Quantifying randomness in real networks

NASA Astrophysics Data System (ADS)

Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

2015-10-01

Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

The influence of retrieval practice on metacognition: The contribution of analytic and non-analytic processes.

PubMed

Miller, Tyler M; Geraci, Lisa

2016-05-01

People may change their memory predictions after retrieval practice using naïve theories of memory and/or by using subjective experience - analytic and non-analytic processes respectively. The current studies disentangled contributions of each process. In one condition, learners studied paired-associates, made a memory prediction, completed a short-run of retrieval practice and made a second prediction. In another condition, judges read about a yoked learners' retrieval practice performance but did not participate in retrieval practice and therefore, could not use non-analytic processes for the second prediction. In Study 1, learners reduced their predictions following moderately difficult retrieval practice whereas judges increased their predictions. In Study 2, learners made lower adjusted predictions than judges following both easy and difficult retrieval practice. In Study 3, judge-like participants used analytic processes to report adjusted predictions. Overall, the results suggested non-analytic processes play a key role for participants to reduce their predictions after retrieval practice. Copyright © 2016 Elsevier Inc. All rights reserved.

Healthcare predictive analytics: An overview with a focus on Saudi Arabia.

PubMed

Alharthi, Hana

2018-03-08

Despite a newfound wealth of data and information, the healthcare sector is lacking in actionable knowledge. This is largely because healthcare data, though plentiful, tends to be inherently complex and fragmented. Health data analytics, with an emphasis on predictive analytics, is emerging as a transformative tool that can enable more proactive and preventative treatment options. This review considers the ways in which predictive analytics has been applied in the for-profit business sector to generate well-timed and accurate predictions of key outcomes, with a focus on key features that may be applicable to healthcare-specific applications. Published medical research presenting assessments of predictive analytics technology in medical applications are reviewed, with particular emphasis on how hospitals have integrated predictive analytics into their day-to-day healthcare services to improve quality of care. This review also highlights the numerous challenges of implementing predictive analytics in healthcare settings and concludes with a discussion of current efforts to implement healthcare data analytics in the developing country, Saudi Arabia. Copyright © 2018 The Author. Published by Elsevier Ltd.. All rights reserved.

Using predictive analytics and big data to optimize pharmaceutical outcomes.

PubMed

Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Predictive analytics and child protection: constraints and opportunities.

PubMed

Russell, Jesse

2015-08-01

This paper considers how predictive analytics might inform, assist, and improve decision making in child protection. Predictive analytics represents recent increases in data quantity and data diversity, along with advances in computing technology. While the use of data and statistical modeling is not new to child protection decision making, its use in child protection is experiencing growth, and efforts to leverage predictive analytics for better decision-making in child protection are increasing. Past experiences, constraints and opportunities are reviewed. For predictive analytics to make the most impact on child protection practice and outcomes, it must embrace established criteria of validity, equity, reliability, and usefulness. Copyright © 2015 Elsevier Ltd. All rights reserved.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Predictive modeling of complications.

PubMed

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

2016-09-01

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

The Promise and Peril of Predictive Analytics in Higher Education: A Landscape Analysis

ERIC Educational Resources Information Center

Ekowo, Manuela; Palmer, Iris

2016-01-01

Predictive analytics in higher education is a hot-button topic among educators and administrators as institutions strive to better serve students by becoming more data-informed. In this paper, the authors describe how predictive analytics are used in higher education to identify students who need extra support, steer students in courses they will…

Improved partition equilibrium model for predicting analyte response in electrospray ionization mass spectrometry.

PubMed

Du, Lihong; White, Robert L

2009-02-01

A previously proposed partition equilibrium model for quantitative prediction of analyte response in electrospray ionization mass spectrometry is modified to yield an improved linear relationship. Analyte mass spectrometer response is modeled by a competition mechanism between analyte and background electrolytes that is based on partition equilibrium considerations. The correlation between analyte response and solution composition is described by the linear model over a wide concentration range and the improved model is shown to be valid for a wide range of experimental conditions. The behavior of an analyte in a salt solution, which could not be explained by the original model, is correctly predicted. The ion suppression effects of 16:0 lysophosphatidylcholine (LPC) on analyte signals are attributed to a combination of competition for excess charge and reduction of total charge due to surface tension effects. In contrast to the complicated mathematical forms that comprise the original model, the simplified model described here can more easily be employed to predict analyte mass spectrometer responses for solutions containing multiple components. Copyright (c) 2008 John Wiley & Sons, Ltd.

Scaling Student Success with Predictive Analytics: Reflections after Four Years in the Data Trenches

ERIC Educational Resources Information Center

Wagner, Ellen; Longanecker, David

2016-01-01

The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable,…

Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

NASA Astrophysics Data System (ADS)

Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

2016-09-01

Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

PubMed Central

2016-01-01

Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. PMID:27729304

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

PubMed

Hoyt, Robert Eugene; Snider, Dallas; Thompson, Carla; Mantravadi, Sarita

2016-10-11

We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

DOT National Transportation Integrated Search

2016-12-25

The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.

PubMed

Janke, Alexander T; Overbeek, Daniel L; Kocher, Keith E; Levy, Phillip D

2016-02-01

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Influence versus intent for predictive analytics in situation awareness

NASA Astrophysics Data System (ADS)

Cui, Biru; Yang, Shanchieh J.; Kadar, Ivan

2013-05-01

Predictive analytics in situation awareness requires an element to comprehend and anticipate potential adversary activities that might occur in the future. Most work in high level fusion or predictive analytics utilizes machine learning, pattern mining, Bayesian inference, and decision tree techniques to predict future actions or states. The emergence of social computing in broader contexts has drawn interests in bringing the hypotheses and techniques from social theory to algorithmic and computational settings for predictive analytics. This paper aims at answering the question on how influence and attitude (some interpreted such as intent) of adversarial actors can be formulated and computed algorithmically, as a higher level fusion process to provide predictions of future actions. The challenges in this interdisciplinary endeavor include drawing existing understanding of influence and attitude in both social science and computing fields, as well as the mathematical and computational formulation for the specific context of situation to be analyzed. The study of `influence' has resurfaced in recent years due to the emergence of social networks in the virtualized cyber world. Theoretical analysis and techniques developed in this area are discussed in this paper in the context of predictive analysis. Meanwhile, the notion of intent, or `attitude' using social theory terminologies, is a relatively uncharted area in the computing field. Note that a key objective of predictive analytics is to identify impending/planned attacks so their `impact' and `threat' can be prevented. In this spirit, indirect and direct observables are drawn and derived to infer the influence network and attitude to predict future threats. This work proposes an integrated framework that jointly assesses adversarial actors' influence network and their attitudes as a function of past actions and action outcomes. A preliminary set of algorithms are developed and tested using the Global Terrorism Database (GTD). Our results reveals the benefits to perform joint predictive analytics with both attitude and influence. At the same time, we discover significant challenges in deriving influence and attitude from indirect observables for diverse adversarial behavior. These observations warrant further investigation of optimal use of influence and attitude for predictive analytics, as well as the potential inclusion of other environmental or capability elements for the actors.

Predictive Analytics to Support Real-Time Management in Pathology Facilities.

PubMed

Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

2016-01-01

Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses.

Predictive Analytics to Support Real-Time Management in Pathology Facilities

PubMed Central

Lessard, Lysanne; Michalowski, Wojtek; Chen Li, Wei; Amyot, Daniel; Halwani, Fawaz; Banerjee, Diponkar

2016-01-01

Predictive analytics can provide valuable support to the effective management of pathology facilities. The introduction of new tests and technologies in anatomical pathology will increase the volume of specimens to be processed, as well as the complexity of pathology processes. In order for predictive analytics to address managerial challenges associated with the volume and complexity increases, it is important to pinpoint the areas where pathology managers would most benefit from predictive capabilities. We illustrate common issues in managing pathology facilities with an analysis of the surgical specimen process at the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital, which processes all surgical specimens for the Eastern Ontario Regional Laboratory Association. We then show how predictive analytics could be used to support management. Our proposed approach can be generalized beyond the DPLM, contributing to a more effective management of pathology facilities and in turn to quicker clinical diagnoses. PMID:28269873

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

PubMed

Alejo, Luz; Atkinson, John; Guzmán-Fierro, Víctor; Roeckel, Marlene

2018-05-16

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Pavement Performance : Approaches Using Predictive Analytics

DOT National Transportation Integrated Search

2018-03-23

Acceptable pavement condition is paramount to road safety. Using predictive analytics techniques, this project attempted to develop models that provide an assessment of pavement condition based on an array of indictors that include pavement distress,...

Analytical Finite Element Simulation Model for Structural Crashworthiness Prediction

DOT National Transportation Integrated Search

1974-02-01

The analytical development and appropriate derivations are presented for a simulation model of vehicle crashworthiness prediction. Incremental equations governing the nonlinear elasto-plastic dynamic response of three-dimensional frame structures are...

The Role of Teamwork in the Analysis of Big Data: A Study of Visual Analytics and Box Office Prediction.

PubMed

Buchanan, Verica; Lu, Yafeng; McNeese, Nathan; Steptoe, Michael; Maciejewski, Ross; Cooke, Nancy

2017-03-01

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

How health leaders can benefit from predictive analytics.

PubMed

Giga, Aliyah

2017-11-01

Predictive analytics can support a better integrated health system providing continuous, coordinated, and comprehensive person-centred care to those who could benefit most. In addition to dollars saved, using a predictive model in healthcare can generate opportunities for meaningful improvements in efficiency, productivity, costs, and better population health with targeted interventions toward patients at risk.

Evaluation of plasma proteomic data for Alzheimer disease state classification and for the prediction of progression from mild cognitive impairment to Alzheimer disease.

PubMed

Llano, Daniel A; Devanarayan, Viswanath; Simon, Adam J

2013-01-01

Previous studies that have examined the potential for plasma markers to serve as biomarkers for Alzheimer disease (AD) have studied single analytes and focused on the amyloid-β and τ isoforms and have failed to yield conclusive results. In this study, we performed a multivariate analysis of 146 plasma analytes (the Human DiscoveryMAP v 1.0 from Rules-Based Medicine) in 527 subjects with AD, mild cognitive impairment (MCI), or cognitively normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative database. We identified 4 different proteomic signatures, each using 5 to 14 analytes, that differentiate AD from control patients with sensitivity and specificity ranging from 74% to 85%. Five analytes were common to all 4 signatures: apolipoprotein A-II, apolipoprotein E, serum glutamic oxaloacetic transaminase, α-1-microglobulin, and brain natriuretic peptide. None of the signatures adequately predicted progression from MCI to AD over a 12- and 24-month period. A new panel of analytes, optimized to predict MCI to AD conversion, was able to provide 55% to 60% predictive accuracy. These data suggest that a simple panel of plasma analytes may provide an adjunctive tool to differentiate AD from controls, may provide mechanistic insights to the etiology of AD, but cannot adequately predict MCI to AD conversion.

An analytical and experimental study of crack extension in center-notched composites

NASA Technical Reports Server (NTRS)

Beuth, Jack L., Jr.; Herakovich, Carl T.

1987-01-01

The normal stress ratio theory for crack extension in anisotropic materials is studied analytically and experimentally. The theory is applied within a microscopic-level analysis of a single center notch of arbitrary orientation in a unidirectional composite material. The bulk of the analytical work of this study applies an elasticity solution for an infinite plate with a center line to obtain critical stress and crack growth direction predictions. An elasticity solution for an infinite plate with a center elliptical flaw is also used to obtain qualitative predictions of the location of crack initiation on the border of a rounded notch tip. The analytical portion of the study includes the formulation of a new crack growth theory that includes local shear stress. Normal stress ratio theory predictions are obtained for notched unidirectional tensile coupons and unidirectional Iosipescu shear specimens. These predictions are subsequently compared to experimental results.

Experimental Evaluation of Tuned Chamber Core Panels for Payload Fairing Noise Control

NASA Technical Reports Server (NTRS)

Schiller, Noah H.; Allen, Albert R.; Herlan, Jonathan W.; Rosenthal, Bruce N.

2015-01-01

Analytical models have been developed to predict the sound absorption and sound transmission loss of tuned chamber core panels. The panels are constructed of two facesheets sandwiching a corrugated core. When ports are introduced through one facesheet, the long chambers within the core can be used as an array of low-frequency acoustic resonators. To evaluate the accuracy of the analytical models, absorption and sound transmission loss tests were performed on flat panels. Measurements show that the acoustic resonators embedded in the panels improve both the absorption and transmission loss of the sandwich structure at frequencies near the natural frequency of the resonators. Analytical predictions for absorption closely match measured data. However, transmission loss predictions miss important features observed in the measurements. This suggests that higher-fidelity analytical or numerical models will be needed to supplement transmission loss predictions in the future.

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation - A Systematic Review.

PubMed

Tomaselli Muensterman, Elena; Tisdale, James E

2018-06-08

Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsades de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors, which include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of ≥ 2 QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this paper are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation, and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduces the risk of QTc interval prolongation in cardiac ICUs and identifies health-system patients at increased risk for mortality. The impact of these QTc interval prolongation predictive analytics on overall patient safety outcomes, such as TdP and sudden cardiac death relative to the cost of development and implementation, requires further study. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Continuous Metabolic Monitoring Based on Multi-Analyte Biomarkers to Predict Exhaustion

PubMed Central

Kastellorizios, Michail; Burgess, Diane J.

2015-01-01

This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject’s perception. PMID:26028477

Continuous metabolic monitoring based on multi-analyte biomarkers to predict exhaustion.

PubMed

Kastellorizios, Michail; Burgess, Diane J

2015-06-01

This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject's perception.

Automated Predictive Big Data Analytics Using Ontology Based Semantics.

PubMed

Nural, Mustafa V; Cotterell, Michael E; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A

2015-10-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.

Automated Predictive Big Data Analytics Using Ontology Based Semantics

PubMed Central

Nural, Mustafa V.; Cotterell, Michael E.; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A.

2017-01-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology. PMID:29657954

Analytic Formulation and Numerical Implementation of an Acoustic Pressure Gradient Prediction

NASA Technical Reports Server (NTRS)

Lee, Seongkyu; Brentner, Kenneth S.; Farassat, F.; Morris, Philip J.

2008-01-01

Two new analytical formulations of the acoustic pressure gradient have been developed and implemented in the PSU-WOPWOP rotor noise prediction code. The pressure gradient can be used to solve the boundary condition for scattering problems and it is a key aspect to solve acoustic scattering problems. The first formulation is derived from the gradient of the Ffowcs Williams-Hawkings (FW-H) equation. This formulation has a form involving the observer time differentiation outside the integrals. In the second formulation, the time differentiation is taken inside the integrals analytically. This formulation avoids the numerical time differentiation with respect to the observer time, which is computationally more efficient. The acoustic pressure gradient predicted by these new formulations is validated through comparison with available exact solutions for a stationary and moving monopole sources. The agreement between the predictions and exact solutions is excellent. The formulations are applied to the rotor noise problems for two model rotors. A purely numerical approach is compared with the analytical formulations. The agreement between the analytical formulations and the numerical method is excellent for both stationary and moving observer cases.

Development of a Nano-Satellite Micro-Coupling Mechanism with Characterization of a Shape Memory Alloy Interference Joint

DTIC Science & Technology

2010-12-01

satellite incorporation are explored by assembly and experimentation. Research on pseudoelastic material properties , analytical predictions, and...are explored by assembly and experimentation. Research on pseudoelastic material properties , analytical predictions, and tests of coupling strengths...20  Table 2.  Material Properties Used in Micro-Coupling Predicted Strength Calculations

Predictive Analytical Model for Isolator Shock-Train Location in a Mach 2.2 Direct-Connect Supersonic Combustion Tunnel

NASA Astrophysics Data System (ADS)

Lingren, Joe; Vanstone, Leon; Hashemi, Kelley; Gogineni, Sivaram; Donbar, Jeffrey; Akella, Maruthi; Clemens, Noel

2016-11-01

This study develops an analytical model for predicting the leading shock of a shock-train in the constant area isolator section in a Mach 2.2 direct-connect scramjet simulation tunnel. The effective geometry of the isolator is assumed to be a weakly converging duct owing to boundary-layer growth. For some given pressure rise across the isolator, quasi-1D equations relating to isentropic or normal shock flows can be used to predict the normal shock location in the isolator. The surface pressure distribution through the isolator was measured during experiments and both the actual and predicted locations can be calculated. Three methods of finding the shock-train location are examined, one based on the measured pressure rise, one using a non-physics-based control model, and one using the physics-based analytical model. It is shown that the analytical model performs better than the non-physics-based model in all cases. The analytic model is less accurate than the pressure threshold method but requires significantly less information to compute. In contrast to other methods for predicting shock-train location, this method is relatively accurate and requires as little as a single pressure measurement. This makes this method potentially useful for unstart control applications.

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

PubMed

Mønsted, Bjarke; Sapieżyński, Piotr; Ferrara, Emilio; Lehmann, Sune

2017-01-01

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

Biological ageing and clinical consequences of modern technology.

PubMed

Kyriazis, Marios

2017-08-01

The pace of technology is steadily increasing, and this has a widespread effect on all areas of health and society. When we interact with this technological environment we are exposed to a wide variety of new stimuli and challenges, which may modulate the stress response and thus change the way we respond and adapt. In this Opinion paper I will examine certain aspects of the human-computer interaction with regards to health and ageing. There are practical, everyday effects which also include social and cultural elements. I will discuss how human evolution may be affected by this new environmental change (the hormetic immersion in a virtual/technological environment). Finally, I will also explore certain biological aspects which have direct relevance to the ageing human. By embracing new technologies and engaging with a techno-social ecosystem (which is no longer formed by several interacting species, but by just two main elements: humans and machines), we may be subjected to beneficial hormetic effects, which upregulate the stress response and modulate adaptation. This is likely to improve overall health as we age and, as I speculate here, may also result in the reduction of age-related dysfunction.

Emergence of consensus as a modular-to-nested transition in communication dynamics

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics.

PubMed

Borge-Holthoefer, Javier; Baños, Raquel A; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-30

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics

PubMed Central

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources –visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems. PMID:28134358

New thinking: the evolution of human cognition

PubMed Central

Heyes, Cecilia

2012-01-01

Humans are animals that specialize in thinking and knowing, and our extraordinary cognitive abilities have transformed every aspect of our lives. In contrast to our chimpanzee cousins and Stone Age ancestors, we are complex political, economic, scientific and artistic creatures, living in a vast range of habitats, many of which are our own creation. Research on the evolution of human cognition asks what types of thinking make us such peculiar animals, and how they have been generated by evolutionary processes. New research in this field looks deeper into the evolutionary history of human cognition, and adopts a more multi-disciplinary approach than earlier ‘Evolutionary Psychology’. It is informed by comparisons between humans and a range of primate and non-primate species, and integrates findings from anthropology, archaeology, economics, evolutionary biology, neuroscience, philosophy and psychology. Using these methods, recent research reveals profound commonalities, as well striking differences, between human and non-human minds, and suggests that the evolution of human cognition has been much more gradual and incremental than previously assumed. It accords crucial roles to cultural evolution, techno-social co-evolution and gene–culture co-evolution. These have produced domain-general developmental processes with extraordinary power—power that makes human cognition, and human lives, unique. PMID:22734052

New thinking: the evolution of human cognition.

PubMed

Heyes, Cecilia

2012-08-05

Humans are animals that specialize in thinking and knowing, and our extraordinary cognitive abilities have transformed every aspect of our lives. In contrast to our chimpanzee cousins and Stone Age ancestors, we are complex political, economic, scientific and artistic creatures, living in a vast range of habitats, many of which are our own creation. Research on the evolution of human cognition asks what types of thinking make us such peculiar animals, and how they have been generated by evolutionary processes. New research in this field looks deeper into the evolutionary history of human cognition, and adopts a more multi-disciplinary approach than earlier 'Evolutionary Psychology'. It is informed by comparisons between humans and a range of primate and non-primate species, and integrates findings from anthropology, archaeology, economics, evolutionary biology, neuroscience, philosophy and psychology. Using these methods, recent research reveals profound commonalities, as well striking differences, between human and non-human minds, and suggests that the evolution of human cognition has been much more gradual and incremental than previously assumed. It accords crucial roles to cultural evolution, techno-social co-evolution and gene-culture co-evolution. These have produced domain-general developmental processes with extraordinary power-power that makes human cognition, and human lives, unique.

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

NASA Technical Reports Server (NTRS)

Javeed, Mehzad; Edighoffer, Harold H.; Mcgowan, Paul E.

1993-01-01

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined.

An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

PubMed

De Vore, Karl W; Fatahi, Nadia M; Sass, John E

2016-08-01

Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

Light aircraft crash safety program

NASA Technical Reports Server (NTRS)

Thomson, R. G.; Hayduk, R. J.

1974-01-01

NASA is embarked upon research and development tasks aimed at providing the general aviation industry with a reliable crashworthy airframe design technology. The goals of the NASA program are: reliable analytical techniques for predicting the nonlinear behavior of structures; significant design improvements of airframes; and simulated full-scale crash test data. The analytical tools will include both simplified procedures for estimating energy absorption characteristics and more complex computer programs for analysis of general airframe structures under crash loading conditions. The analytical techniques being developed both in-house and under contract are described, and a comparison of some analytical predictions with experimental results is shown.

The Behaviour of Naturally Debonded Composites Due to Bending Using a Meso-Level Model

NASA Astrophysics Data System (ADS)

Lord, C. E.; Rongong, J. A.; Hodzic, A.

2012-06-01

Numerical simulations and analytical models are increasingly being sought for the design and behaviour prediction of composite materials. The use of high-performance composite materials is growing in both civilian and defence related applications. With this growth comes the necessity to understand and predict how these new materials will behave under their exposed environments. In this study, the displacement behaviour of naturally debonded composites under out-of-plane bending conditions has been investigated. An analytical approach has been developed to predict the displacement response behaviour. The analytical model supports multi-layered composites with full and partial delaminations. The model can be used to extract bulk effective material properties in which can be represented, later, as an ESL (Equivalent Single Layer). The friction between each of the layers is included in the analytical model and is shown to have distinct behaviour for these types of composites. Acceptable agreement was observed between the model predictions, the ANSYS finite element model, and the experiments.

