.gov (United States)

Kuss, Oliver; Hoyer, Annika; Solms, Alexander

2014-01-15

There are still challenges when meta-analyzing data from studies on diagnostic accuracy. This is mainly due to the bivariate nature of the response where information on sensitivity and specificity must be summarized while accounting for their correlation within a single trial. In this paper, we propose a new statistical model for the meta-analysis for diagnostic accuracy studies. This model uses beta-binomial distributions for the marginal numbers of true positives and true negatives and links these margins by a bivariate copula distribution. The new model comes with all the features of the current standard model, a bivariate logistic regression model with random effects, but has the additional advantages of a closed likelihood function and a larger flexibility for the correlation structure of sensitivity and specificity. In a simulation study, which compares three copula models and two implementations of the standard model, the Plackett and the Gauss copula do rarely perform worse but frequently better than the standard model. We use an example from a meta-analysis to judge the diagnostic accuracy of telomerase (a urinary tumor marker) for the diagnosis of primary bladder cancer for illustration. Copyright © 2013 John Wiley & Sons, Ltd.

Integrating Nursing Diagnostic Concepts into the Medical Entities Dictionary Using the ISO Reference Terminology Model for Nursing Diagnosis

OpenAIRE

Hwang, Jee-In; Cimino, James J.; Bakken, Suzanne

2003-01-01

Objective: The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED.

An intercomparison of several diagnostic meteorological processors used in mesoscale air quality modeling

Energy Technology Data Exchange (ETDEWEB)

Vimont, J.C. [National Park Service, Lakewood, CO (United States); Scire, J.S. [Sigma Research Corp., Concord, MA (United States)

1994-12-31

A major component, and area of uncertainty, in mesoscale air quality modeling, is the specification of the meteorological fields which affect the transport and dispersion of pollutants. Various options are available for estimating the wind and mixing depth fields over a mesoscale domain. Estimates of the wind field can be obtained from spatial and temporal interpolation of available observations or from diagnostic meteorological models, which estimate a meteorological field from available data and adjust those fields based on parameterizations of physical processes. A major weakness of these processors is their dependence on spatially and temporally sparse input data, particularly upper air data. These problems are exacerbated in regions of complex terrain and along the shorelines of large bodies of water. Similarly, the estimation of mixing depth is also reliant upon sparse observations and the parameterization of the convective and mechanical processes. The meteorological processors examined in this analysis were developed to drive different Lagrangian puff models. This paper describes the algorithms these processors use to estimate the wind fields and mixing depth fields.

Mobile phone-based biosensing: An emerging "diagnostic and communication" technology.

Science.gov (United States)

Quesada-González, Daniel; Merkoçi, Arben

2017-06-15

In this review we discuss recent developments on the use of mobile phones and similar devices for biosensing applications in which diagnostics and communications are coupled. Owing to the capabilities of mobile phones (their cameras, connectivity, portability, etc.) and to advances in biosensing, the coupling of these two technologies is enabling portable and user-friendly analytical devices. Any user can now perform quick, robust and easy (bio)assays anywhere and at any time. Among the most widely reported of such devices are paper-based platforms. Herein we provide an overview of a broad range of biosensing possibilities, from optical to electrochemical measurements; explore the various reported designs for adapters; and consider future opportunities for this technology in fields such as health diagnostics, safety & security, and environment monitoring. Copyright © 2016 Elsevier B.V. All rights reserved.

ADIPOSITY-BASED CHRONIC DISEASE AS A NEW DIAGNOSTIC TERM: THE AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY POSITION STATEMENT.

Science.gov (United States)

Mechanick, Jeffrey I; Hurley, Daniel L; Garvey, W Timothy

2017-03-01

The American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) have created a chronic care model, advanced diagnostic framework, clinical practice guidelines, and clinical practice algorithm for the comprehensive management of obesity. This coordinated effort is not solely based on body mass index as in previous models, but emphasizes a complications-centric approach that primarily determines therapeutic decisions and desired outcomes. Adiposity-Based Chronic Disease (ABCD) is a new diagnostic term for obesity that explicitly identifies a chronic disease, alludes to a precise pathophysiologic basis, and avoids the stigmata and confusion related to the differential use and multiple meanings of the term "obesity." Key elements to further the care of patients using this new ABCD term are: (1) positioning lifestyle medicine in the promotion of overall health, not only as the first algorithmic step, but as the central, pervasive action; (2) standardizing protocols that comprehensively and durably address weight loss and management of adiposity-based complications; (3) approaching patient care through contextualization (e.g., primordial prevention to decrease obesogenic environmental risk factors and transculturalization to adapt evidence-based recommendations for different ethnicities, cultures, and socio-economics); and lastly, (4) developing evidence-based strategies for successful implementation, monitoring, and optimization of patient care over time. This AACE/ACE blueprint extends current work and aspires to meaningfully improve both individual and population health by presenting a new ABCD term for medical diagnostic purposes, use in a complications-centric management and staging strategy, and precise reference to the obesity chronic disease state, divested from counterproductive stigmata and ambiguities found in the general public sphere. AACE = American Association of Clinical Endocrinologists ABCD = Adiposity-Based

What's in a Name? Health Care Providers' Perceptions of Pediatric Pain Patients Based on Diagnostic Labels.

Science.gov (United States)

Betsch, Taylor A; Gorodzinsky, Ayala Y; Finley, G A; Sangster, Michael; Chorney, Jill

2017-08-01

Diagnostic labels can help patients better understand their symptoms and can influence providers' treatment planning and patient interactions. Recurrent pain is common in childhood; however, there are various diagnostic labels used. The objective of this study was to evaluate the influence of diagnostic labels on pediatric health care providers' perceptions of pediatric chronic pain patients. Using an online survey, providers were randomly assigned to 1 of 2 vignette conditions (differing only in diagnostic label provided) and completed questionnaires about their perceptions of the vignette patient. Responses from 58 participants were analyzed. The 2 groups, based on diagnostic conditions used (fibromyalgia and chronic widespread pain) did not differ significantly on general demographics and health care providers' perceptions of the patient. Perceived origin of the pain influenced providers' perceptions; pain of a perceived medical origin was negatively correlated with stigmatization and positively correlated with sympathy. Perceived psychological origin was positively correlated with stigmatization and providers' age. Health care providers' perceptions of children's pain are more likely influenced by the presumed etiology rather than the diagnostic label used. Pain believed to be more medically based was associated with more positive reactions from providers (ie, less stigmatization). Older providers in particular perceived the patient more negatively if they believe the pain to be psychologically based. The findings of this pediatric study replicated findings from adult literature on chronic pain, suggesting that children and adults are subject to negative perceptions from health care providers when the providers believe the pain to be psychological in origin.

Modeling Complex Workflow in Molecular Diagnostics

Science.gov (United States)

Gomah, Mohamed E.; Turley, James P.; Lu, Huimin; Jones, Dan

2010-01-01

One of the hurdles to achieving personalized medicine has been implementing the laboratory processes for performing and reporting complex molecular tests. The rapidly changing test rosters and complex analysis platforms in molecular diagnostics have meant that many clinical laboratories still use labor-intensive manual processing and testing without the level of automation seen in high-volume chemistry and hematology testing. We provide here a discussion of design requirements and the results of implementation of a suite of lab management tools that incorporate the many elements required for use of molecular diagnostics in personalized medicine, particularly in cancer. These applications provide the functionality required for sample accessioning and tracking, material generation, and testing that are particular to the evolving needs of individualized molecular diagnostics. On implementation, the applications described here resulted in improvements in the turn-around time for reporting of more complex molecular test sets, and significant changes in the workflow. Therefore, careful mapping of workflow can permit design of software applications that simplify even the complex demands of specialized molecular testing. By incorporating design features for order review, software tools can permit a more personalized approach to sample handling and test selection without compromising efficiency. PMID:20007844

Development of a robust model-based reactivity control system

International Nuclear Information System (INIS)

Rovere, L.A.; Otaduy, P.J.; Brittain, C.R.