Experimental validation of an analytical kinetic model for edge-localized modes in JET-ITER-like wall

NASA Astrophysics Data System (ADS)

Guillemaut, C.; Metzger, C.; Moulton, D.; Heinola, K.; O’Mullane, M.; Balboa, I.; Boom, J.; Matthews, G. F.; Silburn, S.; Solano, E. R.; contributors, JET

2018-06-01

The design and operation of future fusion devices relying on H-mode plasmas requires reliable modelling of edge-localized modes (ELMs) for precise prediction of divertor target conditions. An extensive experimental validation of simple analytical predictions of the time evolution of target plasma loads during ELMs has been carried out here in more than 70 JET-ITER-like wall H-mode experiments with a wide range of conditions. Comparisons of these analytical predictions with diagnostic measurements of target ion flux density, power density, impact energy and electron temperature during ELMs are presented in this paper and show excellent agreement. The analytical predictions tested here are made with the ‘free-streaming’ kinetic model (FSM) which describes ELMs as a quasi-neutral plasma bunch expanding along the magnetic field lines into the Scrape-Off Layer without collisions. Consequences of the FSM on energy reflection and deposition on divertor targets during ELMs are also discussed.

Predicting playing frequencies for clarinets: A comparison between numerical simulations and simplified analytical formulas.

PubMed

Coyle, Whitney L; Guillemain, Philippe; Kergomard, Jean; Dalmont, Jean-Pierre

2015-11-01

When designing a wind instrument such as a clarinet, it can be useful to be able to predict the playing frequencies. This paper presents an analytical method to deduce these playing frequencies using the input impedance curve. Specifically there are two control parameters that have a significant influence on the playing frequency, the blowing pressure and reed opening. Four effects are known to alter the playing frequency and are examined separately: the flow rate due to the reed motion, the reed dynamics, the inharmonicity of the resonator, and the temperature gradient within the clarinet. The resulting playing frequencies for the first register of a particular professional level clarinet are found using the analytical formulas presented in this paper. The analytical predictions are then compared to numerically simulated results to validate the prediction accuracy. The main conclusion is that in general the playing frequency decreases above the oscillation threshold because of inharmonicity, then increases above the beating reed regime threshold because of the decrease of the flow rate effect.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior.

PubMed

Poore, Joshua C; Forlines, Clifton L; Miller, Sarah M; Regan, John R; Irvine, John M

2014-12-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures-personality, cognitive style, motivated cognition-predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated "top-down" cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior

PubMed Central

Forlines, Clifton L.; Miller, Sarah M.; Regan, John R.; Irvine, John M.

2014-01-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures—personality, cognitive style, motivated cognition—predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated “top-down” cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains. PMID:25983670

Analytical and experimental studies of graphite-epoxy and boron-epoxy angle ply laminates in compression

NASA Technical Reports Server (NTRS)

Weller, T.

1977-01-01

The applicability and adequacy of several computer techniques in predicting satisfactorily the nonlinear/inelastic response of angle ply laminates were evaluated. The analytical predictions were correlated with the results of a test program on the inelastic response under axial compression of a large variety of graphite-epoxy and boron-epoxy angle ply laminates. These comparison studies indicate that neither of the abovementioned analyses can satisfactorily predict either the mode of response or the ultimate stress value corresponding to a particular angle ply laminate configuration. Consequently, also the simple failure mechanisms assumed in the analytical models were not verified.

Are Higher Education Institutions Prepared for Learning Analytics?

ERIC Educational Resources Information Center

Ifenthaler, Dirk

2017-01-01

Higher education institutions and involved stakeholders can derive multiple benefits from learning analytics by using different data analytics strategies to produce summative, real-time, and predictive insights and recommendations. However, are institutions and academic as well as administrative staff prepared for learning analytics? A learning…

Turbine blade tip durability analysis

NASA Technical Reports Server (NTRS)

Mcknight, R. L.; Laflen, J. H.; Spamer, G. T.

1981-01-01

An air-cooled turbine blade from an aircraft gas turbine engine chosen for its history of cracking was subjected to advanced analytical and life-prediction techniques. The utility of advanced structural analysis techniques and advanced life-prediction techniques in the life assessment of hot section components are verified. Three dimensional heat transfer and stress analyses were applied to the turbine blade mission cycle and the results were input into advanced life-prediction theories. Shortcut analytical techniques were developed. The proposed life-prediction theories are evaluated.

Fluid manifold design for a solar energy storage tank

NASA Technical Reports Server (NTRS)

Humphries, W. R.; Hewitt, H. C.; Griggs, E. I.

1975-01-01

A design technique for a fluid manifold for use in a solar energy storage tank is given. This analytical treatment generalizes the fluid equations pertinent to manifold design, giving manifold pressures, velocities, and orifice pressure differentials in terms of appropriate fluid and manifold geometry parameters. Experimental results used to corroborate analytical predictions are presented. These data indicate that variations in discharge coefficients due to variations in orifices can cause deviations between analytical predictions and actual performance values.

Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

NASA Technical Reports Server (NTRS)

Hipol, Philip J.

1990-01-01

The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

Literature search of publications concerning the prediction of dynamic inlet flow distortion and related topics

NASA Technical Reports Server (NTRS)

Schweikhhard, W. G.; Chen, Y. S.

1983-01-01

Publications prior to March 1981 were surveyed to determine inlet flow dynamic distortion prediction methods and to catalog experimental and analytical information concerning inlet flow dynamic distortion prediction methods and to catalog experimental and analytical information concerning inlet flow dynamics at the engine-inlet interface of conventional aircraft (excluding V/STOL). The sixty-five publications found are briefly summarized and tabulated according to topic and are cross-referenced according to content and nature of the investigation (e.g., predictive, experimental, analytical and types of tests). Three appendices include lists of references, authors, organizations and agencies conducting the studies. Also, selected materials summaries, introductions and conclusions - from the reports are included. Few reports were found covering methods for predicting the probable maximum distortion. The three predictive methods found are those of Melick, Jacox and Motycka. The latter two require extensive high response pressure measurements at the compressor face, while the Melick Technique can function with as few as one or two measurements.

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

PubMed

Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

2018-01-01

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Prediction of relative and absolute permeabilities for gas and water from soil water retention curves using a pore-scale network model

NASA Astrophysics Data System (ADS)

Fischer, Ulrich; Celia, Michael A.

1999-04-01

Functional relationships for unsaturated flow in soils, including those between capillary pressure, saturation, and relative permeabilities, are often described using analytical models based on the bundle-of-tubes concept. These models are often limited by, for example, inherent difficulties in prediction of absolute permeabilities, and in incorporation of a discontinuous nonwetting phase. To overcome these difficulties, an alternative approach may be formulated using pore-scale network models. In this approach, the pore space of the network model is adjusted to match retention data, and absolute and relative permeabilities are then calculated. A new approach that allows more general assignments of pore sizes within the network model provides for greater flexibility to match measured data. This additional flexibility is especially important for simultaneous modeling of main imbibition and drainage branches. Through comparisons between the network model results, analytical model results, and measured data for a variety of both undisturbed and repacked soils, the network model is seen to match capillary pressure-saturation data nearly as well as the analytical model, to predict water phase relative permeabilities equally well, and to predict gas phase relative permeabilities significantly better than the analytical model. The network model also provides very good estimates for intrinsic permeability and thus for absolute permeabilities. Both the network model and the analytical model lost accuracy in predicting relative water permeabilities for soils characterized by a van Genuchten exponent n≲3. Overall, the computational results indicate that reliable predictions of both relative and absolute permeabilities are obtained with the network model when the model matches the capillary pressure-saturation data well. The results also indicate that measured imbibition data are crucial to good predictions of the complete hysteresis loop.

Aeroelastic loads and stability investigation of a full-scale hingeless rotor

NASA Technical Reports Server (NTRS)

Peterson, Randall L.; Johnson, Wayne

1991-01-01

An analytical investigation was conducted to study the influence of various parameters on predicting the aeroelastic loads and stability of a full-scale hingeless rotor in hover and forward flight. The CAMRAD/JA (Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics, Johnson Aeronautics) analysis code is used to obtain the analytical predictions. Data are presented for rotor blade bending and torsional moments as well as inplane damping data obtained for rotor operation in hover at a constant rotor rotational speed of 425 rpm and thrust coefficients between 0.0 and 0.12. Experimental data are presented from a test in the wind tunnel. Validation of the rotor system structural model with experimental rotor blade loads data shows excellent correlation with analytical results. Using this analysis, the influence of different aerodynamic inflow models, the number of generalized blade and body degrees of freedom, and the control-system stiffness at predicted stability levels are shown. Forward flight predictions of the BO-105 rotor system for 1-G thrust conditions at advance ratios of 0.0 to 0.35 are presented. The influence of different aerodynamic inflow models, dynamic inflow models and shaft angle variations on predicted stability levels are shown as a function of advance ratio.

Analytical relationships for prediction of the mechanical properties of additively manufactured porous biomaterials

PubMed Central

Hedayati, Reza

2016-01-01

Abstract Recent developments in additive manufacturing techniques have motivated an increasing number of researchers to study regular porous biomaterials that are based on repeating unit cells. The physical and mechanical properties of such porous biomaterials have therefore received increasing attention during recent years. One of the areas that have revived is analytical study of the mechanical behavior of regular porous biomaterials with the aim of deriving analytical relationships that could predict the relative density and mechanical properties of porous biomaterials, given the design and dimensions of their repeating unit cells. In this article, we review the analytical relationships that have been presented in the literature for predicting the relative density, elastic modulus, Poisson's ratio, yield stress, and buckling limit of regular porous structures based on various types of unit cells. The reviewed analytical relationships are used to compare the mechanical properties of porous biomaterials based on different types of unit cells. The major areas where the analytical relationships have improved during the recent years are discussed and suggestions are made for future research directions. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 3164–3174, 2016. PMID:27502358

Propagation of Bayesian Belief for Near-Real Time Statistical Assessment of Geosynchronous Satellite Status Based on Non-Resolved Photometry Data

DTIC Science & Technology

2014-09-01

of the BRDF for the Body and Panel. In order to provide a continuously updated baseline, the Photometry Model application is performed using a...brightness to its predicted brightness. The brightness predictions can be obtained using any analytical model chosen by the user. The inference for a...the analytical model as possible; and to mitigate the effect of bias that could be introduced by the choice of analytical model . It considers that a

Analysis methods for Kevlar shield response to rotor fragments

NASA Technical Reports Server (NTRS)

Gerstle, J. H.

1977-01-01

Several empirical and analytical approaches to rotor burst shield sizing are compared and principal differences in metal and fabric dynamic behavior are discussed. The application of transient structural response computer programs to predict Kevlar containment limits is described. For preliminary shield sizing, present analytical methods are useful if insufficient test data for empirical modeling are available. To provide other information useful for engineering design, analytical methods require further developments in material characterization, failure criteria, loads definition, and post-impact fragment trajectory prediction.

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

USGS Publications Warehouse

Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

2014-01-01

Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

Combined mechanical loading of composite tubes

NASA Technical Reports Server (NTRS)

Derstine, Mark S.; Pindera, Marek-Jerzy; Bowles, David E.

1988-01-01

An analytical/experimental investigation was performed to study the effect of material nonlinearities on the response of composite tubes subjected to combined axial and torsional loading. The effect of residual stresses on subsequent mechanical response was included in the investigation. Experiments were performed on P75/934 graphite-epoxy tubes with a stacking sequence of (15/0/ + or - 10/0/ -15), using pure torsion and combined axial/torsional loading. In the presence of residual stresses, the analytical model predicted a reduction in the initial shear modulus. Experimentally, coupling between axial loading and shear strain was observed in laminated tubes under combined loading. The phenomenon was predicted by the nonlinear analytical model. The experimentally observed linear limit of the global shear response was found to correspond to the analytically predicted first ply failure. Further, the failure of the tubes was found to be path dependent above a critical load level.

Assessment of analytical techniques for predicting solid propellant exhaust plumes

NASA Technical Reports Server (NTRS)

Tevepaugh, J. A.; Smith, S. D.; Penny, M. M.

1977-01-01

The calculation of solid propellant exhaust plume flow fields is addressed. Two major areas covered are: (1) the applicability of empirical data currently available to define particle drag coefficients, heat transfer coefficients, mean particle size and particle size distributions, and (2) thermochemical modeling of the gaseous phase of the flow field. Comparisons of experimentally measured and analytically predicted data are made. The experimental data were obtained for subscale solid propellant motors with aluminum loadings of 2, 10 and 15%. Analytical predictions were made using a fully coupled two-phase numerical solution. Data comparisons will be presented for radial distributions at plume axial stations of 5, 12, 16 and 20 diameters.

Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

PubMed

Keim-Malpass, Jessica; Kitzmiller, Rebecca R; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew T; Tai, Robert; Calland, James Forrest; Sullivan, Kevin; Randall Moorman, J; Anderson, Ruth A

2018-06-01

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting the Development of Analytical and Creative Abilities in Upper Elementary Grades

ERIC Educational Resources Information Center

Gubbels, Joyce; Segers, Eliane; Verhoeven, Ludo

2017-01-01

In some models, intelligence has been described as a multidimensional construct comprising both analytical and creative abilities. In addition, intelligence is considered to be dynamic rather than static. A structural equation model was used to examine the predictive role of cognitive (visual short-term memory, verbal short-term memory, selective…

Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

ERIC Educational Resources Information Center

He, Lingjun; Levine, Richard A.; Fan, Juanjuan; Beemer, Joshua; Stronach, Jeanne

2018-01-01

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

The Impact of Proactive Student-Success Coaching Using Predictive Analytics on Community College Students

ERIC Educational Resources Information Center

Hall, Mark Monroe

2017-01-01

The purpose of this study was to examine the effects of proactive student-success coaching, informed by predictive analytics, on student academic performance and persistence. Specifically, semester GPA and semester-to-semester student persistence were the investigated outcomes. Uniquely, the community college focused the intervention on only…

A NONSTEADY-STATE ANALYTICAL MODEL TO PREDICT GASEOUS EMISSIONS OF VOLATILE ORGANIC COMPOUNDS FROM LANDFILLS. (R825689C072)

EPA Science Inventory

Abstract

A general mathematical model is developed to predict emissions of volatile organic compounds (VOCs) from hazardous or sanitary landfills. The model is analytical in nature and includes important mechanisms occurring in unsaturated subsurface landfill environme...

How Predictive Analytics and Choice Architecture Can Improve Student Success

ERIC Educational Resources Information Center

Denley, Tristan

2014-01-01

This article explores the challenges that students face in navigating the curricular structure of post-secondary degree programs, and how predictive analytics and choice architecture can play a role. It examines Degree Compass, a course recommendation system that successfully pairs current students with the courses that best fit their talents and…

The Development of MST Test Information for the Prediction of Test Performances

ERIC Educational Resources Information Center

Park, Ryoungsun; Kim, Jiseon; Chung, Hyewon; Dodd, Barbara G.

2017-01-01

The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the…

The legal and ethical concerns that arise from using complex predictive analytics in health care.

PubMed

Cohen, I Glenn; Amarasingham, Ruben; Shah, Anand; Xie, Bin; Lo, Bernard

2014-07-01

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Project HOPE—The People-to-People Health Foundation, Inc.

Analytic cognitive style predicts paranormal explanations of anomalous experiences but not the experiences themselves: Implications for cognitive theories of delusions.

PubMed

Ross, Robert M; Hartig, Bjoern; McKay, Ryan

2017-09-01

It has been proposed that delusional beliefs are attempts to explain anomalous experiences. Why, then, do anomalous experiences induce delusions in some people but not in others? One possibility is that people with delusions have reasoning biases that result in them failing to reject implausible candidate explanations for anomalous experiences. We examine this hypothesis by studying paranormal interpretations of anomalous experiences. We examined whether analytic cognitive style (i.e. the willingness or disposition to critically evaluate outputs from intuitive processing and engage in effortful analytic processing) predicted anomalous experiences and paranormal explanations for these experiences after controlling for demographic variables and cognitive ability. Analytic cognitive style predicted paranormal explanations for anomalous experiences, but not the anomalous experiences themselves. We did not study clinical delusions. Our attempts to control for cognitive ability may have been inadequate. Our sample was predominantly students. Limited analytic cognitive style might contribute to the interpretation of anomalous experiences in terms of delusional beliefs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experimentally validated mathematical model of analyte uptake by permeation passive samplers.

PubMed

Salim, F; Ioannidis, M; Górecki, T

2017-11-15

A mathematical model describing the sampling process in a permeation-based passive sampler was developed and evaluated numerically. The model was applied to the Waterloo Membrane Sampler (WMS), which employs a polydimethylsiloxane (PDMS) membrane as a permeation barrier, and an adsorbent as a receiving phase. Samplers of this kind are used for sampling volatile organic compounds (VOC) from air and soil gas. The model predicts the spatio-temporal variation of sorbed and free analyte concentrations within the sampler components (membrane, sorbent bed and dead volume), from which the uptake rate throughout the sampling process can be determined. A gradual decline in the uptake rate during the sampling process is predicted, which is more pronounced when sampling higher concentrations. Decline of the uptake rate can be attributed to diminishing analyte concentration gradient within the membrane, which results from resistance to mass transfer and the development of analyte concentration gradients within the sorbent bed. The effects of changing the sampler component dimensions on the rate of this decline in the uptake rate can be predicted from the model. Performance of the model was evaluated experimentally for sampling of toluene vapors under controlled conditions. The model predictions proved close to the experimental values. The model provides a valuable tool to predict changes in the uptake rate during sampling, to assign suitable exposure times at different analyte concentration levels, and to optimize the dimensions of the sampler in a manner that minimizes these changes during the sampling period.

SU-C-204-01: A Fast Analytical Approach for Prompt Gamma and PET Predictions in a TPS for Proton Range Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kroniger, K; Herzog, M; Landry, G

2015-06-15

Purpose: We describe and demonstrate a fast analytical tool for prompt-gamma emission prediction based on filter functions applied on the depth dose profile. We present the implementation in a treatment planning system (TPS) of the same algorithm for positron emitter distributions. Methods: The prediction of the desired observable is based on the convolution of filter functions with the depth dose profile. For both prompt-gammas and positron emitters, the results of Monte Carlo simulations (MC) are compared with those of the analytical tool. For prompt-gamma emission from inelastic proton-induced reactions, homogeneous and inhomogeneous phantoms alongside with patient data are used asmore » irradiation targets of mono-energetic proton pencil beams. The accuracy of the tool is assessed in terms of the shape of the analytically calculated depth profiles and their absolute yields, compared to MC. For the positron emitters, the method is implemented in a research RayStation TPS and compared to MC predictions. Digital phantoms and patient data are used and positron emitter spatial density distributions are analyzed. Results: Calculated prompt-gamma profiles agree with MC within 3 % in terms of absolute yield and reproduce the correct shape. Based on an arbitrary reference material and by means of 6 filter functions (one per chemical element), profiles in any other material composed of those elements can be predicted. The TPS implemented algorithm is accurate enough to enable, via the analytically calculated positron emitters profiles, detection of range differences between the TPS and MC with errors of the order of 1–2 mm. Conclusion: The proposed analytical method predicts prompt-gamma and positron emitter profiles which generally agree with the distributions obtained by a full MC. The implementation of the tool in a TPS shows that reliable profiles can be obtained directly from the dose calculated by the TPS, without the need of full MC simulation.« less

PAUSE: Predictive Analytics Using SPARQL-Endpoints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Ainsworth, Keela; Bond, Nathaniel

2014-07-11

This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Incorporating photon recycling into the analytical drift-diffusion model of high efficiency solar cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lumb, Matthew P.; Naval Research Laboratory, Washington, DC 20375; Steiner, Myles A.

The analytical drift-diffusion formalism is able to accurately simulate a wide range of solar cell architectures and was recently extended to include those with back surface reflectors. However, as solar cells approach the limits of material quality, photon recycling effects become increasingly important in predicting the behavior of these cells. In particular, the minority carrier diffusion length is significantly affected by the photon recycling, with consequences for the solar cell performance. In this paper, we outline an approach to account for photon recycling in the analytical Hovel model and compare analytical model predictions to GaAs-based experimental devices operating close tomore » the fundamental efficiency limit.« less

Configuration and validation of an analytical model predicting secondary neutron radiation in proton therapy using Monte Carlo simulations and experimental measurements.

PubMed

Farah, J; Bonfrate, A; De Marzi, L; De Oliveira, A; Delacroix, S; Martinetti, F; Trompier, F; Clairand, I

2015-05-01

This study focuses on the configuration and validation of an analytical model predicting leakage neutron doses in proton therapy. Using Monte Carlo (MC) calculations, a facility-specific analytical model was built to reproduce out-of-field neutron doses while separately accounting for the contribution of intra-nuclear cascade, evaporation, epithermal and thermal neutrons. This model was first trained to reproduce in-water neutron absorbed doses and in-air neutron ambient dose equivalents, H*(10), calculated using MCNPX. Its capacity in predicting out-of-field doses at any position not involved in the training phase was also checked. The model was next expanded to enable a full 3D mapping of H*(10) inside the treatment room, tested in a clinically relevant configuration and finally consolidated with experimental measurements. Following the literature approach, the work first proved that it is possible to build a facility-specific analytical model that efficiently reproduces in-water neutron doses and in-air H*(10) values with a maximum difference less than 25%. In addition, the analytical model succeeded in predicting out-of-field neutron doses in the lateral and vertical direction. Testing the analytical model in clinical configurations proved the need to separate the contribution of internal and external neutrons. The impact of modulation width on stray neutrons was found to be easily adjustable while beam collimation remains a challenging issue. Finally, the model performance agreed with experimental measurements with satisfactory results considering measurement and simulation uncertainties. Analytical models represent a promising solution that substitutes for time-consuming MC calculations when assessing doses to healthy organs. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Empirical testing of an analytical model predicting electrical isolation of photovoltaic models

NASA Astrophysics Data System (ADS)

Garcia, A., III; Minning, C. P.; Cuddihy, E. F.

A major design requirement for photovoltaic modules is that the encapsulation system be capable of withstanding large DC potentials without electrical breakdown. Presented is a simple analytical model which can be used to estimate material thickness to meet this requirement for a candidate encapsulation system or to predict the breakdown voltage of an existing module design. A series of electrical tests to verify the model are described in detail. The results of these verification tests confirmed the utility of the analytical model for preliminary design of photovoltaic modules.

B-52 control configured vehicles: Flight test results

NASA Technical Reports Server (NTRS)

Arnold, J. I.; Murphy, F. B.

1976-01-01

Recently completed B-52 Control Configured Vehicles (CCV) flight testing is summarized, and results are compared to analytical predictions. Results are presented for five CCV system concepts: ride control, maneuver load control, flutter mode control, augmented stability, and fatigue reduction. Test results confirm analytical predictions and show that CCV system concepts achieve performance goals when operated individually or collectively.

Beyond Engagement Analytics: Which Online Mixed-Data Factors Predict Student Learning Outcomes?

ERIC Educational Resources Information Center

Strang, Kenneth David

2017-01-01

This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large…

Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

ERIC Educational Resources Information Center

Abdous, M'hammed; He, Wu; Yen, Cherng-Jyh

2012-01-01

As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students' learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped…

Analysis of a virtual memory model for maintaining database views

NASA Technical Reports Server (NTRS)

Kinsley, Kathryn C.; Hughes, Charles E.

1992-01-01

This paper presents an analytical model for predicting the performance of a new support strategy for database views. This strategy, called the virtual method, is compared with traditional methods for supporting views. The analytical model's predictions of improved performance by the virtual method are then validated by comparing these results with those achieved in an experimental implementation.

Buckling Testing and Analysis of Space Shuttle Solid Rocket Motor Cylinders

NASA Technical Reports Server (NTRS)

Weidner, Thomas J.; Larsen, David V.; McCool, Alex (Technical Monitor)

2002-01-01

A series of full-scale buckling tests were performed on the space shuttle Reusable Solid Rocket Motor (RSRM) cylinders. The tests were performed to determine the buckling capability of the cylinders and to provide data for analytical comparison. A nonlinear ANSYS Finite Element Analysis (FEA) model was used to represent and evaluate the testing. Analytical results demonstrated excellent correlation to test results, predicting the failure load within 5%. The analytical value was on the conservative side, predicting a lower failure load than was applied to the test. The resulting study and analysis indicated the important parameters for FEA to accurately predict buckling failure. The resulting method was subsequently used to establish the pre-launch buckling capability of the space shuttle system.

Empirically Optimized Flow Cytometric Immunoassay Validates Ambient Analyte Theory

PubMed Central

Parpia, Zaheer A.; Kelso, David M.

2010-01-01

Ekins’ ambient analyte theory predicts, counter intuitively, that an immunoassay’s limit of detection can be improved by reducing the amount of capture antibody. In addition, it also anticipates that results should be insensitive to the volume of sample as well as the amount of capture antibody added. The objective of this study is to empirically validate all of the performance characteristics predicted by Ekins’ theory. Flow cytometric analysis was used to detect binding between a fluorescent ligand and capture microparticles since it can directly measure fractional occupancy, the primary response variable in ambient analyte theory. After experimentally determining ambient analyte conditions, comparisons were carried out between ambient and non-ambient assays in terms of their signal strengths, limits of detection, and their sensitivity to variations in reaction volume and number of particles. The critical number of binding sites required for an assay to be in the ambient analyte region was estimated to be 0.1VKd. As predicted, such assays exhibited superior signal/noise levels and limits of detection; and were not affected by variations in sample volume and number of binding sites. When the signal detected measures fractional occupancy, ambient analyte theory is an excellent guide to developing assays with superior performance characteristics. PMID:20152793

Analytical relationships for prediction of the mechanical properties of additively manufactured porous biomaterials.

PubMed

Zadpoor, Amir Abbas; Hedayati, Reza

2016-12-01

Recent developments in additive manufacturing techniques have motivated an increasing number of researchers to study regular porous biomaterials that are based on repeating unit cells. The physical and mechanical properties of such porous biomaterials have therefore received increasing attention during recent years. One of the areas that have revived is analytical study of the mechanical behavior of regular porous biomaterials with the aim of deriving analytical relationships that could predict the relative density and mechanical properties of porous biomaterials, given the design and dimensions of their repeating unit cells. In this article, we review the analytical relationships that have been presented in the literature for predicting the relative density, elastic modulus, Poisson's ratio, yield stress, and buckling limit of regular porous structures based on various types of unit cells. The reviewed analytical relationships are used to compare the mechanical properties of porous biomaterials based on different types of unit cells. The major areas where the analytical relationships have improved during the recent years are discussed and suggestions are made for future research directions. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 3164-3174, 2016. © 2016 The Authors Journal of Biomedical Materials Research Part A Published by Wiley Periodicals, Inc.

Thermal Modeling of Resistance Spot Welding and Prediction of Weld Microstructure

NASA Astrophysics Data System (ADS)

Sheikhi, M.; Valaee Tale, M.; Usefifar, GH. R.; Fattah-Alhosseini, Arash

2017-11-01

The microstructure of nuggets in resistance spot welding can be influenced by the many variables involved. This study aimed at examining such a relationship and, consequently, put forward an analytical model to predict the thermal history and microstructure of the nugget zone. Accordingly, a number of numerical simulations and experiments were conducted and the accuracy of the model was assessed. The results of this assessment revealed that the proposed analytical model could accurately predict the cooling rate in the nugget and heat-affected zones. Moreover, both analytical and numerical models confirmed that sheet thickness and electrode-sheet interface temperature were the most important factors influencing the cooling rate at temperatures lower than about T l/2. Decomposition of austenite is one of the most important transformations in steels occurring over this temperature range. Therefore, an easy-to-use map was designed against these parameters to predict the weld microstructure.

Synthesized airfoil data method for prediction of dynamic stall and unsteady airloads

NASA Technical Reports Server (NTRS)

Gangwani, S. T.

1983-01-01

A detailed analysis of dynamic stall experiments has led to a set of relatively compact analytical expressions, called synthesized unsteady airfoil data, which accurately describe in the time-domain the unsteady aerodynamic characteristics of stalled airfoils. An analytical research program was conducted to expand and improve this synthesized unsteady airfoil data method using additional available sets of unsteady airfoil data. The primary objectives were to reduce these data to synthesized form for use in rotor airload prediction analyses and to generalize the results. Unsteady drag data were synthesized which provided the basis for successful expansion of the formulation to include computation of the unsteady pressure drag of airfoils and rotor blades. Also, an improved prediction model for airfoil flow reattachment was incorporated in the method. Application of this improved unsteady aerodynamics model has resulted in an improved correlation between analytic predictions and measured full scale helicopter blade loads and stress data.

Gradient retention prediction of acid-base analytes in reversed phase liquid chromatography: a simplified approach for acetonitrile-water mobile phases.

PubMed

Andrés, Axel; Rosés, Martí; Bosch, Elisabeth

2014-11-28

In previous work, a two-parameter model to predict chromatographic retention of ionizable analytes in gradient mode was proposed. However, the procedure required some previous experimental work to get a suitable description of the pKa change with the mobile phase composition. In the present study this previous experimental work has been simplified. The analyte pKa values have been calculated through equations whose coefficients vary depending on their functional group. Forced by this new approach, other simplifications regarding the retention of the totally neutral and totally ionized species also had to be performed. After the simplifications were applied, new prediction values were obtained and compared with the previously acquired experimental data. The simplified model gave pretty good predictions while saving a significant amount of time and resources. Copyright © 2014 Elsevier B.V. All rights reserved.

Broadband Trailing Edge Noise Predictions in the Time Domain. Revised

NASA Technical Reports Server (NTRS)

Casper, Jay; Farassat, Fereidoun

2003-01-01

A recently developed analytic result in acoustics, "Formulation 1B," is used to compute broadband trailing edge noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Willliams-Hawkings equation with the loading source term, and has been shown in previous research to provide time domain predictions of broadband noise that are in excellent agreement with experimental results. Furthermore, this formulation lends itself readily to rotating reference frames and statistical analysis of broadband trailing edge noise. Formulation 1B is used to calculate the far field noise radiated from the trailing edge of a NACA 0012 airfoil in low Mach number flows, by using both analytical and experimental data on the airfoil surface. The acoustic predictions are compared with analytical results and experimental measurements that are available in the literature. Good agreement between predictions and measurements is obtained.

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

NASA Technical Reports Server (NTRS)

Lee, Allan Y.; Stupik, Joan

2015-01-01

While there has been extensive theoretical and analytical research regarding the characterization of spacecraft propellant slosh and structural frequencies, there have been limited studies to compare the analytical predictions with measured flight data. This paper uses flight telemetry from the Cassini spacecraft to get estimates of high-g propellant slosh frequencies and the magnetometer boom frequency characteristics, and compares these values with those predicted by theoretical works. Most Cassini attitude control data are available at a telemetry frequency of 0.5 Hz. Moreover, liquid sloshing is attenuated by propellant management device and attitude controllers. Identification of slosh and structural frequency are made on a best-effort basis. This paper reviews the analytical approaches that were used to predict the Cassini propellant slosh frequencies. The predicted frequencies are then compared with those estimated using telemetry from selected Cassini burns where propellant sloshing was observed (such as the Saturn Orbit Insertion burn).

Verification of spatial and temporal pressure distributions in segmented solid rocket motors

NASA Technical Reports Server (NTRS)

Salita, Mark

1989-01-01

A wide variety of analytical tools are in use today to predict the history and spatial distributions of pressure in the combustion chambers of solid rocket motors (SRMs). Experimental and analytical methods are presented here that allow the verification of many of these predictions. These methods are applied to the redesigned space shuttle booster (RSRM). Girth strain-gage data is compared to the predictions of various one-dimensional quasisteady analyses in order to verify the axial drop in motor static pressure during ignition transients as well as quasisteady motor operation. The results of previous modeling of radial flows in the bore, slots, and around grain overhangs are supported by approximate analytical and empirical techniques presented here. The predictions of circumferential flows induced by inhibitor asymmetries, nozzle vectoring, and propellant slump are compared to each other and to subscale cold air and water tunnel measurements to ascertain their validity.

Data Analytics in Procurement Fraud Prevention

DTIC Science & Technology

2014-05-30

Certified Fraud Examiners CAC common access card COR contracting officer’s representative CPAR Contractor Performance Assessment Reporting System DCAA...using analytics to predict patterns occurring in known credit card fraud investigations to prevent future schemes before they happen. The goal of...or iTunes . 4. Distributional Analytics Distributional analytics are used to detect anomalies within data. Through the use of distributional

Compelling evidence for Lucky Survivor and gas phase protonation: the unified MALDI analyte protonation mechanism.

PubMed

Jaskolla, Thorsten W; Karas, Michael

2011-06-01

This work experimentally verifies and proves the two long since postulated matrix-assisted laser desorption/ionization (MALDI) analyte protonation pathways known as the Lucky Survivor and the gas phase protonation model. Experimental differentiation between the predicted mechanisms becomes possible by the use of deuterated matrix esters as MALDI matrices, which are stable under typical sample preparation conditions and generate deuteronated reagent ions, including the deuterated and deuteronated free matrix acid, only upon laser irradiation in the MALDI process. While the generation of deuteronated analyte ions proves the gas phase protonation model, the detection of protonated analytes by application of deuterated matrix compounds without acidic hydrogens proves the survival of analytes precharged from solution in accordance with the predictions from the Lucky Survivor model. The observed ratio of the two analyte ionization processes depends on the applied experimental parameters as well as the nature of analyte and matrix. Increasing laser fluences and lower matrix proton affinities favor gas phase protonation, whereas more quantitative analyte protonation in solution and intramolecular ion stabilization leads to more Lucky Survivors. The presented results allow for a deeper understanding of the fundamental processes causing analyte ionization in MALDI and may alleviate future efforts for increasing the analyte ion yield.

Fluid mechanics of dynamic stall. II - Prediction of full scale characteristics

NASA Technical Reports Server (NTRS)

Ericsson, L. E.; Reding, J. P.

1988-01-01

Analytical extrapolations are made from experimental subscale dynamics to predict full scale characteristics of dynamic stall. The method proceeds by establishing analytic relationships between dynamic and static aerodynamic characteristics induced by viscous flow effects. The method is then validated by predicting dynamic test results on the basis of corresponding static test data obtained at the same subscale flow conditions, and the effect of Reynolds number on the static aerodynamic characteristics are determined from subscale to full scale flow conditions.

Analytical prediction of digital signal crosstalk of FCC

NASA Technical Reports Server (NTRS)

Belleisle, A. P.

1972-01-01

The results are presented of study effort whose aim was the development of accurate means of analyzing and predicting signal cross-talk in multi-wire digital data cables. A complete analytical model is developed n + 1 wire systems of uniform transmission lines with arbitrary linear boundary conditions. In addition, a minimum set of parameter measurements required for the application of the model are presented. Comparisons between cross-talk predicted by this model and actual measured cross-talk are shown for a six conductor ribbon cable.

Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions

NASA Technical Reports Server (NTRS)

Balmes, Etienne

1993-01-01

An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.

Q&A with Y.C. Zhang - Bringing Talent and Passion to Power Systems | News

Science.gov Websites

Yingchen Zhang Yingchen Zhang (Y.C.) is the group manager of the sensing and predictive analytics group in appointed manager of the Sensing and Predictive Analytics Group in NREL's Power Systems Engineering Center , my past research involved synchrophasors, which are one type of metering system. Nowadays, many more

Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

ERIC Educational Resources Information Center

Calvert, Carol Elaine

2014-01-01

This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

USGS Publications Warehouse

Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints

PubMed Central

Thompson, John R; Spata, Enti; Abrams, Keith R

2015-01-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions. PMID:26271918

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Spata, Enti; Abrams, Keith R

2017-10-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing-remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions.

Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yingchen; Yang, Rui; Hodge, Brian S

Energy sustainability is a subject of concern to many nations in the modern world. It is critical for electric power systems to diversify energy supply to include systems with different physical characteristics, such as wind energy, solar energy, electrochemical energy storage, thermal storage, bio-energy systems, geothermal, and ocean energy. Each system has its own range of control variables and targets. To be able to operate such a complex energy system, big-data analytics become critical to achieve the goal of predicting energy supplies and consumption patterns, assessing system operation conditions, and estimating system states - all providing situational awareness to powermore » system operators. This chapter presents data analytics and machine learning-based approaches to enable predictive situational awareness of the power systems.« less

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

PubMed

Tulabandhula, Theja; Rudin, Cynthia

2014-06-01

Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

Application of stiffened cylinder analysis to ATP interior noise studies

NASA Technical Reports Server (NTRS)

Wilby, E. G.; Wilby, J. F.

1983-01-01

An analytical model developed to predict the interior noise of propeller driven aircraft was applied to experimental configurations for a Fairchild Swearingen Metro II fuselage exposed to simulated propeller excitation. The floor structure of the test fuselage was of unusual construction - mounted on air springs. As a consequence, the analytical model was extended to include a floor treatment transmission coefficient which could be used to describe vibration attenuation through the mounts. Good agreement was obtained between measured and predicted noise reductions when the foor treatment transmission loss was about 20 dB - a value which is consistent with the vibration attenuation provided by the mounts. The analytical model was also adapted to allow the prediction of noise reductions associated with boundary layer excitation as well as propeller and reverberant noise.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

PubMed Central

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

TH-A-19A-06: Site-Specific Comparison of Analytical and Monte Carlo Based Dose Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schuemann, J; Grassberger, C; Paganetti, H

2014-06-15

Purpose: To investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict dose distributions and to verify currently used uncertainty margins in proton therapy. Methods: Dose distributions predicted by an analytical pencilbeam algorithm were compared with Monte Carlo simulations (MCS) using TOPAS. 79 complete patient treatment plans were investigated for 7 disease sites (liver, prostate, breast, medulloblastoma spine and whole brain, lung and head and neck). A total of 508 individual passively scattered treatment fields were analyzed for field specific properties. Comparisons based on target coverage indices (EUD, D95, D90 and D50)more » were performed. Range differences were estimated for the distal position of the 90% dose level (R90) and the 50% dose level (R50). Two-dimensional distal dose surfaces were calculated and the root mean square differences (RMSD), average range difference (ARD) and average distal dose degradation (ADD), the distance between the distal position of the 80% and 20% dose levels (R80- R20), were analyzed. Results: We found target coverage indices calculated by TOPAS to generally be around 1–2% lower than predicted by the analytical algorithm. Differences in R90 predicted by TOPAS and the planning system can be larger than currently applied range margins in proton therapy for small regions distal to the target volume. We estimate new site-specific range margins (R90) for analytical dose calculations considering total range uncertainties and uncertainties from dose calculation alone based on the RMSD. Our results demonstrate that a reduction of currently used uncertainty margins is feasible for liver, prostate and whole brain fields even without introducing MC dose calculations. Conclusion: Analytical dose calculation algorithms predict dose distributions within clinical limits for more homogeneous patients sites (liver, prostate, whole brain). However, we recommend treatment plan verification using Monte Carlo simulations for patients with complex geometries.« less

Theory of precipitation effects on dead cylindrical fuels

Treesearch

Michael A. Fosberg

1972-01-01

Numerical and analytical solutions of the Fickian diffusion equation were used to determine the effects of precipitation on dead cylindrical forest fuels. The analytical solution provided a physical framework. The numerical solutions were then used to refine the analytical solution through a similarity argument. The theoretical solutions predicted realistic rates of...

Developing a Code of Practice for Learning Analytics

ERIC Educational Resources Information Center

Sclater, Niall

2016-01-01

Ethical and legal objections to learning analytics are barriers to development of the field, thus potentially denying students the benefits of predictive analytics and adaptive learning. Jisc, a charitable organization that champions the use of digital technologies in UK education and research, has attempted to address this with the development of…

Analytic Cognitive Style Predicts Religious and Paranormal Belief

ERIC Educational Resources Information Center

Pennycook, Gordon; Cheyne, James Allan; Seli, Paul; Koehler, Derek J.; Fugelsang, Jonathan A.

2012-01-01

An analytic cognitive style denotes a propensity to set aside highly salient intuitions when engaging in problem solving. We assess the hypothesis that an analytic cognitive style is associated with a history of questioning, altering, and rejecting (i.e., unbelieving) supernatural claims, both religious and paranormal. In two studies, we examined…

Affect, Reason, and Persuasion: Advertising Strategies That Predict Affective and Analytic-Cognitive Responses.

ERIC Educational Resources Information Center

Chaudhuri, Arjun; Buck, Ross

1995-01-01

Develops and tests hypotheses concerning the relationship of specific advertising strategies to affective and analytic cognitive responses of the audience. Analyses undergraduate students' responses to 240 advertisements. Demonstrates that advertising strategy variables accounted substantially for the variance in affective and analytic cognition.…

Big Data: You Are Adding to . . . and Using It

ERIC Educational Resources Information Center

Makela, Carole J.

2016-01-01

"Big data" prompts a whole lexicon of terms--data flow; analytics; data mining; data science; smart you name it (cars, houses, cities, wearables, etc.); algorithms; learning analytics; predictive analytics; data aggregation; data dashboards; digital tracks; and big data brokers. New terms are being coined frequently. Are we paying…

Analytical modeling of fire growth on fire-resistive wood-based materials with changing conditions

Treesearch

Mark A. Dietenberger

2006-01-01

Our analytical model of fire growth for the ASTM E 84 tunnel, which simultaneously predicts heat release rate, flame-over area, and pyrolysis area as functions of time for constant conditions, was documented in the 2001 BCC Symposium for different treated wood materials. The model was extended to predict ignition and fire growth on exterior fire-resistive structures...

Delivery of State-Provided Predictive Analytics to Schools: Wisconsin's DEWS and the Proposed EWIMS Dashboard. WCER Working Paper No. 2016-3

ERIC Educational Resources Information Center

Clune, Bill; Knowles, Jared

2016-01-01

Since 2012, the Wisconsin Department of Public Instruction (DPI) has maintained a statewide predictive analytics system providing schools with an early warning in middle grades of students at risk for not completing high school. DPI is considering extending and enhancing this system, known as the Dropout Early Warning System (DEWS). The proposed…

Predictive Analytics for Coordinated Optimization in Distribution Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui

This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.

Data Analytics in Procurement Fraud Prevention

DTIC Science & Technology

2014-06-01

access card COR contracting officer’s representative CPAR Contractor Performance Assessment Reporting System DCAA Defense Contract Audit Agency DOD...of this can be seen in a company using analytics to predict patterns occurring in known credit card fraud investigations to prevent future schemes...a website such as Amazon or iTunes . 10 4. Distributional Analytics Distributional analytics are used to detect anomalies within data. Through the

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Guidelines and Parameter Selection for the Simulation of Progressive Delamination

NASA Technical Reports Server (NTRS)

Song, Kyongchan; Davila, Carlos G.; Rose, Cheryl A.

2008-01-01

Turon s methodology for determining optimal analysis parameters for the simulation of progressive delamination is reviewed. Recommended procedures for determining analysis parameters for efficient delamination growth predictions using the Abaqus/Standard cohesive element and relatively coarse meshes are provided for single and mixed-mode loading. The Abaqus cohesive element, COH3D8, and a user-defined cohesive element are used to develop finite element models of the double cantilever beam specimen, the end-notched flexure specimen, and the mixed-mode bending specimen to simulate progressive delamination growth in Mode I, Mode II, and mixed-mode fracture, respectively. The predicted responses are compared with their analytical solutions. The results show that for single-mode fracture, the predicted responses obtained with the Abaqus cohesive element correlate well with the analytical solutions. For mixed-mode fracture, it was found that the response predicted using COH3D8 elements depends on the damage evolution criterion that is used. The energy-based criterion overpredicts the peak loads and load-deflection response. The results predicted using a tabulated form of the BK criterion correlate well with the analytical solution and with the results predicted with the user-written element.

Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

PubMed

Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

2017-04-01

Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictive data modeling of human type II diabetes related statistics

NASA Astrophysics Data System (ADS)

Jaenisch, Kristina L.; Jaenisch, Holger M.; Handley, James W.; Albritton, Nathaniel G.

2009-04-01

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

Development of an analytical-numerical model to predict radiant emission or absorption

NASA Technical Reports Server (NTRS)

Wallace, Tim L.

1994-01-01

The development of an analytical-numerical model to predict radiant emission or absorption is discussed. A voigt profile is assumed to predict the spectral qualities of a singlet atomic transition line for atomic species of interest to the OPAD program. The present state of this model is described in each progress report required under contract. Model and code development is guided by experimental data where available. When completed, the model will be used to provide estimates of specie erosion rates from spectral data collected from rocket exhaust plumes or other sources.

Charged aerodynamics of a Low Earth Orbit cylinder

NASA Astrophysics Data System (ADS)

Capon, C. J.; Brown, M.; Boyce, R. R.

2016-11-01

This work investigates the charged aerodynamic interaction of a Low Earth Orbiting (LEO) cylinder with the ionosphere. The ratio of charge to neutral drag force on a 2D LEO cylinder with diffusely reflecting cool walls is derived analytically and compared against self-consistent electrostatic Particle-in-Cell (PIC) simulations. Analytical calculations predict that neglecting charged drag in an O+ dominated LEO plasma with a neutral to ion number density ratio of 102 will cause a 10% over-prediction of O density based on body accelerations when body potential (ɸB) is ≤ -390 V. Above 900 km altitude in LEO, where H+ becomes the dominant ion species, analytical predictions suggest charge drag becomes equivalent to neutral drag for ɸB ≤ -0.75 V. Comparing analytical predictions against PIC simulations in the range of 0 < - ɸB < 50 V found that analytical charged drag was under-estimated for all body potentials; the degree of under-estimation increasing with ɸB. Based on the -50 V PIC simulations, our in-house 6 degree of freedom orbital propagator saw a reduction in the semi-major axis of a 10 kg satellite at 700 km of 6.9 m/day and 0.98 m/day at 900 km compared that caused purely by neutral drag - 0.67 m/day and 0.056 m/day respectively. Hence, this work provides initial evidence that charged aerodynamics may become significant compared to neutral aerodynamics for high voltage LEO bodies.

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

NASA Technical Reports Server (NTRS)

Lee, Allan Y.; Stupik, Joan

2015-01-01

While there has been extensive theoretical and analytical research regarding the characterization of spacecraft propellant slosh and structural frequencies, there have been limited studies to compare the analytical predictions with measured flight data. This paper uses flight telemetry from the Cassini spacecraft to get estimates of high-g propellant slosh frequencies and the magnetometer boom frequency characteristics, and compares these values with those predicted by theoretical works. Most Cassini attitude control data are available at a telemetry frequency of 0.5 Hz. Moreover, liquid sloshing is attenuated by propellant management device and attitude controllers. Identification of slosh and structural frequency are made on a best-effort basis. This paper reviews the analytical approaches that were used to predict the Cassini propellant slosh frequencies. The predicted frequencies are then compared with those estimated using telemetry from selected Cassini burns where propellant sloshing was observed (such as the Saturn Orbit Insertion burn). Determination of the magnetometer boom structural frequency is also discussed.

Confocal Raman Microscopy for pH-Gradient Preconcentration and Quantitative Analyte Detection in Optically Trapped Phospholipid Vesicles.

PubMed

Hardcastle, Chris D; Harris, Joel M

2015-08-04

The ability of a vesicle membrane to preserve a pH gradient, while allowing for diffusion of neutral molecules across the phospholipid bilayer, can provide the isolation and preconcentration of ionizable compounds within the vesicle interior. In this work, confocal Raman microscopy is used to observe (in situ) the pH-gradient preconcentration of compounds into individual optically trapped vesicles that provide sub-femtoliter collectors for small-volume samples. The concentration of analyte accumulated in the vesicle interior is determined relative to a perchlorate-ion internal standard, preloaded into the vesicle along with a high-concentration buffer. As a guide to the experiments, a model for the transfer of analyte into the vesicle based on acid-base equilibria is developed to predict the concentration enrichment as a function of source-phase pH and analyte concentration. To test the concept, the accumulation of benzyldimethylamine (BDMA) was measured within individual 1 μm phospholipid vesicles having a stable initial pH that is 7 units lower than the source phase. For low analyte concentrations in the source phase (100 nM), a concentration enrichment into the vesicle interior of (5.2 ± 0.4) × 10(5) was observed, in agreement with the model predictions. Detection of BDMA from a 25 nM source-phase sample was demonstrated, a noteworthy result for an unenhanced Raman scattering measurement. The developed model accurately predicts the falloff of enrichment (and measurement sensitivity) at higher analyte concentrations, where the transfer of greater amounts of BDMA into the vesicle titrates the internal buffer and decreases the pH gradient. The predictable calibration response over 4 orders of magnitude in source-phase concentration makes it suitable for quantitative analysis of ionizable compounds from small-volume samples. The kinetics of analyte accumulation are relatively fast (∼15 min) and are consistent with the rate of transfer of a polar aromatic molecule across a gel-phase phospholipid membrane.

Heat generation in Aircraft tires under yawed rolling conditions

NASA Technical Reports Server (NTRS)

Dodge, Richard N.; Clark, Samuel K.

1987-01-01

An analytical model was developed for approximating the internal temperature distribution in an aircraft tire operating under conditions of yawed rolling. The model employs an assembly of elements to represent the tire cross section and treats the heat generated within the tire as a function of the change in strain energy associated with predicted tire flexure. Special contact scrubbing terms are superimposed on the symmetrical free rolling model to account for the slip during yawed rolling. An extensive experimental program was conducted to verify temperatures predicted from the analytical model. Data from this program were compared with calculation over a range of operating conditions, namely, vertical deflection, inflation pressure, yaw angle, and direction of yaw. Generally the analytical model predicted overall trends well and correlated reasonably well with individual measurements at locations throughout the cross section.

Monitoring Cosmic Radiation Risk: Comparisons between Observations and Predictive Codes for Naval Aviation

DTIC Science & Technology

2009-01-01

proton PARMA PHITS -based Analytical Radiation Model in the Atmosphere PCAIRE Predictive Code for Aircrew Radiation Exposure PHITS Particle and...radiation transport code utilized is called PARMA ( PHITS based Analytical Radiation Model in the Atmosphere) [36]. The particle fluxes calculated from the...same dose equivalent coefficient regulations from the ICRP-60 regulations. As a result, the transport codes utilized by EXPACS ( PHITS ) and CARI-6

Monitoring Cosmic Radiation Risk: Comparisons Between Observations and Predictive Codes for Naval Aviation

DTIC Science & Technology

2009-07-05

proton PARMA PHITS -based Analytical Radiation Model in the Atmosphere PCAIRE Predictive Code for Aircrew Radiation Exposure PHITS Particle and Heavy...transport code utilized is called PARMA ( PHITS based Analytical Radiation Model in the Atmosphere) [36]. The particle fluxes calculated from the input...dose equivalent coefficient regulations from the ICRP-60 regulations. As a result, the transport codes utilized by EXPACS ( PHITS ) and CARI-6 (PARMA

The Ethics of Using Learning Analytics to Categorize Students on Risk

ERIC Educational Resources Information Center

Scholes, Vanessa

2016-01-01

There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat "risky" students differently. This paper analyses this practice using…

The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics

ERIC Educational Resources Information Center

Berg, Alan\tM.; Mol, Stefan T.; Kismihók, Gábor; Sclater, Niall

2016-01-01

This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and…

ANALYTICAL MODELING OF ELECTRON BACK-BOMBARDMENT INDUCED CURRENT INCREASE IN UN-GATED THERMIONIC CATHODE RF GUNS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, J. P.; Sun, Y.; Harris, J. R.

In this paper we derive analytical expressions for the output current of an un-gated thermionic cathode RF gun in the presence of back-bombardment heating. We provide a brief overview of back-bombardment theory and discuss comparisons between the analytical back-bombardment predictions and simulation models. We then derive an expression for the output current as a function of the RF repetition rate and discuss relationships between back-bombardment, fieldenhancement, and output current. We discuss in detail the relevant approximations and then provide predictions about how the output current should vary as a function of repetition rate for some given system configurations.

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Saxena, Abhinav; Daigle, Matthew; Goebel, Kai

2013-01-01

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Analytic Formulation and Numerical Implementation of an Acoustic Pressure Gradient Prediction

NASA Technical Reports Server (NTRS)

Lee, Seongkyu; Brentner, Kenneth S.; Farassat, Fereidoun

2007-01-01

The scattering of rotor noise is an area that has received little attention over the years, yet the limited work that has been done has shown that both the directivity and intensity of the acoustic field may be significantly modified by the presence of scattering bodies. One of the inputs needed to compute the scattered acoustic field is the acoustic pressure gradient on a scattering surface. Two new analytical formulations of the acoustic pressure gradient have been developed and implemented in the PSU-WOPWOP rotor noise prediction code. These formulations are presented in this paper. The first formulation is derived by taking the gradient of Farassat's retarded-time Formulation 1A. Although this formulation is relatively simple, it requires numerical time differentiation of the acoustic integrals. In the second formulation, the time differentiation is taken inside the integrals analytically. The acoustic pressure gradient predicted by these new formulations is validated through comparison with the acoustic pressure gradient determined by a purely numerical approach for two model rotors. The agreement between analytic formulations and numerical method is excellent for both stationary and moving observers case.

Ionization Efficiency of Doubly Charged Ions Formed from Polyprotic Acids in Electrospray Negative Mode

NASA Astrophysics Data System (ADS)

Liigand, Piia; Kaupmees, Karl; Kruve, Anneli

2016-07-01

The ability of polyprotic acids to give doubly charged ions in negative mode electrospray was studied and related to physicochemical properties of the acids via linear discriminant analysis (LDA). It was discovered that the compound has to be strongly acidic (low p K a1 and p K a2) and to have high hydrophobicity (log P ow) to become multiply charged. Ability to give multiply charged ions in ESI/MS cannot be directly predicted from the solution phase acidities. Therefore, for the first time, a quantitative model to predict the charge state of the analyte in ESI/MS is proposed and validated for small anions. Also, a model to predict ionization efficiencies of these analytes was developed. Results indicate that acidity of the analyte, its octanol-water partition coefficient, and charge delocalization are important factors that influence ionization efficiencies as well as charge states of the analytes. The pH of the solvent was also found to be an important factor influencing the ionization efficiency of doubly charged ions.

Analytic cognitive style predicts religious and paranormal belief.

PubMed

Pennycook, Gordon; Cheyne, James Allan; Seli, Paul; Koehler, Derek J; Fugelsang, Jonathan A

2012-06-01

An analytic cognitive style denotes a propensity to set aside highly salient intuitions when engaging in problem solving. We assess the hypothesis that an analytic cognitive style is associated with a history of questioning, altering, and rejecting (i.e., unbelieving) supernatural claims, both religious and paranormal. In two studies, we examined associations of God beliefs, religious engagement (attendance at religious services, praying, etc.), conventional religious beliefs (heaven, miracles, etc.) and paranormal beliefs (extrasensory perception, levitation, etc.) with performance measures of cognitive ability and analytic cognitive style. An analytic cognitive style negatively predicted both religious and paranormal beliefs when controlling for cognitive ability as well as religious engagement, sex, age, political ideology, and education. Participants more willing to engage in analytic reasoning were less likely to endorse supernatural beliefs. Further, an association between analytic cognitive style and religious engagement was mediated by religious beliefs, suggesting that an analytic cognitive style negatively affects religious engagement via lower acceptance of conventional religious beliefs. Results for types of God belief indicate that the association between an analytic cognitive style and God beliefs is more nuanced than mere acceptance and rejection, but also includes adopting less conventional God beliefs, such as Pantheism or Deism. Our data are consistent with the idea that two people who share the same cognitive ability, education, political ideology, sex, age and level of religious engagement can acquire very different sets of beliefs about the world if they differ in their propensity to think analytically. Copyright © 2012 Elsevier B.V. All rights reserved.

Shuttle payload bay dynamic environments: Summary and conclusion report for STS flights 1-5 and 9

NASA Technical Reports Server (NTRS)

Oconnell, M.; Garba, J.; Kern, D.

1984-01-01

The vibration, acoustic and low frequency loads data from the first 5 shuttle flights are presented. The engineering analysis of that data is also presented. Vibroacoustic data from STS-9 are also presented because they represent the only data taken on a large payload. Payload dynamic environment predictions developed by the participation of various NASA and industrial centers are presented along with a comparison of analytical loads methodology predictions with flight data, including a brief description of the methodologies employed in developing those predictions for payloads. The review of prediction methodologies illustrates how different centers have approached the problems of developing shuttle dynamic environmental predictions and criteria. Ongoing research activities related to the shuttle dynamic environments are also described. Analytical software recently developed for the prediction of payload acoustic and vibration environments are also described.

Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

NASA Astrophysics Data System (ADS)

Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

2015-03-01

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

Prediction of light aircraft interior noise

NASA Technical Reports Server (NTRS)

Howlett, J. T.; Morales, D. A.

1976-01-01

At the present time, predictions of aircraft interior noise depend heavily on empirical correction factors derived from previous flight measurements. However, to design for acceptable interior noise levels and to optimize acoustic treatments, analytical techniques which do not depend on empirical data are needed. This paper describes a computerized interior noise prediction method for light aircraft. An existing analytical program (developed for commercial jets by Cockburn and Jolly in 1968) forms the basis of some modal analysis work which is described. The accuracy of this modal analysis technique for predicting low-frequency coupled acoustic-structural natural frequencies is discussed along with trends indicating the effects of varying parameters such as fuselage length and diameter, structural stiffness, and interior acoustic absorption.

Temporal fluctuations after a quantum quench: Many-particle dephasing

NASA Astrophysics Data System (ADS)

Marquardt, Florian; Kiendl, Thomas

After a quantum quench, the expectation values of observables continue to fluctuate in time. In the thermodynamic limit, one expects such fluctuations to decrease to zero, in order for standard statistical physics to hold. However, it is a challenge to determine analytically how the fluctuations decay as a function of system size. So far, there have been analytical predictions for integrable models (which are, naturally, somewhat special), analytical bounds for arbitrary systems, and numerical results for moderate-size systems. We have discovered a dynamical regime where the decrease of fluctuations is driven by many-particle dephasing, instead of a redistribution of occupation numbers. On the basis of this insight, we are able to provide exact analytical expressions for a model with weak integrability breaking (transverse Ising chain with additional terms). These predictions explicitly show how fluctuations are exponentially suppressed with system size.

An Analytical Model for Two-Order Asperity Degradation of Rock Joints Under Constant Normal Stiffness Conditions

NASA Astrophysics Data System (ADS)

Li, Yingchun; Wu, Wei; Li, Bo

2018-05-01

Jointed rock masses during underground excavation are commonly located under the constant normal stiffness (CNS) condition. This paper presents an analytical formulation to predict the shear behaviour of rough rock joints under the CNS condition. The dilatancy and deterioration of two-order asperities are quantified by considering the variation of normal stress. We separately consider the dilation angles of waviness and unevenness, which decrease to zero as the normal stress approaches the transitional stress. The sinusoidal function naturally yields the decay of dilation angle as a function of relative normal stress. We assume that the magnitude of transitional stress is proportionate to the square root of asperity geometric area. The comparison between the analytical prediction and experimental data shows the reliability of the analytical model. All the parameters involved in the analytical model possess explicit physical meanings and are measurable from laboratory tests. The proposed model is potentially practicable for assessing the stability of underground structures at various field scales.

Decisions through data: analytics in healthcare.

PubMed

Wills, Mary J

2014-01-01

The amount of data in healthcare is increasing at an astonishing rate. However, in general, the industry has not deployed the level of data management and analysis necessary to make use of those data. As a result, healthcare executives face the risk of being overwhelmed by a flood of unusable data. In this essay I argue that, in order to extract actionable information, leaders must take advantage of the promise of data analytics. Small data, predictive modeling expansion, and real-time analytics are three forms of data analytics. On the basis of my analysis for this study, I recommend all three for adoption. Recognizing the uniqueness of each organization's situation, I also suggest that practices, hospitals, and healthcare systems examine small data and conduct real-time analytics and that large-scale organizations managing populations of patients adopt predictive modeling. I found that all three solutions assist in the collection, management, and analysis of raw data to improve the quality of care and decrease costs.

Variations on Debris Disks. IV. An Improved Analytical Model for Collisional Cascades

NASA Astrophysics Data System (ADS)

Kenyon, Scott J.; Bromley, Benjamin C.

2017-04-01

We derive a new analytical model for the evolution of a collisional cascade in a thin annulus around a single central star. In this model, r max the size of the largest object changes with time, {r}\\max \\propto {t}-γ , with γ ≈ 0.1-0.2. Compared to standard models where r max is constant in time, this evolution results in a more rapid decline of M d , the total mass of solids in the annulus, and L d , the luminosity of small particles in the annulus: {M}d\\propto {t}-(γ +1) and {L}d\\propto {t}-(γ /2+1). We demonstrate that the analytical model provides an excellent match to a comprehensive suite of numerical coagulation simulations for annuli at 1 au and at 25 au. If the evolution of real debris disks follows the predictions of the analytical or numerical models, the observed luminosities for evolved stars require up to a factor of two more mass than predicted by previous analytical models.

Analytic cognitive style, not delusional ideation, predicts data gathering in a large beads task study.

PubMed

Ross, Robert M; Pennycook, Gordon; McKay, Ryan; Gervais, Will M; Langdon, Robyn; Coltheart, Max

2016-07-01

It has been proposed that deluded and delusion-prone individuals gather less evidence before forming beliefs than those who are not deluded or delusion-prone. The primary source of evidence for this "jumping to conclusions" (JTC) bias is provided by research that utilises the "beads task" data-gathering paradigm. However, the cognitive mechanisms subserving data gathering in this task are poorly understood. In the largest published beads task study to date (n = 558), we examined data gathering in the context of influential dual-process theories of reasoning. Analytic cognitive style (the willingness or disposition to critically evaluate outputs from intuitive processing and engage in effortful analytic processing) predicted data gathering in a non-clinical sample, but delusional ideation did not. The relationship between data gathering and analytic cognitive style suggests that dual-process theories of reasoning can contribute to our understanding of the beads task. It is not clear why delusional ideation was not found to be associated with data gathering or analytic cognitive style.

Analytical prediction of the interior noise for cylindrical models of aircraft fuselages for prescribed exterior noise fields. Phase 1: Development and validation of preliminary analytical models

NASA Technical Reports Server (NTRS)

Pope, L. D.; Rennison, D. C.; Wilby, E. G.

1980-01-01

The basic theoretical work required to understand sound transmission into an enclosed space (that is, one closed by the transmitting structure) is developed for random pressure fields and for harmonic (tonal) excitation. The analysis is used to predict the noise reducton of unpressurized unstiffened cylinder, and also the interior response of the cylinder given a tonal (plane wave) excitation. Predictions and measurements are compared and the transmission is analyzed. In addition, results for tonal (harmonic) mechanical excitation are considered.

Progressive damage, fracture predictions and post mortem correlations for fiber composites

NASA Technical Reports Server (NTRS)

1985-01-01

Lewis Research Center is involved in the development of computational mechanics methods for predicting the structural behavior and response of composite structures. In conjunction with the analytical methods development, experimental programs including post failure examination are conducted to study various factors affecting composite fracture such as laminate thickness effects, ply configuration, and notch sensitivity. Results indicate that the analytical capabilities incorporated in the CODSTRAN computer code are effective in predicting the progressive damage and fracture of composite structures. In addition, the results being generated are establishing a data base which will aid in the characterization of composite fracture.

Composite Repairs of Cracked Metallic Airframe Structures

DTIC Science & Technology

1993-05-01

painting of the surface of composites. Therefore, repairs on external surfaces of aircraft should be painted prior to service. 30 2. ANALITICAL AND...tends to decrease the ’apparent’ stress intensity factor. These factors have to be taken into account when comparing the analytical predictions with the...analytical predictions . The fatigue crack growth data for one of the specimens appears in Figure 2-46Zhe ’Inferred’ stress-intensity factor [from the

Mated vertical ground vibration test

NASA Technical Reports Server (NTRS)

Ivey, E. W.

1980-01-01

The Mated Vertical Ground Vibration Test (MVGVT) was considered to provide an experimental base in the form of structural dynamic characteristics for the shuttle vehicle. This data base was used in developing high confidence analytical models for the prediction and design of loads, pogo controls, and flutter criteria under various payloads and operational missions. The MVGVT boost and launch program evolution, test configurations, and their suspensions are described. Test results are compared with predicted analytical results.

Prediction of thermal cycling induced matrix cracking

NASA Technical Reports Server (NTRS)

Mcmanus, Hugh L.

1992-01-01

Thermal fatigue has been observed to cause matrix cracking in laminated composite materials. A method is presented to predict transverse matrix cracks in composite laminates subjected to cyclic thermal load. Shear lag stress approximations and a simple energy-based fracture criteria are used to predict crack densities as a function of temperature. Prediction of crack densities as a function of thermal cycling is accomplished by assuming that fatigue degrades the material's inherent resistance to cracking. The method is implemented as a computer program. A simple experiment provides data on progressive cracking of a laminate with decreasing temperature. Existing data on thermal fatigue is also used. Correlations of the analytical predictions to the data are very good. A parametric study using the analytical method is presented which provides insight into material behavior under cyclical thermal loads.