1990-01-01

This paper describes the development and implementation of a digital model-based reactivity control system that incorporates a knowledge of the plant physics into the control algorithm to improve system performance. This controller is composed of a model-based module and modified proportional-integral-derivative (PID) module. The model-based module has an estimation component to synthesize unmeasurable process variables that are necessary for the control action computation. These estimated variables, besides being used within the control algorithm, will be used for diagnostic purposes by a supervisory control system under development. The PID module compensates for inaccuracies in model coefficients by supplementing the model-based output with a correction term that eliminates any demand tracking or steady state errors. This control algorithm has been applied to develop controllers for a simulation of liquid metal reactors in a multimodular plant. It has shown its capability to track demands in neutron power much more accurately than conventional controllers, reducing overshoots to almost negligible value while providing a good degree of robustness to unmodeled dynamics. 10 refs., 4 figs

Development of a diagnostic technique based on Cherenkov effect for measurements of fast electrons in fusion devices

Energy Technology Data Exchange (ETDEWEB)

Plyusnin, V. V.; Duarte, P.; Fernandes, H.; Silva, C. [Association Euratom/IST, Instituto de Plasmas e Fusao Nuclear, Instituto Superior Tecnico, Universidade Tecnica de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa (Portugal); Jakubowski, L.; Zebrowski, J.; Malinowski, K.; Rabinski, M.; Sadowski, M. J. [National Centre for Nuclear Research (NCBJ), 7 Andrzeja Soltana Str., 05-400 Otwock (Poland)

2012-08-15

A diagnostic technique based on the Cherenkov effect is proposed for detection and characterization of fast (super-thermal and runaway) electrons in fusion devices. The detectors of Cherenkov radiation have been specially designed for measurements in the ISTTOK tokamak. Properties of several materials have been studied to determine the most appropriate one to be used as a radiator of Cherenkov emission in the detector. This technique has enabled the detection of energetic electrons (70 keV and higher) and the determination of their spatial and temporal variations in the ISTTOK discharges. Measurement of hard x-ray emission has also been carried out in experiments for validation of the measuring capabilities of the Cherenkov-type detector and a high correlation was found between the data of both diagnostics. A reasonable agreement was found between experimental data and the results of numerical modeling of the runaway electron generation in ISTTOK.

Efficient Probabilistic Diagnostics for Electrical Power Systems

Science.gov (United States)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Diagnostic system and diagnostic experiences at the Paks Nuclear Power Plant

International Nuclear Information System (INIS)

Katona, Tamas

1986-01-01

The major functions of the diagnostic system of the first two units of the Paks Nuclear Power Plant are as follows: monitoring the mechanical integrity of the reactor and the primary coolant circuit by means of vibration diagnostics; leakage detection of the primary coolant circuit by means of high frequency sonic analysis; loose parts monitoring based on the analysis of high frequency signals of acceleration detectors; and monitoring the vibration state of the turbines and rotary machines by the latter method or by a procedure based on the detection of mechanical vibrations. Up-to-date vibration diagnostics is based on the information supplied by either acceleration detectors or pressure fluctuation detectors, or in-core and ex-core neutron detectors. (V.N.)

Music-based Autism Diagnostics (MUSAD) - A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism.

Science.gov (United States)

Bergmann, Thomas; Sappok, Tanja; Diefenbacher, Albert; Dames, Sibylle; Heinrich, Manuel; Ziegler, Matthias; Dziobek, Isabel

2015-01-01

The MUSAD was developed as a diagnostic observational instrument in an interactional music framework. It is based on the ICD-10/DSM-5 criteria for autism spectrum disorder (ASD) and was designed to assess adults on a lower level of functioning, including individuals with severe language impairments. This study aimed to evaluate the psychometric properties of the newly developed instrument. Calculations were based on a consecutive clinical sample of N=76 adults with intellectual and developmental disabilities (IDD) suspected of ASD. Objectivity, test-retest reliability, and construct validity were calculated and a confirmatory factor analysis was applied to verify a reduced and optimized test version. The structural model showed a good fit, while internal consistency of the subscales was excellent (ω>.92). Item difficulties ranged between .04≤pi≤.82 and item-total correlation from .21 to .85. Objectivity was assessed by comparing the scorings of two external raters based on a subsample of n=12; interrater agreement was .71 (ICC 2, 1). Reliability was calculated for four test repetitions: the average ICC (3, 1) was .69. Convergent ASD measures correlated significantly with the MUSAD, while the discriminant Modified Overt Aggression Scale (MOAS) showed no significant overlap. Confirmation of factorial structure and acceptable psychometric properties suggest that the MUSAD is a promising new instrument for diagnosing ASD in adults with IDD. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection.

Directory of Open Access Journals (Sweden)

Veronika Tchesnokova

Full Text Available Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (pre-culture antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs, which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25-35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by ≥ 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.

Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

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Duarte, Adam; Adams, Michael J.; Peterson, James T.

2018-01-01

Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision

Mobile diagnostics: next-generation technologies for in vitro diagnostics.

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Shin, Joonchul; Chakravarty, Sudesna; Choi, Wooseok; Lee, Kyungyeon; Han, Dongsik; Hwang, Hyundoo; Choi, Jaekyu; Jung, Hyo-Il

2018-03-26

The emergence of a wide range of applications of smartphones along with advances in 'liquid biopsy' has significantly propelled medical research particularly in the field of in vitro diagnostics (IVD). Herein, we have presented a detailed analysis of IVD, its associated critical concerns and probable solutions. It also demonstrates the transition in terms of analytes from minimally invasive (blood) to non-invasive (urine, saliva and sweat) and depicts how the different features of a smartphone can be integrated for specific diagnostic purposes. This review basically highlights recent advances in the applications of smartphone-based biosensors in IVD taking into account the following factors: accuracy and portability; quantitative and qualitative analysis; and centralization and decentralization tests. Furthermore, the critical concerns and future direction of diagnostics based on smartphones are also discussed.

Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

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Mendiguren, Gorka; Koch, Julian; Stisen, Simon

2017-11-01

Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

Modeling pulsed excitation for gas-phase laser diagnostics

International Nuclear Information System (INIS)

Settersten, Thomas B.; Linne, Mark A.

2002-01-01

Excitation dynamics for pulsed optical excitation are described with the density-matrix equations and the rate equations for a two-level system. A critical comparison of the two descriptions is made with complete and consistent formalisms that are amenable to the modeling of applied laser-diagnostic techniques. General solutions, resulting from numerical integration of the differential equations describing the excitation process, are compared for collisional conditions that range from the completely coherent limit to the steady-state limit, for which the two formalisms are identical. This analysis demonstrates the failure of the rate equations to correctly describe the transient details of the excitation process outside the steady-state limit. However, reasonable estimates of the resultant population are obtained for nonsaturating (linear) excitation. This comparison provides the laser diagnostician with the means to evaluate the appropriate model for excitation through a simple picture of the breakdown of the rate-equation validity

Clinical usefulness of a biomarker-based diagnostic test for acute stroke: the Biomarker Rapid Assessment in Ischemic Injury (BRAIN) study.

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Laskowitz, Daniel T; Kasner, Scott E; Saver, Jeffrey; Remmel, Kerri S; Jauch, Edward C

2009-01-01

One of the significant limitations in the evaluation and management of patients with suspected acute cerebral ischemia is the absence of a widely available, rapid, and sensitive diagnostic test. The objective of the current study was to assess whether a test using a panel of biomarkers might provide useful diagnostic information in the early evaluation of stroke by differentiating patients with cerebral ischemia from other causes of acute neurological deficit. A total of 1146 patients presenting with neurological symptoms consistent with possible stroke were prospectively enrolled at 17 different sites. Timed blood samples were assayed for matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and protein S100beta. A separate cohort of 343 patients was independently enrolled to validate the multiple biomarker model approach. A diagnostic tool incorporating the values of matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and S-100beta into a composite score was sensitive for acute cerebral ischemia. The multivariate model demonstrated modest discriminative capabilities with an area under the receiver operating characteristic curve of 0.76 for hemorrhagic stroke and 0.69 for all stroke (likelihood test P<0.001). When the threshold for the logistic model was set at the first quartile, this resulted in a sensitivity of 86% for detecting all stroke and a sensitivity of 94% for detecting hemorrhagic stroke. Moreover, results were reproducible in a separate cohort tested on a point-of-care platform. These results suggest that a biomarker panel may add valuable and time-sensitive diagnostic information in the early evaluation of stroke. Such an approach is feasible on a point-of-care platform. The rapid identification of patients with suspected stroke would expand the availability of time-limited treatment strategies. Although the diagnostic accuracy of the current panel is clearly imperfect, this study demonstrates the feasibility of incorporating a

Diagnostics of helium plasma by collisional-radiative modeling and optical emission spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Lee, Wonwook; Kwon, Duck-Hee [KAERI, Daejeon (Korea, Republic of)

2015-05-15

Optical diagnostics for the electron temperature (T{sub e}) and the electron density (n{sub e}) of fusion plasma is important for understanding and controlling the edge and the divertor plasmas in tokamak. Since the line intensity ratio method using the collisional-radiative modeling and OES (optical emission spectroscopy) is simple and does not disturb the plasma, many fusion devices with TEXTOR, JET, JT-60U, LHD, and so on, have employed the line intensity ratio method as a basic diagnostic tool for neutral helium (He I). The accuracy of the line intensity ratio method depends on the reliability of the cross sections and rate coefficients. We performed state-of-the-art R-matrix calculations including couplings up to n=7 states and the distorted wave (DW) calculations for the electron-impact excitation (EIE) cross sections of He I using the flexible atomic code (FAC). The collisional-radiative model for He I was constructed using the calculated the cross sections. The helium collisional-radiative model for He I was constructed to diagnose the electron temperature and the electron density of the plasma. The electron temperature and density were determined by using the line intensity ratio method.