Ares I-X Upper Stage Simulator Compartment Pressure Comparisons During Ascent

NASA Technical Reports Server (NTRS)

Downs. William J.; Kirchner, Robert D.; McLachlan, Blair G.; Hand, Lawrence A.; Nelson, Stuart L.

2011-01-01

Predictions of internal compartment pressures are necessary in the design of interstage regions, systems tunnels, and protuberance covers of launch vehicles to assess potential burst and crush loading of the structure. History has proven that unexpected differential pressure loads can lead to catastrophic failure. Pressures measured in the Upper Stage Simulator (USS) compartment of Ares I-X during flight were compared to post-flight analytical predictions using the CHCHVENT chamber-to-chamber venting analysis computer program. The measured pressures were enveloped by the analytical predictions for most of the first minute of flight but were outside of the predictions thereafter. This paper summarizes the venting system for the USS, discusses the probable reasons for the discrepancies between the measured and predicted pressures, and provides recommendations for future flight vehicles.

Analytical Methodology for Predicting the Onset of Widespread Fatigue Damage in Fuselage Structure

NASA Technical Reports Server (NTRS)

Harris, Charles E.; Newman, James C., Jr.; Piascik, Robert S.; Starnes, James H., Jr.

1996-01-01

NASA has developed a comprehensive analytical methodology for predicting the onset of widespread fatigue damage in fuselage structure. The determination of the number of flights and operational hours of aircraft service life that are related to the onset of widespread fatigue damage includes analyses for crack initiation, fatigue crack growth, and residual strength. Therefore, the computational capability required to predict analytically the onset of widespread fatigue damage must be able to represent a wide range of crack sizes from the material (microscale) level to the global structural-scale level. NASA studies indicate that the fatigue crack behavior in aircraft structure can be represented conveniently by the following three analysis scales: small three-dimensional cracks at the microscale level, through-the-thickness two-dimensional cracks at the local structural level, and long cracks at the global structural level. The computational requirements for each of these three analysis scales are described in this paper.

Override the controversy: Analytic thinking predicts endorsement of evolution.

PubMed

Gervais, Will M

2015-09-01

Despite overwhelming scientific consensus, popular opinions regarding evolution are starkly divided. In the USA, for example, nearly one in three adults espouse a literal and recent divine creation account of human origins. Plausibly, resistance to scientific conclusions regarding the origins of species-like much resistance to other scientific conclusions (Bloom & Weisberg, 2007)-gains support from reliably developing intuitions. Intuitions about essentialism, teleology, agency, and order may combine to make creationism potentially more cognitively attractive than evolutionary concepts. However, dual process approaches to cognition recognize that people can often analytically override their intuitions. Two large studies (total N=1324) found consistent evidence that a tendency to engage analytic thinking predicted endorsement of evolution, even controlling for relevant demographic, attitudinal, and religious variables. Meanwhile, exposure to religion predicted reduced endorsement of evolution. Cognitive style is one factor among many affecting opinions on the origin of species. Copyright © 2015 Elsevier B.V. All rights reserved.

Trailing Edge Noise Prediction Based on a New Acoustic Formulation

NASA Technical Reports Server (NTRS)

Casper, J.; Farassat, F.

2002-01-01

A new analytic result in acoustics called 'Formulation 1B,' proposed by Farassat, is used to compute broadband trailing edge noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Williams-Hawkings equation with the loading source term, and has been shown in previous research to provide time domain predictions of broadband noise that are in excellent agreement with experiment. Furthermore, this formulation lends itself readily to rotating reference frames and statistical analysis of broadband trailing edge noise. Formulation 1B is used to calculate the far field noise radiated from the trailing edge of a NACA 0012 airfoil in low Mach number flows, using both analytical and experimental data on the airfoil surface. The results are compared to analytical results and experimental measurements that are available in the literature. Good agreement between predictions and measurements is obtained.

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

PubMed

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.

Predicting Student Success using Analytics in Course Learning Management Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olama, Mohammed M; Thakur, Gautam; McNair, Wade

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems,more » called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.« less

Predicting student success using analytics in course learning management systems

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Thakur, Gautam; McNair, Allen W.; Sukumar, Sreenivas R.

2014-05-01

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students' scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Using Multilingual Analytics to Explore the Usage of a Learning Portal in Developing Countries

ERIC Educational Resources Information Center

Protonotarios, Vassilis; Stoitsis, Giannis; Kastrantas, Kostas; Sanchez-Alonso, Salvador

2013-01-01

Learning analytics is a domain that has been constantly evolving throughout recent years due to the acknowledgement of its importance by those using intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning [1]. Learning analytics may be applied in a…

Determining passive cooling limits in CPV using an analytical thermal model

NASA Astrophysics Data System (ADS)

Gualdi, Federico; Arenas, Osvaldo; Vossier, Alexis; Dollet, Alain; Aimez, Vincent; Arès, Richard

2013-09-01

We propose an original thermal analytical model aiming to predict the practical limits of passive cooling systems for high concentration photovoltaic modules. The analytical model is described and validated by comparison with a commercial 3D finite element model. The limiting performances of flat plate cooling systems in natural convection are then derived and discussed.

University of Missouri-St. Louis: Data-Driven Online Course Design and Effective Practices

ERIC Educational Resources Information Center

Grant, Mary Rose

2012-01-01

Analytics has a significant place in the future of higher education by guiding reform and system change. As this case study has shown, analytics can do more than evaluate what students have done and predict what they will do. Learning analytics can be transformative, altering existing pedagogical processes, research, data management, and…

Modeling and Predicting the Stress Relaxation of Composites with Short and Randomly Oriented Fibers

PubMed Central

Obaid, Numaira; Sain, Mohini

2017-01-01

The addition of short fibers has been experimentally observed to slow the stress relaxation of viscoelastic polymers, producing a change in the relaxation time constant. Our recent study attributed this effect of fibers on stress relaxation behavior to the interfacial shear stress transfer at the fiber-matrix interface. This model explained the effect of fiber addition on stress relaxation without the need to postulate structural changes at the interface. In our previous study, we developed an analytical model for the effect of fully aligned short fibers, and the model predictions were successfully compared to finite element simulations. However, in most industrial applications of short-fiber composites, fibers are not aligned, and hence it is necessary to examine the time dependence of viscoelastic polymers containing randomly oriented short fibers. In this study, we propose an analytical model to predict the stress relaxation behavior of short-fiber composites where the fibers are randomly oriented. The model predictions were compared to results obtained from Monte Carlo finite element simulations, and good agreement between the two was observed. The analytical model provides an excellent tool to accurately predict the stress relaxation behavior of randomly oriented short-fiber composites. PMID:29053601

Modeling and Analysis of Structural Dynamics for a One-Tenth Scale Model NGST Sunshield

NASA Technical Reports Server (NTRS)

Johnston, John; Lienard, Sebastien; Brodeur, Steve (Technical Monitor)

2001-01-01

New modeling and analysis techniques have been developed for predicting the dynamic behavior of the Next Generation Space Telescope (NGST) sunshield. The sunshield consists of multiple layers of pretensioned, thin-film membranes supported by deployable booms. Modeling the structural dynamic behavior of the sunshield is a challenging aspect of the problem due to the effects of membrane wrinkling. A finite element model of the sunshield was developed using an approximate engineering approach, the cable network method, to account for membrane wrinkling effects. Ground testing of a one-tenth scale model of the NGST sunshield were carried out to provide data for validating the analytical model. A series of analyses were performed to predict the behavior of the sunshield under the ground test conditions. Modal analyses were performed to predict the frequencies and mode shapes of the test article and transient response analyses were completed to simulate impulse excitation tests. Comparison was made between analytical predictions and test measurements for the dynamic behavior of the sunshield. In general, the results show good agreement with the analytical model correctly predicting the approximate frequency and mode shapes for the significant structural modes.

Mechanical behavior of regular open-cell porous biomaterials made of diamond lattice unit cells.

PubMed

Ahmadi, S M; Campoli, G; Amin Yavari, S; Sajadi, B; Wauthle, R; Schrooten, J; Weinans, H; Zadpoor, A A

2014-06-01

Cellular structures with highly controlled micro-architectures are promising materials for orthopedic applications that require bone-substituting biomaterials or implants. The availability of additive manufacturing techniques has enabled manufacturing of biomaterials made of one or multiple types of unit cells. The diamond lattice unit cell is one of the relatively new types of unit cells that are used in manufacturing of regular porous biomaterials. As opposed to many other types of unit cells, there is currently no analytical solution that could be used for prediction of the mechanical properties of cellular structures made of the diamond lattice unit cells. In this paper, we present new analytical solutions and closed-form relationships for predicting the elastic modulus, Poisson׳s ratio, critical buckling load, and yield (plateau) stress of cellular structures made of the diamond lattice unit cell. The mechanical properties predicted using the analytical solutions are compared with those obtained using finite element models. A number of solid and porous titanium (Ti6Al4V) specimens were manufactured using selective laser melting. A series of experiments were then performed to determine the mechanical properties of the matrix material and cellular structures. The experimentally measured mechanical properties were compared with those obtained using analytical solutions and finite element (FE) models. It has been shown that, for small apparent density values, the mechanical properties obtained using analytical and numerical solutions are in agreement with each other and with experimental observations. The properties estimated using an analytical solution based on the Euler-Bernoulli theory markedly deviated from experimental results for large apparent density values. The mechanical properties estimated using FE models and another analytical solution based on the Timoshenko beam theory better matched the experimental observations. Copyright © 2014 Elsevier Ltd. All rights reserved.

Analytic model for ultrasound energy receivers and their optimal electric loads II: Experimental validation

NASA Astrophysics Data System (ADS)

Gorostiaga, M.; Wapler, M. C.; Wallrabe, U.

2017-10-01

In this paper, we verify the two optimal electric load concepts based on the zero reflection condition and on the power maximization approach for ultrasound energy receivers. We test a high loss 1-3 composite transducer, and find that the measurements agree very well with the predictions of the analytic model for plate transducers that we have developed previously. Additionally, we also confirm that the power maximization and zero reflection loads are very different when the losses in the receiver are high. Finally, we compare the optimal load predictions by the KLM and the analytic models with frequency dependent attenuation to evaluate the influence of the viscosity.

Qualitative comparison of calculated turbulence responses with wind-tunnel measurements for a DC-10 derivative wing with an active control system

NASA Technical Reports Server (NTRS)

Perry, B., III

1981-01-01

Comparisons are presented analytically predicted and experimental turbulence responses of a wind tunnel model of a DC-10 derivative wing equipped with an active control system. The active control system was designed for the purpose of flutter suppression, but it had additional benefit of alleviating gust loads (wing bending moment) by about 25%. Comparisions of various wing responses are presented for variations in active control system parameters and tunnel speed. The analytical turbulence responses were obtained using DYLOFLEX, a computer program for dynamic loads analyses of flexible airplanes with active controls. In general, the analytical predictions agreed reasonably well with the experimental data.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

An analytical model of leakage neutron equivalent dose for passively-scattered proton radiotherapy and validation with measurements.

PubMed

Schneider, Christopher; Newhauser, Wayne; Farah, Jad

2015-05-18

Exposure to stray neutrons increases the risk of second cancer development after proton therapy. Previously reported analytical models of this exposure were difficult to configure and had not been investigated below 100 MeV proton energy. The purposes of this study were to test an analytical model of neutron equivalent dose per therapeutic absorbed dose  at 75 MeV and to improve the model by reducing the number of configuration parameters and making it continuous in proton energy from 100 to 250 MeV. To develop the analytical model, we used previously published H/D values in water from Monte Carlo simulations of a general-purpose beamline for proton energies from 100 to 250 MeV. We also configured and tested the model on in-air neutron equivalent doses measured for a 75 MeV ocular beamline. Predicted H/D values from the analytical model and Monte Carlo agreed well from 100 to 250 MeV (10% average difference). Predicted H/D values from the analytical model also agreed well with measurements at 75 MeV (15% average difference). The results indicate that analytical models can give fast, reliable calculations of neutron exposure after proton therapy. This ability is absent in treatment planning systems but vital to second cancer risk estimation.

Transition regime analytical solution to gas mass flow rate in a rectangular micro channel

NASA Astrophysics Data System (ADS)

Dadzie, S. Kokou; Dongari, Nishanth

2012-11-01

We present an analytical model predicting the experimentally observed gas mass flow rate in rectangular micro channels over slip and transition regimes without the use of any fitting parameter. Previously, Sone reported a class of pure continuum regime flows that requires terms of Burnett order in constitutive equations of shear stress to be predicted appropriately. The corrective terms to the conventional Navier-Stokes equation were named the ghost effect. We demonstrate in this paper similarity between Sone ghost effect model and newly so-called 'volume diffusion hydrodynamic model'. A generic analytical solution to gas mass flow rate in a rectangular micro channel is then obtained. It is shown that the volume diffusion hydrodynamics allows to accurately predict the gas mass flow rate up to Knudsen number of 5. This can be achieved without necessitating the use of adjustable parameters in boundary conditions or parametric scaling laws for constitutive relations. The present model predicts the non-linear variation of pressure profile along the axial direction and also captures the change in curvature with increase in rarefaction.

Bridging the Gap between Human Judgment and Automated Reasoning in Predictive Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; Riensche, Roderick M.; Unwin, Stephen D.

2010-06-07

Events occur daily that impact the health, security and sustainable growth of our society. If we are to address the challenges that emerge from these events, anticipatory reasoning has to become an everyday activity. Strong advances have been made in using integrated modeling for analysis and decision making. However, a wider impact of predictive analytics is currently hindered by the lack of systematic methods for integrating predictive inferences from computer models with human judgment. In this paper, we present a predictive analytics approach that supports anticipatory analysis and decision-making through a concerted reasoning effort that interleaves human judgment and automatedmore » inferences. We describe a systematic methodology for integrating modeling algorithms within a serious gaming environment in which role-playing by human agents provides updates to model nodes and the ensuing model outcomes in turn influence the behavior of the human players. The approach ensures a strong functional partnership between human players and computer models while maintaining a high degree of independence and greatly facilitating the connection between model and game structures.« less

Tools for studying dry-cured ham processing by using computed tomography.

PubMed

Santos-Garcés, Eva; Muñoz, Israel; Gou, Pere; Sala, Xavier; Fulladosa, Elena

2012-01-11

An accurate knowledge and optimization of dry-cured ham elaboration processes could help to reduce operating costs and maximize product quality. The development of nondestructive tools to characterize chemical parameters such as salt and water contents and a(w) during processing is of special interest. In this paper, predictive models for salt content (R(2) = 0.960 and RMSECV = 0.393), water content (R(2) = 0.912 and RMSECV = 1.751), and a(w) (R(2) = 0.906 and RMSECV = 0.008), which comprise the whole elaboration process, were developed. These predictive models were used to develop analytical tools such as distribution diagrams, line profiles, and regions of interest (ROIs) from the acquired computed tomography (CT) scans. These CT analytical tools provided quantitative information on salt, water, and a(w) in terms of content but also distribution throughout the process. The information obtained was applied to two industrial case studies. The main drawback of the predictive models and CT analytical tools is the disturbance that fat produces in water content and a(w) predictions.

Measurement-based reliability prediction methodology. M.S. Thesis

NASA Technical Reports Server (NTRS)

Linn, Linda Shen

1991-01-01

In the past, analytical and measurement based models were developed to characterize computer system behavior. An open issue is how these models can be used, if at all, for system design improvement. The issue is addressed here. A combined statistical/analytical approach to use measurements from one environment to model the system failure behavior in a new environment is proposed. A comparison of the predicted results with the actual data from the new environment shows a close correspondence.

Cocontraction of pairs of antagonistic muscles: analytical solution for planar static nonlinear optimization approaches.

PubMed

Herzog, W; Binding, P

1993-11-01

It has been stated in the literature that static, nonlinear optimization approaches cannot predict coactivation of pairs of antagonistic muscles; however, numerical solutions of such approaches have predicted coactivation of pairs of one-joint and multijoint antagonists. Analytical support for either finding is not available in the literature for systems containing more than one degree of freedom. The purpose of this study was to investigate analytically the possibility of cocontraction of pairs of antagonistic muscles using a static nonlinear optimization approach for a multidegree-of-freedom, two-dimensional system. Analytical solutions were found using the Karush-Kuhn-Tucker conditions, which were necessary and sufficient for optimality in this problem. The results show that cocontraction of pairs of one-joint antagonistic muscles is not possible, whereas cocontraction of pairs of multijoint antagonists is. These findings suggest that cocontraction of pairs of antagonistic muscles may be an "efficient" way to accomplish many movement tasks.

The rate of bubble growth in a superheated liquid in pool boiling

NASA Astrophysics Data System (ADS)

Abdollahi, Mohammad Reza; Jafarian, Mehdi; Jamialahmadi, Mohammad

2017-12-01

A semi-empirical model for the estimation of the rate of bubble growth in nucleate pool boiling is presented, considering a new equation to estimate the temperature history of the bubble in the bulk of liquid. The conservation equations of energy, mass and momentum have been firstly derived and solved analytically. The present analytical model of the bubble growth predicts that the radius of the bubble grows as a function of √{t}.{\\operatorname{erf}}( N√{t}) , while so far the bubble growth rate has been mainly correlated to √{t} in the previous studies. In the next step, the analytical solutions were used to develop a new semi-empirical equation. To achieve this, firstly the analytical solution were non-dimensionalised and then the experimental data, available in the literature, were applied to tune the dimensionless coefficients appeared in the dimensionless equation. Finally, the reliability of the proposed semi-empirical model was assessed through comparison of the model predictions with the available experimental data in the literature, which were not applied in the tuning of the dimensionless parameters of the model. The comparison of the model predictions with other proposed models in the literature was also performed. These comparisons show that this model enables more accurate predictions than previously proposed models with a deviation of less than 10% in a wide range of operating conditions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Simonetto, Andrea

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are establishedmore » to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.« less

Stochastic modeling of macrodispersion in unsaturated heterogeneous porous media. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yeh, T.C.J.

1995-02-01

Spatial heterogeneity of geologic media leads to uncertainty in predicting both flow and transport in the vadose zone. In this work an efficient and flexible, combined analytical-numerical Monte Carlo approach is developed for the analysis of steady-state flow and transient transport processes in highly heterogeneous, variably saturated porous media. The approach is also used for the investigation of the validity of linear, first order analytical stochastic models. With the Monte Carlo analysis accurate estimates of the ensemble conductivity, head, velocity, and concentration mean and covariance are obtained; the statistical moments describing displacement of solute plumes, solute breakthrough at a compliancemore » surface, and time of first exceedance of a given solute flux level are analyzed; and the cumulative probability density functions for solute flux across a compliance surface are investigated. The results of the Monte Carlo analysis show that for very heterogeneous flow fields, and particularly in anisotropic soils, the linearized, analytical predictions of soil water tension and soil moisture flux become erroneous. Analytical, linearized Lagrangian transport models also overestimate both the longitudinal and the transverse spreading of the mean solute plume in very heterogeneous soils and in dry soils. A combined analytical-numerical conditional simulation algorithm is also developed to estimate the impact of in-situ soil hydraulic measurements on reducing the uncertainty of concentration and solute flux predictions.« less

New method for probabilistic traffic demand predictions for en route sectors based on uncertain predictions of individual flight events.

DOT National Transportation Integrated Search

2011-06-14

This paper presents a novel analytical approach to and techniques for translating characteristics of uncertainty in predicting sector entry times and times in sector for individual flights into characteristics of uncertainty in predicting one-minute ...

Comparison between numeric and approximate analytic solutions for the prediction of soil metal uptake by roots. Example of cadmium.

PubMed

Schneider, André; Lin, Zhongbing; Sterckeman, Thibault; Nguyen, Christophe

2018-04-01

The dissociation of metal complexes in the soil solution can increase the availability of metals for root uptake. When it is accounted for in models of bioavailability of soil metals, the number of partial differential equations (PDEs) increases and the computation time to numerically solve these equations may be problematic when a large number of simulations are required, for example for sensitivity analyses or when considering root architecture. This work presents analytical solutions for the set of PDEs describing the bioavailability of soil metals including the kinetics of complexation for three scenarios where the metal complex in solution was fully inert, fully labile, or partially labile. The analytical solutions are only valid i) at steady-state when the PDEs become ordinary differential equations, the transient phase being not covered, ii) when diffusion is the major mechanism of transport and therefore, when convection is negligible, iii) when there is no between-root competition. The formulation of the analytical solutions is for cylindrical geometry but the solutions rely on the spread of the depletion profile around the root, which was modelled assuming a planar geometry. The analytical solutions were evaluated by comparison with the corresponding PDEs for cadmium in the case of the French agricultural soils. Provided that convection was much lower than diffusion (Péclet's number<0.02), the cumulative uptakes calculated from the analytic solutions were in very good agreement with those calculated from the PDEs, even in the case of a partially labile complex. The analytic solutions can be used instead of the PDEs to predict root uptake of metals. The analytic solutions were also used to build an indicator of the contribution of a complex to the uptake of the metal by roots, which can be helpful to predict the effect of soluble organic matter on the bioavailability of soil metals. Copyright © 2017 Elsevier B.V. All rights reserved.

Turbofan forced mixer lobe flow modeling. 1: Experimental and analytical assessment

NASA Technical Reports Server (NTRS)

Barber, T.; Paterson, R. W.; Skebe, S. A.

1988-01-01

A joint analytical and experimental investigation of three-dimensional flowfield development within the lobe region of turbofan forced mixer nozzles is described. The objective was to develop a method for predicting the lobe exit flowfield. In the analytical approach, a linearized inviscid aerodynamical theory was used for representing the axial and secondary flows within the three-dimensional convoluted mixer lobes and three-dimensional boundary layer analysis was applied thereafter to account for viscous effects. The experimental phase of the program employed three planar mixer lobe models having different waveform shapes and lobe heights for which detailed measurements were made of the three-dimensional velocity field and total pressure field at the lobe exit plane. Velocity data was obtained using Laser Doppler Velocimetry (LDV) and total pressure probing and hot wire anemometry were employed to define exit plane total pressure and boundary layer development. Comparison of data and analysis was performed to assess analytical model prediction accuracy. As a result of this study a planar mixed geometry analysis was developed. A principal conclusion is that the global mixer lobe flowfield is inviscid and can be predicted from an inviscid analysis and Kutta condition.