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Edelen, A. L.; Biedron, S. G.; Milton, S. V.; Edelen, J. P.

2016-12-16

At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.

Diagnostic test of predicted height model in Indonesian elderly: a study in an urban area

Directory of Open Access Journals (Sweden)

Fatmah Fatmah

2010-08-01

Full Text Available Aim In an anthropometric assessment, elderly are frequently unable to measure their height due to mobility and skeletal deformities. An alternative is to use a surrogate value of stature from arm span, knee height, and sitting height. The equations developed for predicting height in Indonesian elderly using these three predictors. The equations put in the nutritional assessment card (NSA of older people. Before the card which is the first new technology in Indonesia will be applied in the community, it should be tested. The study aimed was to conduct diagnostic test of predicted height model in the card compared to actual height.Methods Model validation towards 400 healthy elderly conducted in Jakarta City with cross-sectional design. The study was the second validation test of the model besides Depok City representing semi urban area which was undertaken as the first study.Result Male elderly had higher mean age, height, weight, arm span, knee height, and sitting height as compared to female elderly. The highest correlation between knee height and standing height was similar in women (r = 0.80; P < 0.001 and men (r = 0.78; P < 0.001, and followed by arm span and sitting height. Knee height had the lowest difference with standing height in men (3.13 cm and women (2.79 cm. Knee height had the biggest sensitivity (92.2%, and the highest specificity on sitting height (91.2%.Conclusion Stature prediction equation based on knee-height, arm span, and sitting height are applicable for nutritional status assessment in Indonesian elderly. (Med J Indones 2010;19:199-204Key words: diagnostic test, elderly, predicted height model

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

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Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Wright, Stephanie

2009-01-01

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Ares I-X Ground Diagnostic Prototype

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Schwabacher, Mark A.; Martin, Rodney Alexander; Waterman, Robert D.; Oostdyk, Rebecca Lynn; Ossenfort, John P.; Matthews, Bryan

2010-01-01

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35

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Xu, Xueli; von Davier, Matthias

2008-01-01

The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume…

Crack identification for reinforced concrete using PZT based smart rebar active sensing diagnostic network

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Song, N. N.; Wu, F.

2016-04-01

An active sensing diagnostic system using PZT based smart rebar for SHM of RC structure has been currently under investigation. Previous test results showed that the system could detect the de-bond of concrete from reinforcement, and the diagnostic signals were increased exponentially with the de-bonding size. Previous study also showed that the smart rebar could function well like regular reinforcement to undertake tension stresses. In this study, a smart rebar network has been used to detect the crack damage of concrete based on guided waves. Experimental test has been carried out for the study. In the test, concrete beams with 2 reinforcements have been built. 8 sets of PZT elements were mounted onto the reinforcement bars in an optimized way to form an active sensing diagnostic system. A 90 kHz 5-cycle Hanning-windowed tone burst was used as input. Multiple cracks have been generated on the concrete structures. Through the guided bulk waves propagating in the structures from actuators and sensors mounted from different bars, crack damage could be detected clearly. Cases for both single and multiple cracks were tested. Different crack depths from the surface and different crack numbers have been studied. Test result shows that the amplitude of sensor output signals is deceased linearly with a propagating crack, and is decreased exponentially with increased crack numbers. From the study, the active sensing diagnostic system using PZT based smart rebar network shows a promising way to provide concrete crack damage information through the "talk" among sensors.

Diagnostic criteria, clinical features, and incidence of thyroid storm based on nationwide surveys.

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Akamizu, Takashi; Satoh, Tetsurou; Isozaki, Osamu; Suzuki, Atsushi; Wakino, Shu; Iburi, Tadao; Tsuboi, Kumiko; Monden, Tsuyoshi; Kouki, Tsuyoshi; Otani, Hajime; Teramukai, Satoshi; Uehara, Ritei; Nakamura, Yosikazu; Nagai, Masaki; Mori, Masatomo

2012-07-01

Thyroid storm (TS) is life threatening. Its incidence is poorly defined, few series are available, and population-based diagnostic criteria have not been established. We surveyed TS in Japan, defined its characteristics, and formulated diagnostic criteria, FINAL-CRITERIA1 and FINAL-CRITERIA2, for two grades of TS, TS1, and TS2 respectively. We first developed diagnostic criteria based on 99 patients in the literature and 7 of our patients (LIT-CRITERIA1 for TS1 and LIT-CRITERIA2 for TS2). Thyrotoxicosis was a prerequisite for TS1 and TS2 as well as for combinations of the central nervous system manifestations, fever, tachycardia, congestive heart failure (CHF), and gastrointestinal (GI)/hepatic disturbances. We then conducted initial and follow-up surveys from 2004 through 2008, targeting all hospitals in Japan, with an eight-layered random extraction selection process to obtain and verify information on patients who met LIT-CRITERIA1 and LIT-CRITERIA2. We identified 282 patients with TS1 and 74 patients with TS2. Based on these data and information from the Ministry of Health, Labor, and Welfare of Japan, we estimated the incidence of TS in hospitalized patients in Japan to be 0.20 per 100,000 per year. Serum-free thyroxine and free triiodothyroine concentrations were similar among patients with TS in the literature, Japanese patients with TS1 or TS2, and a group of patients with thyrotoxicosis without TS (Tox-NoTS). The mortality rate was 11.0% in TS1, 9.5% in TS2, and 0% in Tox-NoTS patients. Multiple organ failure was the most common cause of death in TS1 and TS2, followed by CHF, respiratory failure, arrhythmia, disseminated intravascular coagulation, GI perforation, hypoxic brain syndrome, and sepsis. Glasgow Coma Scale results and blood urea nitrogen (BUN) were associated with irreversible damages in 22 survivors. The only change in our final diagnostic criteria for TS as compared with our initial criteria related to serum bilirubin concentration >3 mg

Augment clinical measurement using a constraint-based esophageal model

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Kou, Wenjun; Acharya, Shashank; Kahrilas, Peter; Patankar, Neelesh; Pandolfino, John

2017-11-01

Quantifying the mechanical properties of the esophageal wall is crucial to understanding impairments of trans-esophageal flow characteristic of several esophageal diseases. However, these data are unavailable owing to technological limitations of current clinical diagnostic instruments that instead display esophageal luminal cross sectional area based on intraluminal impedance change. In this work, we developed an esophageal model to predict bolus flow and the wall property based on clinical measurements. The model used the constraint-based immersed-boundary method developed previously by our group. Specifically, we first approximate the time-dependent wall geometry based on impedance planimetry data on luminal cross sectional area. We then fed these along with pressure data into the model and computed wall tension based on simulated pressure and flow fields, and the material property based on the strain-stress relationship. As examples, we applied this model to augment FLIP (Functional Luminal Imaging Probe) measurements in three clinical cases: a normal subject, achalasia, and eosinophilic esophagitis (EoE). Our findings suggest that the wall stiffness was greatest in the EoE case, followed by the achalasia case, and then the normal. This is supported by NIH Grant R01 DK56033 and R01 DK079902.

Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology

Directory of Open Access Journals (Sweden)

Vollmer Ekkehard

2008-04-01

Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image

The Role of Flow Diagnostic Techniques in Fan and Open Rotor Noise Modeling

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Envia, Edmane

2016-01-01

A principal source of turbomachinery noise is the interaction of the rotating and stationary blade rows with the perturbations in the airstream through the engine. As such, a lot of research has been devoted to the study of the turbomachinery noise generation mechanisms. This is particularly true of fan and open rotors, both of which are the major contributors to the overall noise output of modern aircraft engines. Much of the research in fan and open rotor noise has been focused on developing theoretical models for predicting their noise characteristics. These models, which run the gamut from the semi-empirical to fully computational ones, are, in one form or another, informed by the description of the unsteady flow-field in which the propulsors (i.e., the fan and open rotors) operate. Not surprisingly, the fidelity of the theoretical models is dependent, to a large extent, on capturing the nuances of the unsteady flowfield that have a direct role in the noise generation process. As such, flow diagnostic techniques have proven to be indispensible in identifying the shortcoming of theoretical models and in helping to improve them. This presentation will provide a few examples of the role of flow diagnostic techniques in assessing the fidelity and robustness of the fan and open rotor noise prediction models.

EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

Energy Technology Data Exchange (ETDEWEB)

Ha, Jun Su; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

2005-07-01

In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4.

EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

International Nuclear Information System (INIS)

Ha, Jun Su; Seong, Poong Hyun

2005-01-01

In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4

ICF implosion hotspot ion temperature diagnostic techniques based on neutron time-of-flight method

International Nuclear Information System (INIS)

Tang Qi; Song Zifeng; Chen Jiabin; Zhan Xiayu

2013-01-01

Ion temperature of implosion hotspot is a very important parameter for inertial confinement fusion. It reflects the energy level of the hotspot, and it is very sensitive to implosion symmetry and implosion speed. ICF implosion hotspot ion temperature diagnostic techniques based on neutron time-of-flight method were described. A neutron TOF spectrometer was developed using a ultrafast plastic scintillator as the neutron detector. Time response of the spectrometer has 1.1 ns FWHM and 0.5 ns rising time. TOF spectrum resolving method based on deconvolution and low pass filter was illuminated. Implosion hotspot ion temperature in low neutron yield and low ion temperature condition at Shenguang-Ⅲ facility was acquired using the diagnostic techniques. (authors)

Diagnostic reasoning using qualitative causal models

International Nuclear Information System (INIS)

Sudduth, A.L.

1992-01-01

The application of expert systems to reasoning problems involving real-time data from plant measurements has been a topic of much research, but few practical systems have been deployed. One obstacle to wider use of expert systems in applications involving real-time data is the lack of adequate knowledge representation methodologies for dynamic processes. Knowledge bases composed mainly of rules have disadvantages when applied to dynamic processes and real-time data. This paper describes a methodology for the development of qualitative causal models that can be used as knowledge bases for reasoning about process dynamic behavior. These models provide a systematic method for knowledge base construction, considerably reducing the engineering effort required. They also offer much better opportunities for verification and validation of the knowledge base, thus increasing the possibility of the application of expert systems to reasoning about mission critical systems. Starting with the Signed Directed Graph (SDG) method that has been successfully applied to describe the behavior of diverse dynamic processes, the paper shows how certain non-physical behaviors that result from abstraction may be eliminated by applying causal constraint to the models. The resulting Extended Signed Directed Graph (ESDG) may then be compiled to produce a model for use in process fault diagnosis. This model based reasoning methodology is used in the MOBIAS system being developed by Duke Power Company under EPRI sponsorship. 15 refs., 4 figs

Get the Diagnosis: an evidence-based medicine collaborative Wiki for diagnostic test accuracy.

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Hammer, Mark M; Kohlberg, Gavriel D

2017-04-01

Despite widespread calls for its use, there are challenges to the implementation of evidence-based medicine (EBM) in clinical practice. In response to the challenges of finding timely, pertinent information on diagnostic test accuracy, we developed an online, crowd-sourced Wiki on diagnostic test accuracy called Get the Diagnosis (GTD, http://www.getthediagnosis.org). Since its launch in November 2008 till October 2015, GTD has accumulated information on 300 diagnoses, with 1617 total diagnostic entries. There are a total of 1097 unique diagnostic tests with a mean of 5.4 tests (range 0-38) per diagnosis. 73% of entries (1182 of 1617) have an associated sensitivity and specificity and 89% of entries (1432 of 1617) have associated peer-reviewed literature citations. Altogether, GTD contains 474 unique literature citations. For a sample of three diagnoses, the search precision (percentage of relevant results in the first 30 entries) in GTD was 100% as compared with a range of 13.3%-63.3% for PubMed and between 6.7% and 76.7% for Google Scholar. GTD offers a fast, precise and efficient way to look up diagnostic test accuracy. On three selected examples, GTD had a greater precision rate compared with PubMed and Google Scholar in identifying diagnostic test information. GTD is a free resource that complements other currently available resources. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

A diagnostic model incorporating P50 sensory gating and neuropsychological tests for schizophrenia.

Directory of Open Access Journals (Sweden)

Jia-Chi Shan

Full Text Available OBJECTIVES: Endophenotypes in schizophrenia research is a contemporary approach to studying this heterogeneous mental illness, and several candidate neurophysiological markers (e.g. P50 sensory gating and neuropsychological tests (e.g. Continuous Performance Test (CPT and Wisconsin Card Sorting Test (WCST have been proposed. However, the clinical utility of a single marker appears to be limited. In the present study, we aimed to construct a diagnostic model incorporating P50 sensory gating with other neuropsychological tests in order to improve the clinical utility. METHODS: We recruited clinically stable outpatients meeting DSM-IV criteria of schizophrenia and age- and gender-matched healthy controls. Participants underwent P50 sensory gating experimental sessions and batteries of neuropsychological tests, including CPT, WCST and Wechsler Adult Intelligence Scale Third Edition (WAIS-III. RESULTS: A total of 106 schizophrenia patients and 74 healthy controls were enrolled. Compared with healthy controls, the patient group had significantly a larger S2 amplitude, and thus poorer P50 gating ratio (gating ratio = S2/S1. In addition, schizophrenia patients had a poorer performance on neuropsychological tests. We then developed a diagnostic model by using multivariable logistic regression analysis to differentiate patients from healthy controls. The final model included the following covariates: abnormal P50 gating (defined as P50 gating ratio >0.4, three subscales derived from the WAIS-III (Arithmetic, Block Design, and Performance IQ, sensitivity index from CPT and smoking status. This model had an adequate accuracy (concordant percentage = 90.4%; c-statistic = 0.904; Hosmer-Lemeshow Goodness-of-Fit Test, p = 0.64>0.05. CONCLUSION: To the best of our knowledge, this is the largest study to date using P50 sensory gating in subjects of Chinese ethnicity and the first to use P50 sensory gating along with other neuropsychological tests

Flexible non-linear predictive models for large-scale wind turbine diagnostics

DEFF Research Database (Denmark)

Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole

2017-01-01

We demonstrate how flexible non-linear models can provide accurate and robust predictions on turbine component temperature sensor data using data-driven principles and only a minimum of system modeling. The merits of different model architectures are evaluated using data from a large set...... of turbines operating under diverse conditions. We then go on to test the predictive models in a diagnostic setting, where the output of the models are used to detect mechanical faults in rotor bearings. Using retrospective data from 22 actual rotor bearing failures, the fault detection performance...... of the models are quantified using a structured framework that provides the metrics required for evaluating the performance in a fleet wide monitoring setup. It is demonstrated that faults are identified with high accuracy up to 45 days before a warning from the hard-threshold warning system....

Present status on atomic and molecular data relevant to fusion plasma diagnostics and modeling

International Nuclear Information System (INIS)

Tawara, H.

1997-01-01

This issue is the collection of the paper presented status on atomic and molecular data relevant to fusion plasma diagnostics and modeling. The 10 of the presented papers are indexed individually. (J.P.N.)

Model-Based Diagnosis and Prognosis of a Water Recycling System

Science.gov (United States)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Development and testing of a diagnostic system for intelligen distributed control at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Ruhl, D.W.; Klevans, E.H.; Robinson, G.E.

1990-01-01

A diagnostic system is under development for demonstration of Intelligent Distributed Control at the Experimental Breeder Reactor (EBR--II). In the first phase of the project a diagnostic system is being developed for the EBR-II steam plant based on the DISYS expert systems approach. Current testing uses recorded plant data and data from simulated plant faults. The dynamical simulation of the EBR-II steam plant uses the Babcock and Wilcox (B ampersand W) Modular Modeling System (MMS). At EBR-II the diagnostic system operates in the UNIX workstation and receives live plant data from the plant Data Acquisition System (DAS). Future work will seek implementation of the steam plant diagnostic in a distributed manner using UNIX based computers and Bailey microprocessor-based control system. 10 refs., 6 figs

Utility of low-order linear nuclear-power-plant models in plant diagnostics and control

International Nuclear Information System (INIS)

Tylee, J.L.