Empirical and semi-analytical models for predicting peak outflows caused by embankment dam failures

NASA Astrophysics Data System (ADS)

Wang, Bo; Chen, Yunliang; Wu, Chao; Peng, Yong; Song, Jiajun; Liu, Wenjun; Liu, Xin

2018-07-01

Prediction of peak discharge of floods has attracted great attention for researchers and engineers. In present study, nine typical nonlinear mathematical models are established based on database of 40 historical dam failures. The first eight models that were developed with a series of regression analyses are purely empirical, while the last one is a semi-analytical approach that was derived from an analytical solution of dam-break floods in a trapezoidal channel. Water depth above breach invert (Hw), volume of water stored above breach invert (Vw), embankment length (El), and average embankment width (Ew) are used as independent variables to develop empirical formulas of estimating the peak outflow from breached embankment dams. It is indicated from the multiple regression analysis that a function using the former two variables (i.e., Hw and Vw) produce considerably more accurate results than that using latter two variables (i.e., El and Ew). It is shown that the semi-analytical approach works best in terms of both prediction accuracy and uncertainty, and the established empirical models produce considerably reasonable results except the model only using El. Moreover, present models have been compared with other models available in literature for estimating peak discharge.

Analysis of uncertainties in turbine metal temperature predictions

NASA Technical Reports Server (NTRS)

Stepka, F. S.

1980-01-01

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Analytical methods to predict liquid congealing in ram air heat exchangers during cold operation

NASA Astrophysics Data System (ADS)

Coleman, Kenneth; Kosson, Robert

1989-07-01

Ram air heat exchangers used to cool liquids such as lube oils or Ethylene-Glycol/water solutions can be subject to congealing in very cold ambients, resulting in a loss of cooling capability. Two-dimensional, transient analytical models have been developed to explore this phenomenon with both continuous and staggered fin cores. Staggered fin predictions are compared to flight test data from the E-2C Allison T56 engine lube oil system during winter conditions. For simpler calculations, a viscosity ratio correction was introduced and found to provide reasonable cold ambient performance predictions for the staggered fin core, using a one-dimensional approach.

Big Data Analytics for a Smart Green Infrastructure Strategy

NASA Astrophysics Data System (ADS)

Barrile, Vincenzo; Bonfa, Stefano; Bilotta, Giuliana

2017-08-01

As well known, Big Data is a term for data sets so large or complex that traditional data processing applications aren’t sufficient to process them. The term “Big Data” is referred to using predictive analytics. It is often related to user behavior analytics, or other advanced data analytics methods which from data extract value, and rarely to a particular size of data set. This is especially true for the huge amount of Earth Observation data that satellites constantly orbiting the earth daily transmit.

Design and analysis of tubular permanent magnet linear generator for small-scale wave energy converter

NASA Astrophysics Data System (ADS)

Kim, Jeong-Man; Koo, Min-Mo; Jeong, Jae-Hoon; Hong, Keyyong; Cho, Il-Hyoung; Choi, Jang-Young

2017-05-01

This paper reports the design and analysis of a tubular permanent magnet linear generator (TPMLG) for a small-scale wave-energy converter. The analytical field computation is performed by applying a magnetic vector potential and a 2-D analytical model to determine design parameters. Based on analytical solutions, parametric analysis is performed to meet the design specifications of a wave-energy converter (WEC). Then, 2-D FEA is employed to validate the analytical method. Finally, the experimental result confirms the predictions of the analytical and finite element analysis (FEA) methods under regular and irregular wave conditions.

Assessment of analytical techniques for predicting solid propellant exhaust plumes and plume impingement environments

NASA Technical Reports Server (NTRS)

Tevepaugh, J. A.; Smith, S. D.; Penny, M. M.

1977-01-01

An analysis of experimental nozzle, exhaust plume, and exhaust plume impingement data is presented. The data were obtained for subscale solid propellant motors with propellant Al loadings of 2, 10 and 15% exhausting to simulated altitudes of 50,000, 100,000 and 112,000 ft. Analytical predictions were made using a fully coupled two-phase method of characteristics numerical solution and a technique for defining thermal and pressure environments experienced by bodies immersed in two-phase exhaust plumes.

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Matsui, Shigeyuki; Michimae, Hirofumi; Rondeau, Virginie

2017-01-01

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

Effect of vibration on retention characteristics of screen acquisition systems. [for surface tension propellant acquisition

NASA Technical Reports Server (NTRS)

Tegart, J. R.; Aydelott, J. C.

1978-01-01

The design of surface tension propellant acquisition systems using fine-mesh screen must take into account all factors that influence the liquid pressure differentials within the system. One of those factors is spacecraft vibration. Analytical models to predict the effects of vibration have been developed. A test program to verify the analytical models and to allow a comparative evaluation of the parameters influencing the response to vibration was performed. Screen specimens were tested under conditions simulating the operation of an acquisition system, considering the effects of such parameters as screen orientation and configuration, screen support method, screen mesh, liquid flow and liquid properties. An analytical model, based on empirical coefficients, was most successful in predicting the effects of vibration.

Thermal-structural modeling of polymer Bragg grating waveguides illuminated by a light emitting diode.

PubMed

Joon Kim, Kyoung; Bar-Cohen, Avram; Han, Bongtae

2012-02-20

This study reports both analytical and numerical thermal-structural models of polymer Bragg grating (PBG) waveguides illuminated by a light emitting diode (LED). A polymethyl methacrylate (PMMA) Bragg grating (BG) waveguide is chosen as an analysis vehicle to explore parametric effects of incident optical powers and substrate materials on the thermal-structural behavior of the BG. Analytical models are verified by comparing analytically predicted average excess temperatures, and thermally induced axial strains and stresses with numerical predictions. A parametric study demonstrates that the PMMA substrate induces more adverse effects, such as higher excess temperatures, complex axial temperature profiles, and greater and more complicated thermally induced strains in the BG compared with the Si substrate. © 2012 Optical Society of America

Quantum decay model with exact explicit analytical solution

NASA Astrophysics Data System (ADS)

Marchewka, Avi; Granot, Er'El

2009-01-01

A simple decay model is introduced. The model comprises a point potential well, which experiences an abrupt change. Due to the temporal variation, the initial quantum state can either escape from the well or stay localized as a new bound state. The model allows for an exact analytical solution while having the necessary features of a decay process. The results show that the decay is never exponential, as classical dynamics predicts. Moreover, at short times the decay has a fractional power law, which differs from perturbation quantum method predictions. At long times the decay includes oscillations with an envelope that decays algebraically. This is a model where the final state can be either continuous or localized, and that has an exact analytical solution.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

PubMed Central

Poucke, Sven Van; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; Deyne, Cathy De

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research. PMID:26731286

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.

PubMed

Van Poucke, Sven; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; De Deyne, Cathy

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

Durability predictions of adhesively bonded composite structures using accelerated characterization methods

NASA Technical Reports Server (NTRS)

Brinson, H. F.

1985-01-01

The utilization of adhesive bonding for composite structures is briefly assessed. The need for a method to determine damage initiation and propagation for such joints is outlined. Methods currently in use to analyze both adhesive joints and fiber reinforced plastics is mentioned and it is indicated that all methods require the input of the mechanical properties of the polymeric adhesive and composite matrix material. The mechanical properties of polymers are indicated to be viscoelastic and sensitive to environmental effects. A method to analytically characterize environmentally dependent linear and nonlinear viscoelastic properties is given. It is indicated that the methodology can be used to extrapolate short term data to long term design lifetimes. That is, the method can be used for long term durability predictions. Experimental results for near adhesive resins, polymers used as composite matrices and unidirectional composite laminates is given. The data is fitted well with the analytical durability methodology. Finally, suggestions are outlined for the development of an analytical methodology for the durability predictions of adhesively bonded composite structures.

Prediction of pressure and flow transients in a gaseous bipropellant reaction control rocket engine

NASA Technical Reports Server (NTRS)

Markowsky, J. J.; Mcmanus, H. N., Jr.

1974-01-01

An analytic model is developed to predict pressure and flow transients in a gaseous hydrogen-oxygen reaction control rocket engine feed system. The one-dimensional equations of momentum and continuity are reduced by the method of characteristics from partial derivatives to a set of total derivatives which describe the state properties along the feedline. System components, e.g., valves, manifolds, and injectors are represented by pseudo steady-state relations at discrete junctions in the system. Solutions were effected by a FORTRAN IV program on an IBM 360/65. The results indicate the relative effect of manifold volume, combustion lag time, feedline pressure fluctuations, propellant temperature, and feedline length on the chamber pressure transient. The analytical combustion model is verified by good correlation between predicted and observed chamber pressure transients. The developed model enables a rocket designer to vary the design parameters analytically to obtain stable combustion for a particular mode of operation which is prescribed by mission objectives.

The effect of analytic and experiential modes of thought on moral judgment.

PubMed

Kvaran, Trevor; Nichols, Shaun; Sanfey, Alan

2013-01-01

According to dual-process theories, moral judgments are the result of two competing processes: a fast, automatic, affect-driven process and a slow, deliberative, reason-based process. Accordingly, these models make clear and testable predictions about the influence of each system. Although a small number of studies have attempted to examine each process independently in the context of moral judgment, no study has yet tried to experimentally manipulate both processes within a single study. In this chapter, a well-established "mode-of-thought" priming technique was used to place participants in either an experiential/emotional or analytic mode while completing a task in which participants provide judgments about a series of moral dilemmas. We predicted that individuals primed analytically would make more utilitarian responses than control participants, while emotional priming would lead to less utilitarian responses. Support was found for both of these predictions. Implications of these findings for dual-process theories of moral judgment will be discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

PubMed

Dinov, Ivo D; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W; Price, Nathan D; Van Horn, John D; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M; Dauer, William; Toga, Arthur W

2016-01-01

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Temporal Learning Analytics for Adaptive Assessment

ERIC Educational Resources Information Center

Papamitsiou, Zacharoula; Economides, Anastasios A.

2014-01-01

Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine…

Shelf-life dating of shelf-stable strawberry juice based on survival analysis of consumer acceptance information.

PubMed

Buvé, Carolien; Van Bedts, Tine; Haenen, Annelien; Kebede, Biniam; Braekers, Roel; Hendrickx, Marc; Van Loey, Ann; Grauwet, Tara

2018-07-01

Accurate shelf-life dating of food products is crucial for consumers and industries. Therefore, in this study we applied a science-based approach for shelf-life assessment, including accelerated shelf-life testing (ASLT), acceptability testing and the screening of analytical attributes for fast shelf-life predictions. Shelf-stable strawberry juice was selected as a case study. Ambient storage (20 °C) had no effect on the aroma-based acceptance of strawberry juice. The colour-based acceptability decreased during storage under ambient and accelerated (28-42 °C) conditions. The application of survival analysis showed that the colour-based shelf-life was reached in the early stages of storage (≤11 weeks) and that the shelf-life was shortened at higher temperatures. None of the selected attributes (a * and ΔE * value, anthocyanin and ascorbic acid content) is an ideal analytical marker for shelf-life predictions in the investigated temperature range (20-42 °C). Nevertheless, an overall analytical cut-off value over the whole temperature range can be selected. Colour changes of strawberry juice during storage are shelf-life limiting. Combining ASLT with acceptability testing allowed to gain faster insight into the change in colour-based acceptability and to perform shelf-life predictions relying on scientific data. An analytical marker is a convenient tool for shelf-life predictions in the context of ASLT. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Hybrid perturbation methods based on statistical time series models

NASA Astrophysics Data System (ADS)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.

PubMed

Luo, Yuan; Szolovits, Peter; Dighe, Anand S; Baron, Jason M

2018-06-01

A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms require complete datasets (no missing data), clinical analytic approaches often entail an imputation procedure to "fill in" missing data. However, although most clinical datasets contain a temporal component, most commonly used imputation methods do not adequately accommodate longitudinal time-based data. We sought to develop a new imputation algorithm, 3-dimensional multiple imputation with chained equations (3D-MICE), that can perform accurate imputation of missing clinical time series data. We extracted clinical laboratory test results for 13 commonly measured analytes (clinical laboratory tests). We imputed missing test results for the 13 analytes using 3 imputation methods: multiple imputation with chained equations (MICE), Gaussian process (GP), and 3D-MICE. 3D-MICE utilizes both MICE and GP imputation to integrate cross-sectional and longitudinal information. To evaluate imputation method performance, we randomly masked selected test results and imputed these masked results alongside results missing from our original data. We compared predicted results to measured results for masked data points. 3D-MICE performed significantly better than MICE and GP-based imputation in a composite of all 13 analytes, predicting missing results with a normalized root-mean-square error of 0.342, compared to 0.373 for MICE alone and 0.358 for GP alone. 3D-MICE offers a novel and practical approach to imputing clinical laboratory time series data. 3D-MICE may provide an additional tool for use as a foundation in clinical predictive analytics and intelligent clinical decision support.

WetDATA Hub: Democratizing Access to Water Data to Accelerate Innovation through Data Visualization, Predictive Analytics and Artificial Intelligence Applications

NASA Astrophysics Data System (ADS)

Sarni, W.

2017-12-01

Water scarcity and poor quality impacts economic development, business growth, and social well-being. Water has become, in our generation, the foremost critical local, regional, and global issue of our time. Despite these needs, there is no water hub or water technology accelerator solely dedicated to water data and tools. There is a need by the public and private sectors for vastly improved data management and visualization tools. This is the WetDATA opportunity - to develop a water data tech hub dedicated to water data acquisition, analytics, and visualization tools for informed policy and business decisions. WetDATA's tools will help incubate disruptive water data technologies and accelerate adoption of current water data solutions. WetDATA is a Colorado-based (501c3), global hub for water data analytics and technology innovation. WetDATA's vision is to be a global leader in water information, data technology innovation and collaborate with other US and global water technology hubs. ROADMAP * Portal (www.wetdata.org) to provide stakeholders with tools/resources to understand related water risks. * The initial activities will provide education, awareness and tools to stakeholders to support the implementation of the Colorado State Water Plan. * Leverage the Western States Water Council Water Data Exchange database. * Development of visualization, predictive analytics and AI tools to engage with stakeholders and provide actionable data and information. TOOLS Education: Provide information on water issues and risks at the local, state, national and global scale. Visualizations: Development of data analytics and visualization tools based upon the 2030 Water Resources Group methodology to support the implementation of the Colorado State Water Plan. Predictive Analytics: Accessing publically available water databases and using machine learning to develop water availability forecasting tools, and time lapse images to support city / urban planning.

Circular Functions Based Comprehensive Analysis of Plastic Creep Deformations in the Fiber Reinforced Composites

NASA Astrophysics Data System (ADS)

Monfared, Vahid

2016-12-01

Analytically based model is presented for behavioral analysis of the plastic deformations in the reinforced materials using the circular (trigonometric) functions. The analytical method is proposed to predict creep behavior of the fibrous composites based on basic and constitutive equations under a tensile axial stress. New insight of the work is to predict some important behaviors of the creeping matrix. In the present model, the prediction of the behaviors is simpler than the available methods. Principal creep strain rate behaviors are very noteworthy for designing the fibrous composites in the creeping composites. Analysis of the mentioned parameter behavior in the reinforced materials is necessary to analyze failure, fracture, and fatigue studies in the creep of the short fiber composites. Shuttles, spaceships, turbine blades and discs, and nozzle guide vanes are commonly subjected to the creep effects. Also, predicting the creep behavior is significant to design the optoelectronic and photonic advanced composites with optical fibers. As a result, the uniform behavior with constant gradient is seen in the principal creep strain rate behavior, and also creep rupture may happen at the fiber end. Finally, good agreements are found through comparing the obtained analytical and FEM results.

Blade Tip Rubbing Stress Prediction

NASA Technical Reports Server (NTRS)

Davis, Gary A.; Clough, Ray C.

1991-01-01

An analytical model was constructed to predict the magnitude of stresses produced by rubbing a turbine blade against its tip seal. This model used a linearized approach to the problem, after a parametric study, found that the nonlinear effects were of insignificant magnitude. The important input parameters to the model were: the arc through which rubbing occurs, the turbine rotor speed, normal force exerted on the blade, and the rubbing coefficient of friction. Since it is not possible to exactly specify some of these parameters, values were entered into the model which bracket likely values. The form of the forcing function was another variable which was impossible to specify precisely, but the assumption of a half-sine wave with a period equal to the duration of the rub was taken as a realistic assumption. The analytical model predicted resonances between harmonics of the forcing function decomposition and known harmonics of the blade. Thus, it seemed probable that blade tip rubbing could be at least a contributor to the blade-cracking phenomenon. A full-scale, full-speed test conducted on the space shuttle main engine high pressure fuel turbopump Whirligig tester was conducted at speeds between 33,000 and 28,000 RPM to confirm analytical predictions.

Viscoelastic behavior and lifetime (durability) predictions. [for laminated fiber reinforced plastics

NASA Technical Reports Server (NTRS)

Brinson, R. F.

1985-01-01

A method for lifetime or durability predictions for laminated fiber reinforced plastics is given. The procedure is similar to but not the same as the well known time-temperature-superposition principle for polymers. The method is better described as an analytical adaptation of time-stress-super-position methods. The analytical constitutive modeling is based upon a nonlinear viscoelastic constitutive model developed by Schapery. Time dependent failure models are discussed and are related to the constitutive models. Finally, results of an incremental lamination analysis using the constitutive and failure model are compared to experimental results. Favorable results between theory and predictions are presented using data from creep tests of about two months duration.

HART-II: Prediction of Blade-Vortex Interaction Loading

DTIC Science & Technology

2003-09-01

14:30 (2) Improvement of DLR Rotor Aero- acoustic Code ( APSIM ) and its Valida- tion with Analytic Solution J. Yin, J. Delfs (5...of DLR Rotor Aero- acoustic Code ( APSIM ) and its Valida- tion with Analytic Solution J. Yin, J. Delfs (5) Aeroelastic Stability Analysis of...of DLR Rotor Aero- acoustic Code ( APSIM ) and its Valida- tion with Analytic Solution J. Yin, J. Delfs (5) Aeroelastic Stability Analysis of

Spectral multivariate calibration without laboratory prepared or determined reference analyte values.

PubMed

Ottaway, Josh; Farrell, Jeremy A; Kalivas, John H

2013-02-05

An essential part to calibration is establishing the analyte calibration reference samples. These samples must characterize the sample matrix and measurement conditions (chemical, physical, instrumental, and environmental) of any sample to be predicted. Calibration usually requires measuring spectra for numerous reference samples in addition to determining the corresponding analyte reference values. Both tasks are typically time-consuming and costly. This paper reports on a method named pure component Tikhonov regularization (PCTR) that does not require laboratory prepared or determined reference values. Instead, an analyte pure component spectrum is used in conjunction with nonanalyte spectra for calibration. Nonanalyte spectra can be from different sources including pure component interference samples, blanks, and constant analyte samples. The approach is also applicable to calibration maintenance when the analyte pure component spectrum is measured in one set of conditions and nonanalyte spectra are measured in new conditions. The PCTR method balances the trade-offs between calibration model shrinkage and the degree of orthogonality to the nonanalyte content (model direction) in order to obtain accurate predictions. Using visible and near-infrared (NIR) spectral data sets, the PCTR results are comparable to those obtained using ridge regression (RR) with reference calibration sets. The flexibility of PCTR also allows including reference samples if such samples are available.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics.

PubMed

Ganapathiraju, Madhavi K; Orii, Naoki

2013-08-30

Advances in biotechnology have created "big-data" situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist's perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact. We propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI. This position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

Multi-analytical Approaches Informing the Risk of Sepsis

NASA Astrophysics Data System (ADS)

Gwadry-Sridhar, Femida; Lewden, Benoit; Mequanint, Selam; Bauer, Michael

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is huge. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multiple analytic methods that together could be used to provide greater insight. In this paper, we analyze data about patients with and without sepsis using a decision tree approach and a cluster analysis approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities,while considering their clinical significance.

The role of mechanics during brain development

NASA Astrophysics Data System (ADS)

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-12-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated with neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism.

The role of mechanics during brain development

PubMed Central

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-01-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated to neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von-Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism. PMID:25202162

Experimental Results From the Thermal Energy Storage-1 (TES-1) Flight Experiment

NASA Technical Reports Server (NTRS)

Jacqmin, David

1995-01-01

The Thermal Energy Storage (TES) experiments are designed to provide data to help researchers understand the long-duration microgravity behavior of thermal energy storage fluoride salts that undergo repeated melting and freezing. Such data, which have never been obtained before, have direct application to space-based solar dynamic power systems. These power systems will store solar energy in a thermal energy salt, such as lithium fluoride (LiF) or a eutectic of lithium fluoride/calcium difluoride (LiF-CaF2) (which melts at a lower temperature). The energy will be stored as the latent heat of fusion when the salt is melted by absorbing solar thermal energy. The stored energy will then be extracted during the shade portion of the orbit, enabling the solar dynamic power system to provide constant electrical power over the entire orbit. Analytical computer codes have been developed to predict the performance of a spacebased solar dynamic power system. However, the analytical predictions must be verified experimentally before the analytical results can be used for future space power design applications. Four TES flight experiments will be used to obtain the needed experimental data. This article focuses on the flight results from the first experiment, TES-1, in comparison to the predicted results from the Thermal Energy Storage Simulation (TESSIM) analytical computer code.