1981-01-01

A low-order, linear model of a pressurized water reactor (PWR) plant is described and evaluated. The model consists of 23 linear, first-order difference equations and simulates all subsystems of both the primary and secondary sides of the plant. Comparisons between the calculated model response and available test data show the model to be an adequate representation of the actual plant dynamics. Suggested use for the model in an on-line digital plant diagnostics and control system are presented

Diagnosing holographic type dark energy models with the Statefinder hierarchy, composite null diagnostic and w- w' pair

Science.gov (United States)

Zhao, Ze; Wang, Shuang

2018-03-01

The main purpose of this work is to distinguish various holographic type dark energy (DE) models, including the ΛHDE, HDE, NADE, and RDE model, by using various diagnostic tools. The first diagnostic tool is the Statefinder hierarchy, in which the evolution of Statefinder hierarchy parmeter S (1) 3( z) and S (1) 4( z) are studied. The second is composite null diagnostic (CND), in which the trajectories of { S (1) 3, ɛ} and { S (1) 4, ɛ} are investigated, where ɛ is the fractional growth parameter. The last is w-w' analysis, where w is the equation of state for DE and the prime denotes derivative with respect to ln a. In the analysis we consider two cases: varying current fractional DE density Ω de0 and varying DE model parameter C. We find that: (1) both the Statefinder hierarchy and the CND have qualitative impact on ΛHDE, but only have quantitative impact on HDE. (2) S (1) 4 can lead to larger differences than S (1) 3, while the CND pair has a stronger ability to distinguish different models than the Statefinder hierarchy. (3) For the case of varying C, the { w,w'} pair has qualitative impact on ΛHDE; for the case of varying Ω de0, the { w, w'} pair only has quantitative impact; these results are different from the cases of HDE, RDE, and NADE, in which the {w,w'} pair only has quantitative impact on these models. In conclusion, compared with HDE, RDE, and NADE, the ΛHDE model can be easily distinguished by using these diagnostic tools.

Non-invasive diagnostics of the maxillary and frontal sinuses based on diode laser gas spectroscopy.

Science.gov (United States)

Lewander, Märta; Lindberg, Sven; Svensson, Tomas; Siemund, Roger; Svanberg, Katarina; Svanberg, Sune

2012-03-01

Suspected, but objectively absent, rhinosinusitis constitutes a major cause of visits to the doctor, high health care costs, and the over-prescription of antibiotics, contributing to the serious problem of resistant bacteria. This situation is largely due to a lack of reliable and widely applicable diagnostic methods. A novel method for the diagnosis of rhinosinusitis based on non-intrusive diode laser gas spectroscopy is presented. The technique is based on light absorption by free gas (oxygen and water vapour) inside the sinuses, and has the potential to be a complementary diagnostic tool in primary health care. The method was evaluated on 40 patients with suspected sinus problems, referred to the diagnostic radiology clinic for low-dose computed tomography (CT), which was used as the reference technique. The data obtained with the new laser-based method correlated well with the grading of opacification and ventilation using CT. The sensitivity and specificity were estimated to be 93% and 61%, respectively, for the maxillary sinuses, and 94% and 86%, respectively, for the frontal sinuses. Good reproducibility was shown. The laser-based technique presents real-time clinical data that correlate well to CT findings, while being non-intrusive and avoiding the use of ionizing radiation.

Introducing the ESAT-6 free IGRA, a companion diagnostic for TB vaccines based on ESAT-6

DEFF Research Database (Denmark)

Ruhwald, Morten; de Thurah, Lena; Kuchaka, Davis

2017-01-01

tests unspecific after vaccination. This challenge has prompted the development of a companion diagnostic for ESAT-6 based vaccines, an ESAT-6 free IGRA. We screened a panel of seven potential new diagnostic antigens not recognized in BCG vaccinated individuals. Three highly recognized antigens Esp......C, EspF and Rv2348c were identified and combined with CFP10 in an ESAT-6 free antigen cocktail. The cocktail was prepared in a field-friendly format, lyophilized with heparin in ready-to-use vacutainer tubes. The diagnostic performance of the ESAT-6 free IGRA was determined in a cross-validation study....... Compared IGRA, the ESAT-6 free IGRA induced a comparable magnitude of IFN-γ release, and the diagnostic performance was on par with Quantiferon (sensitivity 84% vs 79%; specificity 99% vs 97%). The comparable performance of the ESAT-6 free IGRA to IGRA suggests potential as companion diagnostic for ESAT-6...

Grain Size and Parameter Recovery with TIMSS and the General Diagnostic Model

Science.gov (United States)

Skaggs, Gary; Wilkins, Jesse L. M.; Hein, Serge F.

2016-01-01

The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying…

Fast infectious diseases diagnostics based on microfluidic biochip system

Directory of Open Access Journals (Sweden)

Qin Huang

2017-03-01

Full Text Available Molecular diagnostics is one of the most important tools currently in use for clinical pathogen detection due to its high sensitivity, specificity, and low consume of sample and reagent is keyword to low cost molecular diagnostics. In this paper, a sensitive DNA isothermal amplification method for fast clinical infectious diseases diagnostics at aM concentrations of DNA was developed using a polycarbonate (PC microfluidic chip. A portable confocal optical fluorescence detector was specifically developed for the microfluidic chip that was capable of highly sensitive real-time detection of amplified products for sequence-specific molecular identification near the optical diffraction limit with low background. The molecular diagnostics of Listeria monocytogenes with nucleic acid extracted from stool samples was performed at a minimum DNA template concentration of 3.65aM, and a detection limit of less than five copies of genomic DNA. Contrast to the general polymerase chain reaction (PCR at eppendorf (EP tube, the detection time in our developed method was reduced from 1.5h to 45min for multi-target parallel detection, the consume of sample and reagent was dropped from 25μL to 1.45μL. This novel microfluidic chip system and method can be used to develop a micro total analysis system as a clinically relevant pathogen molecular diagnostics method via the amplification of targets, with potential applications in biotechnology, medicine, and clinical molecular diagnostics.

Severe childhood asthma and allergy to furry animals: refined assessment using molecular-based allergy diagnostics.

Science.gov (United States)

Konradsen, Jon R; Nordlund, Björn; Onell, Annica; Borres, Magnus P; Grönlund, Hans; Hedlin, Gunilla

2014-03-01

Allergy to cats and dogs and polysensitization towards these animals are associated with severe childhood asthma. Molecular-based allergy diagnostics offers new opportunities for improved characterization and has been suggested to be particularly useful in patients with polysensitization and/or severe asthma. The aim was to use extract- and molecular-based allergy diagnostics to compare patterns of IgE sensitization towards aeroallergens in children with problematic severe and controlled asthma. Children with a positive ImmunoCAP towards any furry animal (cat, dog or horse) were recruited from a Nationwide Swedish study on severe childhood asthma. Severe (n = 37, age 13 years) and controlled (n = 28, age 14 years) asthmatics underwent assessment of allergic sensitization by ImmunoCap (kUA /l) and immunosolid-phase allergen chip (ISAC). In addition, Asthma Control Test, spirometry and a methacholine challenge were performed. Children with severe asthma had lower asthma control (p Molecular-based allergy diagnostics revealed a more complex molecular spreading of allergen components in children with the most severe disease. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A model of parametric X-ray radiation for application to diagnostic radiology

International Nuclear Information System (INIS)

Di Domenico, G.; Cardarelli, P.; Gambaccini, M.; Marziani, M.; Taibi, A.; Comandini, A.

2011-01-01

Parametric X-ray Radiation (PXR) is well known as an intense, tunable and quasi-monochromatic X-ray source. From the very first work of Ter-Mikaelian, who proposed the interaction phenomenon for Parametric X-rays many theoretical and experimental studies have investigated the characteristics of such a novel X-ray source. Within the framework of classical electrodynamics, we have thoroughly studied the physical implications of electrons moving through a medium at relativistic speed and then developed an analytical model of X-ray diffraction based on the PXR phenomenon. The model has been used to obtain information on the characteristics of PXR diffracted beam in terms of X-ray intensity, energy spectrum and angular distribution. Several crystals have been studied both in Bragg and Laue geometry and their relative yield has been compared. Preliminary results on the diagnostic potential of PXR have shown that, at a distance from the crystal which produces a size of the X-ray field useful for an imaging application, the photon yield of PXR is higher than that produced by a conventional X-ray tube, provided that a similar electron current is available.

Optimal Combinations of Diagnostic Tests Based on AUC.

Science.gov (United States)

Huang, Xin; Qin, Gengsheng; Fang, Yixin

2011-06-01

When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples. © 2010, The International Biometric Society.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective.