10 CFR 431.445 - Determination of small electric motor efficiency.

Code of Federal Regulations, 2012 CFR

2012-01-01

... statistical analysis, computer simulation or modeling, or other analytic evaluation of performance data. (3... statistical analysis, computer simulation or modeling, and other analytic evaluation of performance data on.... (ii) If requested by the Department, the manufacturer shall conduct simulations to predict the...

Calculation of Thermally-Induced Displacements in Spherically Domed Ion Engine Grids

NASA Technical Reports Server (NTRS)

Soulas, George C.

2006-01-01

An analytical method for predicting the thermally-induced normal and tangential displacements of spherically domed ion optics grids under an axisymmetric thermal loading is presented. A fixed edge support that could be thermally expanded is used for this analysis. Equations for the displacements both normal and tangential to the surface of the spherical shell are derived. A simplified equation for the displacement at the center of the spherical dome is also derived. The effects of plate perforation on displacements and stresses are determined by modeling the perforated plate as an equivalent solid plate with modified, or effective, material properties. Analytical model results are compared to the results from a finite element model. For the solid shell, comparisons showed that the analytical model produces results that closely match the finite element model results. The simplified equation for the normal displacement of the spherical dome center is also found to accurately predict this displacement. For the perforated shells, the analytical solution and simplified equation produce accurate results for materials with low thermal expansion coefficients.

Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell.

PubMed

Beck, Jason G; Skuratovsky, Aleksander; Granger, Michael C; Porter, Marc D

2017-01-17

In this paper, we describe a novel method for analyte quantitation that does not rely on calibrants, internal standards, or calibration curves but, rather, leverages the relationship between disparate and predictable surface-directed analyte flux to an array of sensing addresses and a measured resultant signal. To reduce this concept to practice, we fabricated two flow cells such that the mean linear fluid velocity, U, was varied systematically over an array of electrodes positioned along the flow axis. This resulted in a predictable variation of the address-directed flux of a redox analyte, ferrocenedimethanol (FDM). The resultant limiting currents measured at a series of these electrodes, and accurately described by a convective-diffusive transport model, provided a means to calculate an "unknown" concentration without the use of calibrants, internal standards, or a calibration curve. Furthermore, the experiment and concentration calculation only takes minutes to perform. Deviation in calculated FDM concentrations from true values was minimized to less than 0.5% when empirically derived values of U were employed.

Analytical functions to predict cosmic-ray neutron spectra in the atmosphere.

PubMed

Sato, Tatsuhiko; Niita, Koji

2006-09-01

Estimation of cosmic-ray neutron spectra in the atmosphere has been an essential issue in the evaluation of the aircrew doses and the soft-error rates of semiconductor devices. We therefore performed Monte Carlo simulations for estimating neutron spectra using the PHITS code in adopting the nuclear data library JENDL-High-Energy file. Excellent agreements were observed between the calculated and measured spectra for a wide altitude range even at the ground level. Based on a comprehensive analysis of the simulation results, we propose analytical functions that can predict the cosmic-ray neutron spectra for any location in the atmosphere at altitudes below 20 km, considering the influences of local geometries such as ground and aircraft on the spectra. The accuracy of the analytical functions was well verified by various experimental data.

Analytical formulation of impulsive collision avoidance dynamics

NASA Astrophysics Data System (ADS)

Bombardelli, Claudio

2014-02-01

The paper deals with the problem of impulsive collision avoidance between two colliding objects in three dimensions and assuming elliptical Keplerian orbits. Closed-form analytical expressions are provided that accurately predict the relative dynamics of the two bodies in the encounter b-plane following an impulsive delta-V manoeuvre performed by one object at a given orbit location prior to the impact and with a generic three-dimensional orientation. After verifying the accuracy of the analytical expressions for different orbital eccentricities and encounter geometries the manoeuvre direction that maximises the miss distance is obtained numerically as a function of the arc length separation between the manoeuvre point and the predicted collision point. The provided formulas can be used for high-accuracy instantaneous estimation of the outcome of a generic impulsive collision avoidance manoeuvre and its optimisation.

An analytical approach to obtaining JWL parameters from cylinder tests

NASA Astrophysics Data System (ADS)

Sutton, B. D.; Ferguson, J. W.; Hodgson, A. N.

2017-01-01

An analytical method for determining parameters for the JWL Equation of State from cylinder test data is described. This method is applied to four datasets obtained from two 20.3 mm diameter EDC37 cylinder tests. The calculated pressure-relative volume (p-Vr) curves agree with those produced by hydro-code modelling. The average calculated Chapman-Jouguet (CJ) pressure is 38.6 GPa, compared to the model value of 38.3 GPa; the CJ relative volume is 0.729 for both. The analytical pressure-relative volume curves produced agree with the one used in the model out to the commonly reported expansion of 7 relative volumes, as do the predicted energies generated by integrating under the p-Vr curve. The calculated energy is within 1.6% of that predicted by the model.

Application of laser Raman spectroscopy in concentration measurements of multiple analytes in human body fluids

NASA Astrophysics Data System (ADS)

Qu, Jianan Y.; Suria, David; Wilson, Brian C.

1998-05-01

The primary goal of these studies was to demonstrate that NIR Raman spectroscopy is feasible as a rapid and reagentless analytic method for clinical diagnostics. Raman spectra were collected on human serum and urine samples using a 785 nm excitation laser and a single-stage holographic spectrometer. A partial east squares method was used to predict the analyte concentrations of interest. The actual concentrations were determined by a standard clinical chemistry. The prediction accuracy of total protein, albumin, triglyceride and glucose in human sera ranged from 1.5 percent to 5 percent which is greatly acceptable for clinical diagnostics. The concentration measurements of acetaminophen, ethanol and codeine inhuman urine have demonstrated the potential of NIR Raman technology in screening of therapeutic drugs and substances of abuse.

AI based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics using Reactive Programming

NASA Astrophysics Data System (ADS)

Kaur, Jagreet; Singh Mann, Kulwinder, Dr.

2018-01-01

AI in Healthcare needed to bring real, actionable insights and Individualized insights in real time for patients and Doctors to support treatment decisions., We need a Patient Centred Platform for integrating EHR Data, Patient Data, Prescriptions, Monitoring, Clinical research and Data. This paper proposes a generic architecture for enabling AI based healthcare analytics Platform by using open sources Technologies Apache beam, Apache Flink Apache Spark, Apache NiFi, Kafka, Tachyon, Gluster FS, NoSQL- Elasticsearch, Cassandra. This paper will show the importance of applying AI based predictive and prescriptive analytics techniques in Health sector. The system will be able to extract useful knowledge that helps in decision making and medical monitoring in real-time through an intelligent process analysis and big data processing.

Scaling laws for gas breakdown for nanoscale to microscale gaps at atmospheric pressure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loveless, Amanda M.; Garner, Allen L., E-mail: algarner@purdue.edu

2016-06-06

Electronics miniaturization motivates gas breakdown predictions for microscale and smaller gaps, since traditional breakdown theory fails when gap size, d, is smaller than ∼15 μm at atmospheric pressure, p{sub atm}. We perform a matched asymptotic analysis to derive analytic expressions for breakdown voltage, V{sub b}, at p{sub atm} for 1 nm ≤ d ≤ 35 μm. We obtain excellent agreement between numerical, analytic, and particle-in-cell simulations for argon, and show V{sub b} decreasing as d → 0, instead of increasing as predicted by Paschen's law. This work provides an analytic framework for determining V{sub b} at atmospheric pressure for various gap distances that may be extended tomore » other gases.« less

Theoretical studies of tone noise from a fan rotor

NASA Technical Reports Server (NTRS)

Rao, G. V. R.; Chu, W. T.; Digumarthi, R. V.

1973-01-01

An analytical study was made of some possible rotor alone noise sources of dipole, quadrapole and monopole characters which generate discrete tone noise. Particular emphasis is given to the tone noise caused by fan inlet flow distortion and turbulence. Analytical models are developed to allow prediction of absolute levels. Experimental data measured on a small scale fan is presented which indicates inlet turbulence interaction with a fan rotor can be a source of tone noise. Predicted and measured tone noise for the small scale rotor are shown to be in reasonable agreement.

An analytical method for prediction of stability lobes diagram of milling of large-size thin-walled workpiece

NASA Astrophysics Data System (ADS)

Yao, Jiming; Lin, Bin; Guo, Yu

2017-01-01

Different from common thin-walled workpiece, in the process of milling of large-size thin-walled workpiece chatter in the axial direction along the spindle is also likely to happen because of the low stiffness of the workpiece in this direction. An analytical method for prediction of stability lobes of milling of large-size thin-walled workpiece is presented in this paper. In the method, not only frequency response function of the tool point but also frequency response function of the workpiece is considered.

Distribution analysis for F100(3) engine

NASA Technical Reports Server (NTRS)

Walter, W. A.; Shaw, M.

1980-01-01

The F100(3) compression system response to inlet circumferential distortion was investigated using an analytical compressor flow model. Compression system response to several types of distortion, including pressure, temperature, and combined pressure/temperature distortions, was investigated. The predicted response trends were used in planning future F100(3) distortion tests. Results show that compression system response to combined temperature and pressure distortions depends upon the relative orientation, as well as the individual amplitudes and circumferential extents of the distortions. Also the usefulness of the analytical predictions in planning engine distortion tests is indicated.

Buckling behavior of composite cylinders subjected to compressive loading

NASA Technical Reports Server (NTRS)

Carri, R. L.

1973-01-01

Room temperature compressive buckling strengths of eight cylinders, four boron-epoxy and four boron-epoxy reinforced-titanium, with diameter to thickness ratios ranging between 40 and 67 are determined experimentally and compared with analytical predictions. Numerical buckling strengths are presented for Donnell's, Flugge's and Sanders' shell theories for anisotropic and orthotropic material cases. Comparison of analytical predictions with experimental results indicates good agreement and the recommended correlation factor suggested in the literature is applicable for design. For the cylinders tested, the correlation between experiment and theory ranged from 0.73 to 0.97.

Mechanics of additively manufactured porous biomaterials based on the rhombicuboctahedron unit cell.

PubMed

Hedayati, R; Sadighi, M; Mohammadi-Aghdam, M; Zadpoor, A A

2016-01-01

Thanks to recent developments in additive manufacturing techniques, it is now possible to fabricate porous biomaterials with arbitrarily complex micro-architectures. Micro-architectures of such biomaterials determine their physical and biological properties, meaning that one could potentially improve the performance of such biomaterials through rational design of micro-architecture. The relationship between the micro-architecture of porous biomaterials and their physical and biological properties has therefore received increasing attention recently. In this paper, we studied the mechanical properties of porous biomaterials made from a relatively unexplored unit cell, namely rhombicuboctahedron. We derived analytical relationships that relate the micro-architecture of such porous biomaterials, i.e. the dimensions of the rhombicuboctahedron unit cell, to their elastic modulus, Poisson's ratio, and yield stress. Finite element models were also developed to validate the analytical solutions. Analytical and numerical results were compared with experimental data from one of our recent studies. It was found that analytical solutions and numerical results show a very good agreement particularly for smaller values of apparent density. The elastic moduli predicted by analytical and numerical models were in very good agreement with experimental observations too. While in excellent agreement with each other, analytical and numerical models somewhat over-predicted the yield stress of the porous structures as compared to experimental data. As the ratio of the vertical struts to the inclined struts, α, approaches zero and infinity, the rhombicuboctahedron unit cell respectively approaches the octahedron (or truncated cube) and cube unit cells. For those limits, the analytical solutions presented here were found to approach the analytic solutions obtained for the octahedron, truncated cube, and cube unit cells, meaning that the presented solutions are generalizations of the analytical solutions obtained for several other types of porous biomaterials. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics.

PubMed

Sammour, T; Cohen, L; Karunatillake, A I; Lewis, M; Lawrence, M J; Hunter, A; Moore, J W; Thomas, M L

2017-11-01

Recently published data support the use of a web-based risk calculator ( www.anastomoticleak.com ) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset. Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded. The primary outcome was anastomotic leak within 90 days as defined by previously published criteria. Area under receiver operating characteristic curve (AUROC) was derived and compared with that of the American College of Surgeons National Surgical Quality Improvement Program ® (ACS NSQIP) calculator and the colon leakage score (CLS) calculator for left colectomy. Commercially available artificial intelligence-based analytics software was used to further interrogate the prediction algorithm. A total of 626 patients were identified. Four hundred and fifty-six patients met the inclusion criteria, and 402 had complete data available for all the calculator variables (126 had a left colectomy). Laparoscopic surgery was performed in 39.6% and emergency surgery in 14.7%. The anastomotic leak rate was 7.2%, with 31.0% requiring reoperation. The anastomoticleak.com calculator was significantly predictive of leak and performed better than the ACS NSQIP calculator (AUROC 0.73 vs 0.58) and the CLS calculator (AUROC 0.96 vs 0.80) for left colectomy. Artificial intelligence-predictive analysis supported these findings and identified an improved prediction model. The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.

Towards Actionable Learning Analytics Using Dispositions

ERIC Educational Resources Information Center

Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan

2017-01-01

Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…

Analytical and experimental studies on detection of longitudinal, L and inverted T cracks in isotropic and bi-material beams based on changes in natural frequencies

NASA Astrophysics Data System (ADS)

Ravi, J. T.; Nidhan, S.; Muthu, N.; Maiti, S. K.

2018-02-01

An analytical method for determination of dimensions of longitudinal crack in monolithic beams, based on frequency measurements, has been extended to model L and inverted T cracks. Such cracks including longitudinal crack arise in beams made of layered isotropic or composite materials. A new formulation for modelling cracks in bi-material beams is presented. Longitudinal crack segment sizes, for L and inverted T cracks, varying from 2.7% to 13.6% of length of Euler-Bernoulli beams are considered. Both forward and inverse problems have been examined. In the forward problems, the analytical results are compared with finite element (FE) solutions. In the inverse problems, the accuracy of prediction of crack dimensions is verified using FE results as input for virtual testing. The analytical results show good agreement with the actual crack dimensions. Further, experimental studies have been done to verify the accuracy of the analytical method for prediction of dimensions of three types of crack in isotropic and bi-material beams. The results show that the proposed formulation is reliable and can be employed for crack detection in slender beam like structures in practice.

Development of an analytical solution for the Budyko watershed parameter in terms of catchment physical features

NASA Astrophysics Data System (ADS)

Reaver, N.; Kaplan, D. A.; Jawitz, J. W.

2017-12-01

The Budyko hypothesis states that a catchment's long-term water and energy balances are dependent on two relatively easy to measure quantities: rainfall depth and potential evaporation. This hypothesis is expressed as a simple function, the Budyko equation, which allows for the prediction of a catchment's actual evapotranspiration and discharge from measured rainfall depth and potential evaporation, data which are widely available. However, the two main analytically derived forms of the Budyko equation contain a single unknown watershed parameter, whose value varies across catchments; variation in this parameter has been used to explain the hydrological behavior of different catchments. The watershed parameter is generally thought of as a lumped quantity that represents the influence of all catchment biophysical features (e.g. soil type and depth, vegetation type, timing of rainfall, etc). Previous work has shown that the parameter is statistically correlated with catchment properties, but an explicit expression has been elusive. While the watershed parameter can be determined empirically by fitting the Budyko equation to measured data in gauged catchments where actual evapotranspiration can be estimated, this limits the utility of the framework for predicting impacts to catchment hydrology due to changing climate and land use. In this study, we developed an analytical solution for the lumped catchment parameter for both forms of the Budyko equation. We combined these solutions with a statistical soil moisture model to obtain analytical solutions for the Budyko equation parameter as a function of measurable catchment physical features, including rooting depth, soil porosity, and soil wilting point. We tested the predictive power of these solutions using the U.S. catchments in the MOPEX database. We also compared the Budyko equation parameter estimates generated from our analytical solutions (i.e. predicted parameters) with those obtained through the calibration of the Budyko equation to discharge data (i.e. empirical parameters), and found good agreement. These results suggest that it is possible to predict the Budyko equation watershed parameter directly from physical features, even for ungauged catchments.

Analytical Modelling of Transverse Matrix Cracking of [plus or minus Theta/90(sub n)](sub s) Composite Laminates Under Multiaxial Loading

NASA Technical Reports Server (NTRS)

Mayugo, J A.; Camanho, P. P.; Maimi, P.; Davila, C. G.

2010-01-01

An analytical model based on the analysis of a cracked unit cell of a composite laminate subjected to multiaxial loads is proposed to predict the onset and accumulation of transverse matrix cracks in the 90(sub n) plies of uniformly stressed [plus or minus Theta/90(sub n)](sub s) laminates. The model predicts the effect of matrix cracks on the stiffness of the laminate, as well as the ultimate failure of the laminate, and it accounts for the effect of the ply thickness on the ply strength. Several examples describing the predictions of laminate response, from damage onset up to final failure under both uniaxial and multiaxial loads, are presented.

Analytics to Action: Predictive Model Outcomes and a Communication Strategy for Student Persistence

ERIC Educational Resources Information Center

Miller, Nathan Brad; Bell, Bryan

2016-01-01

Increased federal attention to student completion metrics and uncertain financial forecasts have heightened the tenor of student retention conversations. Improved institutional retention rates will lead to higher completion rates and relieve some funding concerns. To accomplish these improvements, institutions have invested in analytics to better…

Translating Learning into Numbers: A Generic Framework for Learning Analytics

ERIC Educational Resources Information Center

Greller, Wolfgang; Drachsler, Hendrik

2012-01-01

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning…

Comparison of particle tracking algorithms in commercial CFD packages: sedimentation and diffusion.

PubMed

Robinson, Risa J; Snyder, Pam; Oldham, Michael J

2007-05-01

Computational fluid dynamic modeling software has enabled microdosimetry patterns of inhaled toxins and toxicants to be predicted and visualized, and is being used in inhalation toxicology and risk assessment. These predicted microdosimetry patterns in airway structures are derived from predicted airflow patterns within these airways and particle tracking algorithms used in computational fluid dynamics (CFD) software packages. Although these commercial CFD codes have been tested for accuracy under various conditions, they have not been well tested for respiratory flows in general. Nor has their particle tracking algorithm accuracy been well studied. In this study, three software packages, Fluent Discrete Phase Model (DPM), Fluent Fine Particle Model (FPM), and ANSYS CFX, were evaluated. Sedimentation and diffusion were each isolated in a straight tube geometry and tested for accuracy. A range of flow rates corresponding to adult low activity (minute ventilation = 10 L/min) and to heavy exertion (minute ventilation = 60 L/min) were tested by varying the range of dimensionless diffusion and sedimentation parameters found using the Weibel symmetric 23 generation lung morphology. Numerical results for fully developed parabolic and uniform (slip) profiles were compared respectively, to Pich (1972) and Yu (1977) analytical sedimentation solutions. Schum and Yeh (1980) equations for sedimentation were also compared. Numerical results for diffusional deposition were compared to analytical solutions of Ingham (1975) for parabolic and uniform profiles. Significant differences were found among the various CFD software packages and between numerical and analytical solutions. Therefore, it is prudent to validate CFD predictions against analytical solutions in idealized geometry before tackling the complex geometries of the respiratory tract.

What Is Trust? Ethics and Risk Governance in Precision Medicine and Predictive Analytics

PubMed Central

Adjekum, Afua; Ienca, Marcello

2017-01-01

Abstract Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the “futures” and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of “trust facilitators”: (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a “points to consider” on how best to enhance trust in precision medicine and predictive analytics. PMID:29257733

What Is Trust? Ethics and Risk Governance in Precision Medicine and Predictive Analytics.

PubMed

Adjekum, Afua; Ienca, Marcello; Vayena, Effy

2017-12-01

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the "futures" and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of "trust facilitators": (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a "points to consider" on how best to enhance trust in precision medicine and predictive analytics.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

PubMed

Taylor, R Andrew; Pare, Joseph R; Venkatesh, Arjun K; Mowafi, Hani; Melnick, Edward R; Fleischman, William; Hall, M Kennedy

2016-03-01

Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. © 2015 by the Society for Academic Emergency Medicine.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

PubMed Central

Taylor, R. Andrew; Pare, Joseph R.; Venkatesh, Arjun K.; Mowafi, Hani; Melnick, Edward R.; Fleischman, William; Hall, M. Kennedy

2018-01-01

Objectives Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data–driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. Methods This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. Results There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). Conclusions In this proof-of-concept study, a local big data–driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. PMID:26679719

Development of a child head analytical dynamic model considering cranial nonuniform thickness and curvature - Applying to children aged 0-1 years old.

PubMed

Li, Zhigang; Ji, Cheng; Wang, Lishu

2018-07-01

Although analytical models have been used to quickly predict head response under impact condition, the existing models generally took the head as regular shell with uniform thickness which cannot account for the actual head geometry with varied cranial thickness and curvature at different locations. The objective of this study is to develop and validate an analytical model incorporating actual cranial thickness and curvature for child aged 0-1YO and investigate their effects on child head dynamic responses at different head locations. To develop the new analytical model, the child head was simplified into an irregular fluid-filled shell with non-uniform thickness and the cranial thickness and curvature at different locations were automatically obtained from CT scans using a procedure developed in this study. The implicit equation of maximum impact force was derived as a function of elastic modulus, thickness and radius of curvature of cranium. The proposed analytical model are compared with cadaver test data of children aged 0-1 years old and it is shown to be accurate in predicting head injury metrics. According to this model, obvious difference in injury metrics were observed among subjects with the same age, but different cranial thickness and curvature; and the injury metrics at forehead location are significant higher than those at other locations due to large thickness it owns. The proposed model shows good biofidelity and can be used in quickly predicting the dynamics response at any location of head for child younger than 1 YO. Copyright © 2018 Elsevier B.V. All rights reserved.