Science.gov (United States)

Findeisen, Peter; Neumaier, Michael

2009-01-01

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

Novel Infiltration Diagnostics based on Laser-line Scanning and Infrared Temperature Field Imaging

Energy Technology Data Exchange (ETDEWEB)

Wang, Xinwei [Iowa State Univ., Ames, IA (United States)

2017-12-08

This project targets the building energy efficiency problems induced by building infiltration/leaks. The current infiltration inspection techniques often require extensive visual inspection and/or whole building pressure test. These current techniques cannot meet more than three of the below five criteria of ideal infiltration diagnostics: 1. location and extent diagnostics, 2. building-level application, 3. least surface preparation, 4. weather-proof, and 5. non-disruption to building occupants. These techniques are either too expensive or time consuming, and often lack accuracy and repeatability. They are hardly applicable to facades/facades section. The goal of the project was to develop a novel infiltration diagnostics technology based on laser line-scanning and simultaneous infrared temperature imaging. A laboratory scale experimental setup was designed to mimic a model house of well-defined pressure difference below or above the outside pressure. Algorithms and Matlab-based programs had been developed for recognition of the hole location in infrared images. Our experiment based on laser wavelengths of 450 and 1550 nm and laser beam diameters of 4-25 mm showed that the location of the holes could be identified using laser heating; the diagnostic approach however could not readily distinguish between infiltration and non-infiltration points. To significantly improve the scanning throughput and recognition accuracy, a second approach was explored, developed, and extensively tested. It incorporates a liquid spray on the surface to induce extra phase change cooling effect. In this spray method, we termed it as PECIT (Phase-change Enhanced Cooling Infrared Thermography), phase-change enhanced cooling was used, which significantly amplifies the effect of air flow (infiltration and exfiltration). This heat transfer method worked extremely well to identify infiltration and exfiltration locations with high accuracy and increased throughput. The PECIT technique was

Computation of large scale currents in the Arabian Sea during winter using a semi-diagnostic model

Digital Repository Service at National Institute of Oceanography (India)

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

A 3-dimensional, semi-diagnostic model with 331 levels in the vertical has been used for the computation of climatic circulation in the western tropical Indian Ocean. Model is driven with the seasonal mean data on wind stress, temperature...

An operator model-based filtering scheme

International Nuclear Information System (INIS)

Sawhney, R.S.; Dodds, H.L.; Schryer, J.C.

1990-01-01

This paper presents a diagnostic model developed at Oak Ridge National Laboratory (ORNL) for off-normal nuclear power plant events. The diagnostic model is intended to serve as an embedded module of a cognitive model of the human operator, one application of which could be to assist control room operators in correctly responding to off-normal events by providing a rapid and accurate assessment of alarm patterns and parameter trends. The sequential filter model is comprised of two distinct subsystems --- an alarm analysis followed by an analysis of interpreted plant signals. During the alarm analysis phase, the alarm pattern is evaluated to generate hypotheses of possible initiating events in order of likelihood of occurrence. Each hypothesis is further evaluated through analysis of the current trends of state variables in order to validate/reject (in the form of increased/decreased certainty factor) the given hypothesis. 7 refs., 4 figs

Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.

Science.gov (United States)

Mi, Gu; Di, Yanming; Schafer, Daniel W

2015-01-01

This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.

Diagnostic utility of novel MRI-based biomarkers for Alzheimer's disease: diffusion tensor imaging and deformation-based morphometry.

Science.gov (United States)

Friese, Uwe; Meindl, Thomas; Herpertz, Sabine C; Reiser, Maximilian F; Hampel, Harald; Teipel, Stefan J

2010-01-01

We report evidence that multivariate analyses of deformation-based morphometry and diffusion tensor imaging (DTI) data can be used to discriminate between healthy participants and patients with Alzheimer's disease (AD) with comparable diagnostic accuracy. In contrast to other studies on MRI-based biomarkers which usually only focus on a single modality, we derived deformation maps from high-dimensional normalization of T1-weighted images, as well as mean diffusivity maps and fractional anisotropy maps from DTI of the same group of 21 patients with AD and 20 healthy controls. Using an automated multivariate analysis of the entire brain volume, widespread decreased white matter integrity and atrophy effects were found in cortical and subcortical regions of AD patients. Mean diffusivity maps and deformation maps were equally effective in discriminating between AD patients and controls (AUC =0.88 vs. AUC=0.85) while fractional anisotropy maps performed slightly inferior. Combining the maps from different modalities in a logistic regression model resulted in a classification accuracy of AUC=0.86 after leave-one-out cross-validation. It remains to be shown if this automated multivariate analysis of DTI-measures can improve early diagnosis of AD in predementia stages.

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Published diagnostic models safely excluded colorectal cancer in an independent primary care validation study

NARCIS (Netherlands)

Elias, Sjoerd G; Kok, Liselotte; Witteman, Ben J M; Goedhard, Jelle G; Romberg-Camps, Mariëlle J L; Muris, Jean W M; de Wit, Niek J; Moons, Karel G M

OBJECTIVE: To validate published diagnostic models for their ability to safely reduce unnecessary endoscopy referrals in primary care patients suspected of significant colorectal disease. STUDY DESIGN AND SETTING: Following a systematic literature search, we independently validated the identified

Diagnostic model of 3-D circulation in the Arabian Sea and western equatorial Indian Ocean: Results of monthly mean sea surface topography

Digital Repository Service at National Institute of Oceanography (India)

Bahulayan, N.; Shaji, C.

A three-dimensional diagnostic model has been developed to compute the monthly mean circulation and sea surface topography in the Western Tropical Indian Ocean north of 20 degrees S and west of 80 degrees E. The diagnostic model equations...

On-line diagnostic techniques for air-operated control valves based on time series analysis

International Nuclear Information System (INIS)

Ito, Kenji; Matsuoka, Yoshinori; Minamikawa, Shigeru; Komatsu, Yasuki; Satoh, Takeshi.

1996-01-01

The objective of this research is to study the feasibility of applying on-line diagnostic techniques based on time series analysis to air-operated control valves - numerous valves of the type which are used in PWR plants. Generally the techniques can detect anomalies by failures in the initial stages for which detection is difficult by conventional surveillance of process parameters measured directly. However, the effectiveness of these techniques depends on the system being diagnosed. The difficulties in applying diagnostic techniques to air-operated control valves seem to come from the reduced sensitivity of their response as compared with hydraulic control systems, as well as the need to identify anomalies in low level signals that fluctuate only slightly but continuously. In this research, simulation tests were performed by setting various kinds of failure modes for a test valve with the same specifications as of a valve actually used in the plants. Actual control signals recorded from an operating plant were then used as input signals for simulation. The results of the tests confirmed the feasibility of applying on-line diagnostic techniques based on time series analysis to air-operated control valves. (author)

USXR Based MHD, Transport, Equilibria and Current Profile Diagnostics for NSTX. Final Report

International Nuclear Information System (INIS)

Finkenthal, Michael

2009-01-01

The present report resumes the research activities of the Plasma Spectroscopy/Diagnostics Group at Johns Hopkins University performed on the NSTX tokamak at PPPL during the period 1999-2009. During this period we have designed and implemented XUV based diagnostics for a large number of tasks: study of impurity content and particle transport, MHD activity, time-resolved electron temperature measeurements, ELM research, etc. Both line emission and continuum were used in the XUV range. New technics and novel methods have been devised within the framework of the present research. Graduate and post-graduate students have been involved at all times in addition to the senior research personnel. Several tens of papers have been published and lectures have been given based on the obtained results at conferences and various research institutions (lists of these activities were attached both in each proposal and in the annual reports submitted to our supervisors at OFES)

Development of Demonstrably Predictive Models for Emissions from Alternative Fuels Based Aircraft Engines

Science.gov (United States)

2017-05-01

Engineering Chemistry Fundamentals, Vol. 5, No. 3, 1966, pp. 356–363. [14] Burns, R. A., Development of scalar and velocity imaging diagnostics...in an Aero- Engine Model Combustor at Elevated Pressure Using URANS and Finite- Rate Chemistry ,” 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference...FINAL REPORT Development of Demonstrably Predictive Models for Emissions from Alternative Fuels Based Aircraft Engines SERDP Project WP-2151

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

Energy Technology Data Exchange (ETDEWEB)

Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.

The effect of nonlinear propagation on heating of tissue: A numerical model of diagnostic ultrasound beams

Science.gov (United States)

Cahill, Mark D.; Humphrey, Victor F.; Doody, Claire

2000-07-01

Thermal safety indices for diagnostic ultrasound beams are calculated under the assumption that the sound propagates under linear conditions. A non-axisymmetric finite difference model is used to solve the KZK equation, and so to model the beam of a diagnostic scanner in pulsed Doppler mode. Beams from both a uniform focused rectangular source and a linear array are considered. Calculations are performed in water, and in attenuating media with tissue-like characteristics. Attenuating media are found to exhibit significant nonlinear effects for finite-amplitude beams. The resulting loss of intensity by the beam is then used as the source term in a model of tissue heating to estimate the maximum temperature rises. These are compared with the thermal indices, derived from the properties of the water-propagated beams.

Microfluidic technology for molecular diagnostics.

Science.gov (United States)

Robinson, Tom; Dittrich, Petra S

2013-01-01

Molecular diagnostics have helped to improve the lives of millions of patients worldwide by allowing clinicians to diagnose patients earlier as well as providing better ongoing therapies. Point-of-care (POC) testing can bring these laboratory-based techniques to the patient in a home setting or to remote settings in the developing world. However, despite substantial progress in the field, there still remain many challenges. Progress in molecular diagnostics has benefitted greatly from microfluidic technology. This chapter aims to summarise the more recent advances in microfluidic-based molecular diagnostics. Sections include an introduction to microfluidic technology, the challenges of molecular diagnostics, how microfluidic advances are working to solve these issues, some alternative design approaches, and detection within these systems.

Contribution of 18F-FDG PET in the diagnostic assessment of fever of unknown origin (FUO): a stratification-based meta-analysis

International Nuclear Information System (INIS)

Besson, Florent L.; Chaumet-Riffaud, Philippe; Prigent, Alain; Durand, Emmanuel; Playe, Margot; Noel, Nicolas; Lambotte, Olivier; Goujard, Cecile

2016-01-01

The aim of this study was to quantify the contribution of FDG PET to the diagnostic assessment of fever of unknown origin (FUO), taking into account the diagnostic limitations resulting from the composite nature of this entity. The PubMed/MEDLINE database was searched from 2000 to September 2015. Original articles fulfilling the following criteria were included: (1) FUO as the initial diagnosis, (2) no immunosuppressed or nosocomial condition, (3) final diagnosis not based on PET, (4) a follow-up period specified, (5) adult population, and (6) availability of adapted data for calculation of odds ratios (ORs). ORs were computed for each study and then pooled using a random effects model. Stratification-based sensitivity analyses were finally performed using the following prespecified criteria: (a) study design, (b) PET device, (c) geographic area, and (d) follow-up period. A meta-analysis of the 14 included studies showed that normal PET findings led to an increase in the absolute final diagnostic rate of 36 % abnormal PET findings to an increase of 83 %, corresponding to a pooled OR of 8.94 (95 % CI 4.18 - 19.12, Z = 5.65; p < 0.00001). The design of the studies influenced the results (OR 2.92, 95 % CI 1.00 - 8.53 for prospective studies; OR 18,57, 95 % CI 7.57 - 45.59 for retrospective studies; p = 0.01), whereas devices (dedicated or hybrid), geographic area and follow-up period did not. Abnormal PET findings are associated with a substantially increased final diagnostic rate in FUO. Consequently, FDG PET could be considered for inclusion in the first-line diagnostic work-up of FUO. Further randomized prospective studies with standardized FDG PET procedures are warranted to confirm this first-line position. (orig.)

Two-dimensional AXUV-based radiated power density diagnostics on NSTX-U.

Science.gov (United States)

Faust, I; Delgado-Aparicio, L; Bell, R E; Tritz, K; Diallo, A; Gerhardt, S P; LeBlanc, B; Kozub, T A; Parker, R R; Stratton, B C

2014-11-01

A new set of radiated-power-density diagnostics for the National Spherical Torus Experiment Upgrade (NSTX-U) tokamak have been designed to measure the two-dimensional poloidal structure of the total photon emissivity profile in order to perform power balance, impurity transport, and magnetohydrodynamic studies. Multiple AXUV-diode based pinhole cameras will be installed in the same toroidal angle at various poloidal locations. The local emissivity will be obtained from several types of tomographic reconstructions. The layout and response expected for the new radially viewing poloidal arrays will be shown for different impurity concentrations to characterize the diagnostic sensitivity. The radiated power profile inverted from the array data will also be used for estimates of power losses during transitions from various divertor configurations in NSTX-U. The effect of in-out and top/bottom asymmetries in the core radiation from high-Z impurities will be addressed.

Two-dimensional AXUV-based radiated power density diagnostics on NSTX-Ua)

Science.gov (United States)

Faust, I.; Delgado-Aparicio, L.; Bell, R. E.; Tritz, K.; Diallo, A.; Gerhardt, S. P.; LeBlanc, B.; Kozub, T. A.; Parker, R. R.; Stratton, B. C.

2014-11-01

A new set of radiated-power-density diagnostics for the National Spherical Torus Experiment Upgrade (NSTX-U) tokamak have been designed to measure the two-dimensional poloidal structure of the total photon emissivity profile in order to perform power balance, impurity transport, and magnetohydrodynamic studies. Multiple AXUV-diode based pinhole cameras will be installed in the same toroidal angle at various poloidal locations. The local emissivity will be obtained from several types of tomographic reconstructions. The layout and response expected for the new radially viewing poloidal arrays will be shown for different impurity concentrations to characterize the diagnostic sensitivity. The radiated power profile inverted from the array data will also be used for estimates of power losses during transitions from various divertor configurations in NSTX-U. The effect of in-out and top/bottom asymmetries in the core radiation from high-Z impurities will be addressed.

Two-dimensional AXUV-based radiated power density diagnostics on NSTX-U

Energy Technology Data Exchange (ETDEWEB)

Faust, I.; Parker, R. R. [MIT - Plasma Science and Fusion Center, Cambridge, Massachusetts 02139 (United States); Delgado-Aparicio, L.; Bell, R. E.; Diallo, A.; Gerhardt, S. P.; LeBlanc, B.; Kozub, T. A. [Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540 (United States); Tritz, K. [The Johns Hopkins University, Baltimore, Maryland 21209 (United States); Stratton, B. C. [MIT - Plasma Science and Fusion Center, Cambridge, Massachusetts 02139 (United States); Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540 (United States)

2014-11-15

A new set of radiated-power-density diagnostics for the National Spherical Torus Experiment Upgrade (NSTX-U) tokamak have been designed to measure the two-dimensional poloidal structure of the total photon emissivity profile in order to perform power balance, impurity transport, and magnetohydrodynamic studies. Multiple AXUV-diode based pinhole cameras will be installed in the same toroidal angle at various poloidal locations. The local emissivity will be obtained from several types of tomographic reconstructions. The layout and response expected for the new radially viewing poloidal arrays will be shown for different impurity concentrations to characterize the diagnostic sensitivity. The radiated power profile inverted from the array data will also be used for estimates of power losses during transitions from various divertor configurations in NSTX-U. The effect of in-out and top/bottom asymmetries in the core radiation from high-Z impurities will be addressed.

Two-dimensional AXUV-based radiated power density diagnostics on NSTX-U

International Nuclear Information System (INIS)

Faust, I.; Parker, R. R.; Delgado-Aparicio, L.; Bell, R. E.; Diallo, A.; Gerhardt, S. P.; LeBlanc, B.; Kozub, T. A.; Tritz, K.; Stratton, B. C.

2014-01-01

A new set of radiated-power-density diagnostics for the National Spherical Torus Experiment Upgrade (NSTX-U) tokamak have been designed to measure the two-dimensional poloidal structure of the total photon emissivity profile in order to perform power balance, impurity transport, and magnetohydrodynamic studies. Multiple AXUV-diode based pinhole cameras will be installed in the same toroidal angle at various poloidal locations. The local emissivity will be obtained from several types of tomographic reconstructions. The layout and response expected for the new radially viewing poloidal arrays will be shown for different impurity concentrations to characterize the diagnostic sensitivity. The radiated power profile inverted from the array data will also be used for estimates of power losses during transitions from various divertor configurations in NSTX-U. The effect of in-out and top/bottom asymmetries in the core radiation from high-Z impurities will be addressed

Cylinder pressure sensing and model-based control in engine management systems

Energy Technology Data Exchange (ETDEWEB)

Truscott, A.; Noble, A.; Akoachere, A.; Beaumont, A. [Ricardo Consulting Engineers Ltd., Bridge Works (United Kingdom); Mueller, R.; Hart, M. [FT2/EA, HPC T721, DaimlerChrysler AG, Stuttgart (Germany); Kroetz, G. [FT2/M, DaimlerChrysler AG, Muenchen (Germany); Cavalloni, C.; Gnielka, M. [Kistler Instrumente AG, Winterthur (Switzerland)

2000-07-01

Global demands on fuel economy and lower emissions from automotive vehicles have had a large impact on the development of engine management systems (EMS) in recent years. However, despite the advances in system hardware, the software programmed into these systems has yet to utilise the full potential of modern control methodologies. Model based control and diagnostics is the next step forward in the development of EMS software with the potential of providing improvements in cost, efficiency, emissions and comfort. However, the full utilisation of such techniques requires very close monitoring of engine conditions. This is made possible with the advent of new inexpensive sensor technology that can withstand the harsh environment of the combustion chamber. To exploit the above advances, the AENEAS collaborative project is being carried out by Ricardo, DaimlerChrysler and Kistler, with financial support from the European Commission and Swiss government, and has the objective of realising the benefits of cylinder pressure based engine management system (CPEMS) technology. This paper describes the application of CPEMS technology to a spark ignition (SI) engine. It describes how the combination of model based algorithms, incorporating physical principles, and cylinder pressure sensing can provide an effective means of engine control and diagnostics. (orig.)

Computer modeling and design of diagnostic workstations and radiology reading rooms

Science.gov (United States)

Ratib, Osman M.; Amato, Carlos L.; Balbona, Joseph A.; Boots, Kevin; Valentino, Daniel J.

2000-05-01

We used 3D modeling techniques to design and evaluate the ergonomics of diagnostic workstation and radiology reading room in the planning phase of building a new hospital at UCLA. Given serious space limitations, the challenge was to provide more optimal working environment for radiologists in a crowded and busy environment. A particular attention was given to flexibility, lighting condition and noise reduction in rooms shared by multiple users performing diagnostic tasks as well as regular clinical conferences. Re-engineering workspace ergonomics rely on the integration of new technologies, custom designed cabinets, indirect lighting, sound-absorbent partitioning and geometric arrangement of workstations to allow better privacy while optimizing space occupation. Innovations included adjustable flat monitors, integration of videoconferencing and voice recognition, control monitor and retractable keyboard for optimal space utilization. An overhead compartment protecting the monitors from ambient light is also used as accessory lightbox and rear-view projection screen for conferences.

Refining multi-model projections of temperature extremes by evaluation against land-atmosphere coupling diagnostics

Science.gov (United States)

Sippel, Sebastian; Zscheischler, Jakob; Mahecha, Miguel D.; Orth, Rene; Reichstein, Markus; Vogel, Martha; Seneviratne, Sonia I.

2017-05-01

and present-day climate extremes are affected to a lesser extent by the applied constraint, i.e. projected changes are reduced locally by around 0.5 to 1 °C - but this remains a local effect in regions that are highly sensitive to land-atmosphere coupling. In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures.

Some analytic diagnostic models for transport processes in estuarine and coastal waters

International Nuclear Information System (INIS)

Suarez Antola, R.

2001-03-01

Advection and dispersion processes in estuarine and coastal waters are briefly reviewed. Beginning from the basic macroscopic equations of transport for a substance diluted or suspended in the considered body of water,several levels of filtering in time and space are described and applied to obtain suitable diagnostic mathematical models both with scale effects and gaussian.The solutions of the aforementioned models,for initial distributions and boundary conditions with enough symmetry,are discussed, as well as their applications to a parameter characterization of the transport properties of the receiving body of water

Operator support and diagnostic reasoning in an industrial process

Energy Technology Data Exchange (ETDEWEB)

Aaker, O.

1996-12-31

Efficient use of energy in production plants requires that the various processes are well controlled. The main focus of this doctoral thesis is on detection of errors and malfunctions using analytical redundancy and on state estimation using an open loop nonlinear model. A ``residual`` is present if a system does not behave as expected, or if a certain rule is violated. ``Reasoning`` is the action of finding process malfunctions based on observed residuals. The thesis applies a new formalism for comparing diagnostic reasoning methods both in terms of what knowledge is used and how it is used, and suggests a formal model of what is known about the process. The formalism is used to illustrate the difference between diagnostic reasoning based on physically interconnected process units and streams, and reasoning about goals and functions for finding a diagnosis. As an example of application, results and experiences from a test implementation using an open loop model for operator support in a complex fertilizer factory are reported. 108 refs., 61 figs., 37 tabs.

Toward an in-situ analytics and diagnostics framework for earth system models

Science.gov (United States)

Anantharaj, Valentine; Wolf, Matthew; Rasch, Philip; Klasky, Scott; Williams, Dean; Jacob, Rob; Ma, Po-Lun; Kuo, Kwo-Sen

2017-04-01

The development roadmaps for many earth system models (ESM) aim for a globally cloud-resolving model targeting the pre-exascale and exascale systems of the future. The ESMs will also incorporate more complex physics, chemistry and biology - thereby vastly increasing the fidelity of the information content simulated by the model. We will then be faced with an unprecedented volume of simulation output that would need to be processed and analyzed concurrently in order to derive the valuable scientific results. We are already at this threshold with our current generation of ESMs at higher resolution simulations. Currently, the nominal I/O throughput in the Community Earth System Model (CESM) via Parallel IO (PIO) library is around 100 MB/s. If we look at the high frequency I/O requirements, it would require an additional 1 GB / simulated hour, translating to roughly 4 mins wallclock / simulated-day => 24.33 wallclock hours / simulated-model-year => 1,752,000 core-hours of charge per simulated-model-year on the Titan supercomputer at the Oak Ridge Leadership Computing Facility. There is also a pending need for 3X more volume of simulation output . Meanwhile, many ESMs use instrument simulators to run forward models to compare model simulations against satellite and ground-based instruments, such as radars and radiometers. The CFMIP Observation Simulator Package (COSP) is used in CESM as well as the Accelerated Climate Model for Energy (ACME), one of the ESMs specifically targeting current and emerging leadership-class computing platforms These simulators can be computationally expensive, accounting for as much as 30% of the computational cost. Hence the data are often written to output files that are then used for offline calculations. Again, the I/O bottleneck becomes a limitation. Detection and attribution studies also use large volume of data for pattern recognition and feature extraction to analyze weather and climate phenomenon such as tropical cyclones

Magnetic diagnostic plasma position in the TCA/BR tokamak

International Nuclear Information System (INIS)

Galvao, R.M.O.; Kuznetsov, Yu.K.; Nascimento, I.C.

1996-01-01

The cross-section of the plasma column is TCA/BR has a nearly circular plasma shape. This allows implementation of simplified methods of magnetic diagnostics. Although these methods were in may tokamaks and are well described, their accuracies are not clearly defined because the very simplified theoretical model of plasma equilibrium on which they are based differs from the real conditions in tokamaks like TCA/BR. In this paper we present the methods of plasma position diagnostics in TCA/BR from external magnetic measurements with an error analysis. (author). 4 refs., 3 figs

Temporal diagnostic analysis of the SWAT model to detect dominant periods of poor model performance

Science.gov (United States)

Guse, Björn; Reusser, Dominik E.; Fohrer, Nicola

2013-04-01

Hydrological models generally include thresholds and non-linearities, such as snow-rain-temperature thresholds, non-linear reservoirs, infiltration thresholds and the like. When relating observed variables to modelling results, formal methods often calculate performance metrics over long periods, reporting model performance with only few numbers. Such approaches are not well suited to compare dominating processes between reality and model and to better understand when thresholds and non-linearities are driving model results. We present a combination of two temporally resolved model diagnostic tools to answer when a model is performing (not so) well and what the dominant processes are during these periods. We look at the temporal dynamics of parameter sensitivities and model performance to answer this question. For this, the eco-hydrological SWAT model is applied in the Treene lowland catchment in Northern Germany. As a first step, temporal dynamics of parameter sensitivities are analyzed using the Fourier Amplitude Sensitivity test (FAST). The sensitivities of the eight model parameters investigated show strong temporal variations. High sensitivities were detected for two groundwater (GW_DELAY, ALPHA_BF) and one evaporation parameters (ESCO) most of the time. The periods of high parameter sensitivity can be related to different phases of the hydrograph with dominances of the groundwater parameters in the recession phases and of ESCO in baseflow and resaturation periods. Surface runoff parameters show high parameter sensitivities in phases of a precipitation event in combination with high soil water contents. The dominant parameters give indication for the controlling processes during a given period for the hydrological catchment. The second step included the temporal analysis of model performance. For each time step, model performance was characterized with a "finger print" consisting of a large set of performance measures. These finger prints were clustered into

Overview of data acquisition system for SST-1 diagnostics

International Nuclear Information System (INIS)

Sharma, Manika; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-01-01

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Overview of data acquisition system for SST-1 diagnostics

Energy Technology Data Exchange (ETDEWEB)

Sharma, Manika, E-mail: bithi@ipr.res.in; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-11-15

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Scintillator-based diagnostic for fast ion loss measurements on DIII-D

International Nuclear Information System (INIS)

Fisher, R. K.; Van Zeeland, M. A.; Pace, D. C.; Heidbrink, W. W.; Muscatello, C. M.; Zhu, Y. B.; Garcia-Munoz, M.

2010-01-01

A new scintillator-based fast ion loss detector has been installed on DIII-D with the time response (>100 kHz) needed to study energetic ion losses induced by Alfven eigenmodes and other MHD instabilities. Based on the design used on ASDEX Upgrade, the diagnostic measures the pitch angle and gyroradius of ion losses based on the position of the ions striking the two-dimensional scintillator. For fast time response measurements, a beam splitter and fiberoptics couple a portion of the scintillator light to a photomultiplier. Reverse orbit following techniques trace the lost ions to their possible origin within the plasma. Initial DIII-D results showing prompt losses and energetic ion loss due to MHD instabilities are discussed.

A Memory-Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

Science.gov (United States)

Rubin, David C.; Berntsen, Dorthe; Bohni, Malene Klindt

2008-01-01

In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed., text rev.; American Psychiatric Association,…