An analytical solution for predicting the transient seepage from a subsurface drainage system

NASA Astrophysics Data System (ADS)

Xin, Pei; Dan, Han-Cheng; Zhou, Tingzhang; Lu, Chunhui; Kong, Jun; Li, Ling

2016-05-01

Subsurface drainage systems have been widely used to deal with soil salinization and waterlogging problems around the world. In this paper, a mathematical model was introduced to quantify the transient behavior of the groundwater table and the seepage from a subsurface drainage system. Based on the assumption of a hydrostatic pressure distribution, the model considered the pore-water flow in both the phreatic and vadose soil zones. An approximate analytical solution for the model was derived to quantify the drainage of soils which were initially water-saturated. The analytical solution was validated against laboratory experiments and a 2-D Richards equation-based model, and found to predict well the transient water seepage from the subsurface drainage system. A saturated flow-based model was also tested and found to over-predict the time required for drainage and the total water seepage by nearly one order of magnitude, in comparison with the experimental results and the present analytical solution. During drainage, a vadose zone with a significant water storage capacity developed above the phreatic surface. A considerable amount of water still remained in the vadose zone at the steady state with the water table situated at the drain bottom. Sensitivity analyses demonstrated that effects of the vadose zone were intensified with an increased thickness of capillary fringe, capillary rise and/or burying depth of drains, in terms of the required drainage time and total water seepage. The analytical solution provides guidance for assessing the capillary effects on the effectiveness and efficiency of subsurface drainage systems for combating soil salinization and waterlogging problems.

Towards Adaptive Educational Assessments: Predicting Student Performance using Temporal Stability and Data Analytics in Learning Management Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thakur, Gautam; Olama, Mohammed M; McNair, Wade

Data-driven assessments and adaptive feedback are becoming a cornerstone research in educational data analytics and involve developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the students and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present our efforts in using data analytics that enable educationists to design novel data-driven assessment and feedback mechanisms. In order to achieve this objective, we investigate temporal stabilitymore » of students grades and perform predictive analytics on academic data collected from 2009 through 2013 in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for assessments and predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total Grade Point Average(GPA) at the same term they enrolled in the course. Second, time series models in both frequency and time domains are applied to characterize the progression as well as overall projections of the grades. In particular, the model analyzed the stability as well as fluctuation of grades among students during the collegiate years (from freshman to senior) and disciplines. Third, Logistic Regression and Neural Network predictive models are used to identify students as early as possible who are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. The time series analysis indicates that assessments and continuous feedback are critical for freshman and sophomores (even with easy courses) than for seniors, and those assessments may be provided using the predictive models. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy. Our results show that there are strong ties associated with the first few weeks for coursework and they have an impact on the design and distribution of individual modules.« less

A density functional theory study of the correlation between analyte basicity, ZnPc adsorption strength, and sensor response.

PubMed

Tran, N L; Bohrer, F I; Trogler, W C; Kummel, A C

2009-05-28

Density functional theory (DFT) simulations were used to determine the binding strength of 12 electron-donating analytes to the zinc metal center of a zinc phthalocyanine molecule (ZnPc monomer). The analyte binding strengths were compared to the analytes' enthalpies of complex formation with boron trifluoride (BF(3)), which is a direct measure of their electron donating ability or Lewis basicity. With the exception of the most basic analyte investigated, the ZnPc binding energies were found to correlate linearly with analyte basicities. Based on natural population analysis calculations, analyte complexation to the Zn metal of the ZnPc monomer resulted in limited charge transfer from the analyte to the ZnPc molecule, which increased with analyte-ZnPc binding energy. The experimental analyte sensitivities from chemiresistor ZnPc sensor data were proportional to an exponential of the binding energies from DFT calculations consistent with sensitivity being proportional to analyte coverage and binding strength. The good correlation observed suggests DFT is a reliable method for the prediction of chemiresistor metallophthalocyanine binding strengths and response sensitivities.

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

PubMed Central

Dinov, Ivo D.; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W.; Price, Nathan D.; Van Horn, John D.; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M.; Dauer, William; Toga, Arthur W.

2016-01-01

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications. PMID:27494614

TU-F-17A-03: An Analytical Respiratory Perturbation Model for Lung Motion Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G; Yuan, A; Wei, J

2014-06-15

Purpose: Breathing irregularity is common, causing unreliable prediction in tumor motion for correlation-based surrogates. Both tidal volume (TV) and breathing pattern (BP=ΔVthorax/TV, where TV=ΔVthorax+ΔVabdomen) affect lung motion in anterior-posterior and superior-inferior directions. We developed a novel respiratory motion perturbation (RMP) model in analytical form to account for changes in TV and BP in motion prediction from simulation to treatment. Methods: The RMP model is an analytical function of patient-specific anatomic and physiologic parameters. It contains a base-motion trajectory d(x,y,z) derived from a 4-dimensional computed tomography (4DCT) at simulation and a perturbation term Δd(ΔTV,ΔBP) accounting for deviation at treatment from simulation.more » The perturbation is dependent on tumor-specific location and patient-specific anatomy. Eleven patients with simulation and treatment 4DCT images were used to assess the RMP method in motion prediction from 4DCT1 to 4DCT2, and vice versa. For each patient, ten motion trajectories of corresponding points in the lower lobes were measured in both 4DCTs: one served as the base-motion trajectory and the other as the ground truth for comparison. In total, 220 motion trajectory predictions were assessed. The motion discrepancy between two 4DCTs for each patient served as a control. An established 5D motion model was used for comparison. Results: The average absolute error of RMP model prediction in superior-inferior direction is 1.6±1.8 mm, similar to 1.7±1.6 mm from the 5D model (p=0.98). Some uncertainty is associated with limited spatial resolution (2.5mm slice thickness) and temporal resolution (10-phases). Non-corrected motion discrepancy between two 4DCTs is 2.6±2.7mm, with the maximum of ±20mm, and correction is necessary (p=0.01). Conclusion: The analytical motion model predicts lung motion with accuracy similar to the 5D model. The analytical model is based on physical relationships, requires no training, and therefore is potentially more resilient to breathing irregularities. On-going investigation introduces airflow into the RMP model for improvement. This research is in part supported by NIH (U54CA137788/132378). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.« less

Initiating an Online Reputation Monitoring System with Open Source Analytics Tools

NASA Astrophysics Data System (ADS)

Shuhud, Mohd Ilias M.; Alwi, Najwa Hayaati Md; Halim, Azni Haslizan Abd

2018-05-01

Online reputation is an invaluable asset for modern organizations as it can help in business performance especially in sales and profit. However, if we are not aware of our reputation, it is difficult to maintain it. Thus, social media analytics is a new tool that can provide online reputation monitoring in various ways such as sentiment analysis. As a result, numerous large-scale organizations have implemented Online Reputation Monitoring (ORM) systems. However, this solution is not supposed to be exclusively for high-income organizations, as many organizations regardless sizes and types are now online. This research attempts to propose an affordable and reliable ORM system using combination of open source analytics tools for both novice practitioners and academicians. We also evaluate its prediction accuracy and we discovered that the system provides acceptable predictions (sixty percent accuracy) and demonstrate a tally prediction of major polarity by human annotation. The proposed system can help in supporting business decisions with flexible monitoring strategies especially for organization that want to initiate and administrate ORM themselves at low cost.

Predicting CH4 adsorption capacity of microporous carbon using N2 isotherm and a new analytical model

USGS Publications Warehouse

Sun, Jielun; Chen, S.; Rostam-Abadi, M.; Rood, M.J.

1998-01-01

A new analytical pore size distribution (PSD) model was developed to predict CH4 adsorption (storage) capacity of microporous adsorbent carbon. The model is based on a 3-D adsorption isotherm equation, derived from statistical mechanical principles. Least squares error minimization is used to solve the PSD without any pre-assumed distribution function. In comparison with several well-accepted analytical methods from the literature, this 3-D model offers relatively realistic PSD description for select reference materials, including activated carbon fibers. N2 and CH4 adsorption data were correlated using the 3-D model for commercial carbons BPL and AX-21. Predicted CH4 adsorption isotherms, based on N2 adsorption at 77 K, were in reasonable agreement with the experimental CH4 isotherms. Modeling results indicate that not all the pores contribute the same percentage Vm/Vs for CH4 storage due to different adsorbed CH4 densities. Pores near 8-9 A?? shows higher Vm/Vs on the equivalent volume basis than does larger pores.

Shuttle antenna radome technology test program. Volume 2: Development of S-band antenna interface design

NASA Technical Reports Server (NTRS)

Kuhlman, E. A.; Baranowski, L. C.

1977-01-01

The effects of the Thermal Protection Subsystem (TPS) contamination on the space shuttle orbiter S band quad antenna due to multiple mission buildup are discussed. A test fixture was designed, fabricated and exposed to ten cycles of simulated ground and flight environments. Radiation pattern and impedance tests were performed to measure the effects of the contaminates. The degradation in antenna performance was attributed to the silicone waterproofing in the TPS tiles rather than exposure to the contaminating sources used in the test program. Validation of the accuracy of an analytical thermal model is discussed. Thermal vacuum tests with a test fixture and a representative S band quad antenna were conducted to evaluate the predictions of the analytical thermal model for two orbital heating conditions and entry from each orbit. The results show that the accuracy of predicting the test fixture thermal responses is largely dependent on the ability to define the boundary and ambient conditions. When the test conditions were accurately included in the analytical model, the predictions were in excellent agreement with measurements.

An overview of selected NASP aeroelastic studies at the NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Spain, Charles V.; Soistmann, David L.; Parker, Ellen C.; Gibbons, Michael D.; Gilbert, Michael G.

1990-01-01

Following an initial discussion of the NASP flight environment, the results of recent aeroelastic testing of NASP-type highly swept delta-wing models in Langley's Transonic Dynamics Tunnel (TDT) are summarized. Subsonic and transonic flutter characteristics of a variety of these models are described, and several analytical codes used to predict flutter of these models are evaluated. These codes generally provide good, but conservative predictions of subsonic and transonic flutter. Also, test results are presented on a nonlinear transonic phenomena known as aileron buzz which occurred in the wind tunnel on highly swept delta wings with full-span ailerons. An analytical procedure which assesses the effects of hypersonic heating on aeroelastic instabilities (aerothermoelasticity) is also described. This procedure accurately predicted flutter of a heated aluminum wing on which experimental data exists. Results are presented on the application of this method to calculate the flutter characteristics of a fine-element model of a generic NASP configuration. Finally, it is demonstrated analytically that active controls can be employed to improve the aeroelastic stability and ride quality of a generic NASP vehicle flying at hypersonic speeds.

An investigation of the information propagation and entropy transport aspects of Stirling machine numerical simulation

NASA Technical Reports Server (NTRS)

Goldberg, Louis F.

1992-01-01

Aspects of the information propagation modeling behavior of integral machine computer simulation programs are investigated in terms of a transmission line. In particular, the effects of pressure-linking and temporal integration algorithms on the amplitude ratio and phase angle predictions are compared against experimental and closed-form analytic data. It is concluded that the discretized, first order conservation balances may not be adequate for modeling information propagation effects at characteristic numbers less than about 24. An entropy transport equation suitable for generalized use in Stirling machine simulation is developed. The equation is evaluated by including it in a simulation of an incompressible oscillating flow apparatus designed to demonstrate the effect of flow oscillations on the enhancement of thermal diffusion. Numerical false diffusion is found to be a major factor inhibiting validation of the simulation predictions with experimental and closed-form analytic data. A generalized false diffusion correction algorithm is developed which allows the numerical results to match their analytic counterparts. Under these conditions, the simulation yields entropy predictions which satisfy Clausius' inequality.

Prediction of the chromatographic retention of acid-base compounds in pH buffered methanol-water mobile phases in gradient mode by a simplified model.

PubMed

Andrés, Axel; Rosés, Martí; Bosch, Elisabeth

2015-03-13

Retention of ionizable analytes under gradient elution depends on the pH of the mobile phase, the pKa of the analyte and their evolution along the programmed gradient. In previous work, a model depending on two fitting parameters was recommended because of its very favorable relationship between accuracy and required experimental work. It was developed using acetonitrile as the organic modifier and involves pKa modeling by means of equations that take into account the acidic functional group of the compound (carboxylic acid, protonated amine, etc.). In this work, the two-parameter predicting model is tested and validated using methanol as the organic modifier of the mobile phase and several compounds of higher pharmaceutical relevance and structural complexity as testing analytes. The results have been quite good overall, showing that the predicting model is applicable to a wide variety of acid-base compounds using mobile phases prepared with acetonitrile or methanol. Copyright © 2015 Elsevier B.V. All rights reserved.

The Acoustic Analogy: A Powerful Tool in Aeroacoustics with Emphasis on Jet Noise Prediction

NASA Technical Reports Server (NTRS)

Farassat, F.; Doty, Michael J.; Hunter, Craig A.

2004-01-01

The acoustic analogy introduced by Lighthill to study jet noise is now over 50 years old. In the present paper, Lighthill s Acoustic Analogy is revisited together with a brief evaluation of the state-of-the-art of the subject and an exploration of the possibility of further improvements in jet noise prediction from analytical methods, computational fluid dynamics (CFD) predictions, and measurement techniques. Experimental Particle Image Velocimetry (PIV) data is used both to evaluate turbulent statistics from Reynolds-averaged Navier-Stokes (RANS) CFD and to propose correlation models for the Lighthill stress tensor. The NASA Langley Jet3D code is used to study the effect of these models on jet noise prediction. From the analytical investigation, a retarded time correction is shown that improves, by approximately 8 dB, the over-prediction of aft-arc jet noise by Jet3D. In experimental investigation, the PIV data agree well with the CFD mean flow predictions, with room for improvement in Reynolds stress predictions. Initial modifications, suggested by the PIV data, to the form of the Jet3D correlation model showed no noticeable improvements in jet noise prediction.

A micromechanics-based strength prediction methodology for notched metal matrix composites

NASA Technical Reports Server (NTRS)

Bigelow, C. A.

1992-01-01

An analytical micromechanics based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and post fatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

A micromechanics-based strength prediction methodology for notched metal-matrix composites

NASA Technical Reports Server (NTRS)

Bigelow, C. A.

1993-01-01

An analytical micromechanics-based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three-dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and postfatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics-based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

Analytical and Finite Element Modeling of Nanomembranes for Miniaturized, Continuous Hemodialysis

PubMed Central

Burgin, Tucker; Johnson, Dean; Chung, Henry; Clark, Alfred; McGrath, James

2015-01-01

Hemodialysis involves large, periodic treatment doses using large-area membranes. If the permeability of dialysis membranes could be increased, it would reduce the necessary dialyzer size and could enable a wearable device that administers a continuous, low dose treatment of chronic kidney disease. This paper explores the application of ultrathin silicon membranes to this purpose, by way of analytical and finite element models of diffusive and convective transport of plasma solutes during hemodialysis, which we show to be predictive of experimental results. A proof-of-concept miniature nanomembrane dialyzer design is then proposed and analytically predicted to clear uremic toxins at near-ideal levels, as measured by several markers of dialysis adequacy. This work suggests the feasibility of miniature nanomembrane-based dialyzers that achieve therapeutic levels of uremic toxin clearance for patients with kidney failure. PMID:26729179

Black hole radiation and S-matrix.

NASA Astrophysics Data System (ADS)

Russo, J. G.

1999-04-01

The existence of an S-matrix below the threshold of black hole formation would be enough to exhibit, through its analytic structure, eventual thresholds for the creation of new objects and to describe, through analytic continuation, the physics above them in a unitary framework. In the context of a two-dimensional exactly soluble model, the semiclassical dynamics of quantum black holes is obtained by analytically continuing the description of the regime where no black hole is formed. The resulting spectrum of outgoing radiation departs from the one predicted by the Hawking model by the time the outgoing modes arise from the horizon with Planck-order frequencies. The theory predicts an unconventional scenario for the evolution: black holes only radiate out an energy of Planck mass order, stabilizing after a transitory period. A similar picture is obtained in 3+1 dimensions with spherical symmetry.

Development and application of a three dimensional numerical model for predicting pollutant and sediment transport using an Eulerian-Lagrangian marker particle technique

NASA Technical Reports Server (NTRS)

Pavish, D. L.; Spaulding, M. L.

1977-01-01

A computer coded Lagrangian marker particle in Eulerian finite difference cell solution to the three dimensional incompressible mass transport equation, Water Advective Particle in Cell Technique, WAPIC, was developed, verified against analytic solutions, and subsequently applied in the prediction of long term transport of a suspended sediment cloud resulting from an instantaneous dredge spoil release. Numerical results from WAPIC were verified against analytic solutions to the three dimensional incompressible mass transport equation for turbulent diffusion and advection of Gaussian dye releases in unbounded uniform and uniformly sheared uni-directional flow, and for steady-uniform plug channel flow. WAPIC was utilized to simulate an analytic solution for non-equilibrium sediment dropout from an initially vertically uniform particle distribution in one dimensional turbulent channel flow.

Survey of NASA research on crash dynamics

NASA Technical Reports Server (NTRS)

Thomson, R. G.; Carden, H. D.; Hayduk, R. J.

1984-01-01

Ten years of structural crash dynamics research activities conducted on general aviation aircraft by the National Aeronautics and Space Administration (NASA) are described. Thirty-two full-scale crash tests were performed at Langley Research Center, and pertinent data on airframe and seat behavior were obtained. Concurrent with the experimental program, analytical methods were developed to help predict structural behavior during impact. The effects of flight parameters at impact on cabin deceleration pulses at the seat/occupant interface, experimental and analytical correlation of data on load-limiting subfloor and seat configurations, airplane section test results for computer modeling validation, and data from emergency-locator-transmitter (ELT) investigations to determine probable cause of false alarms and nonactivations are assessed. Computer programs which provide designers with analytical methods for predicting accelerations, velocities, and displacements of collapsing structures are also discussed.

Prediction of turning stability using receptance coupling

NASA Astrophysics Data System (ADS)

Jasiewicz, Marcin; Powałka, Bartosz

2018-01-01

This paper presents an issue of machining stability prediction of dynamic "lathe - workpiece" system evaluated using receptance coupling method. Dynamic properties of the lathe components (the spindle and the tailstock) are assumed to be constant and can be determined experimentally based on the results of the impact test. Hence, the variable of the system "machine tool - holder - workpiece" is the machined part, which can be easily modelled analytically. The method of receptance coupling enables a synthesis of experimental (spindle, tailstock) and analytical (machined part) models, so impact testing of the entire system becomes unnecessary. The paper presents methodology of analytical and experimental models synthesis, evaluation of the stability lobes and experimental validation procedure involving both the determination of the dynamic properties of the system and cutting tests. In the summary the experimental verification results would be presented and discussed.

Quantitative prediction of solute strengthening in aluminium alloys.

PubMed

Leyson, Gerard Paul M; Curtin, William A; Hector, Louis G; Woodward, Christopher F

2010-09-01

Despite significant advances in computational materials science, a quantitative, parameter-free prediction of the mechanical properties of alloys has been difficult to achieve from first principles. Here, we present a new analytic theory that, with input from first-principles calculations, is able to predict the strengthening of aluminium by substitutional solute atoms. Solute-dislocation interaction energies in and around the dislocation core are first calculated using density functional theory and a flexible-boundary-condition method. An analytic model for the strength, or stress to move a dislocation, owing to the random field of solutes, is then presented. The theory, which has no adjustable parameters and is extendable to other metallic alloys, predicts both the energy barriers to dislocation motion and the zero-temperature flow stress, allowing for predictions of finite-temperature flow stresses. Quantitative comparisons with experimental flow stresses at temperature T=78 K are made for Al-X alloys (X=Mg, Si, Cu, Cr) and good agreement is obtained.

Comparison of simulator fidelity model predictions with in-simulator evaluation data

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Mckissick, B. T.; Ashworth, B. R.

1983-01-01

A full factorial in simulator experiment of a single axis, multiloop, compensatory pitch tracking task is described. The experiment was conducted to provide data to validate extensions to an analytic, closed loop model of a real time digital simulation facility. The results of the experiment encompassing various simulation fidelity factors, such as visual delay, digital integration algorithms, computer iteration rates, control loading bandwidths and proprioceptive cues, and g-seat kinesthetic cues, are compared with predictions obtained from the analytic model incorporating an optimal control model of the human pilot. The in-simulator results demonstrate more sensitivity to the g-seat and to the control loader conditions than were predicted by the model. However, the model predictions are generally upheld, although the predicted magnitudes of the states and of the error terms are sometimes off considerably. Of particular concern is the large sensitivity difference for one control loader condition, as well as the model/in-simulator mismatch in the magnitude of the plant states when the other states match.

Modeling of Compressible Flow with Friction and Heat Transfer Using the Generalized Fluid System Simulation Program (GFSSP)

NASA Technical Reports Server (NTRS)

Bandyopadhyay, Alak; Majumdar, Alok

2007-01-01

The present paper describes the verification and validation of a quasi one-dimensional pressure based finite volume algorithm, implemented in Generalized Fluid System Simulation Program (GFSSP), for predicting compressible flow with friction, heat transfer and area change. The numerical predictions were compared with two classical solutions of compressible flow, i.e. Fanno and Rayleigh flow. Fanno flow provides an analytical solution of compressible flow in a long slender pipe where incoming subsonic flow can be choked due to friction. On the other hand, Raleigh flow provides analytical solution of frictionless compressible flow with heat transfer where incoming subsonic flow can be choked at the outlet boundary with heat addition to the control volume. Nonuniform grid distribution improves the accuracy of numerical prediction. A benchmark numerical solution of compressible flow in a converging-diverging nozzle with friction and heat transfer has been developed to verify GFSSP's numerical predictions. The numerical predictions compare favorably in all cases.

Prediction of retention times in comprehensive two-dimensional gas chromatography using thermodynamic models.

PubMed

McGinitie, Teague M; Harynuk, James J

2012-09-14

A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC(p) for an analyte's interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both (1)t(r) and (2)t(r) for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for (1)t(r) and an average error of only 2.22% for (2)t(r). The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

Analytical Modeling for Mechanical Strength Prediction with Raman Spectroscopy and Fractured Surface Morphology of Novel Coconut Shell Powder Reinforced: Epoxy Composites

NASA Astrophysics Data System (ADS)

Singh, Savita; Singh, Alok; Sharma, Sudhir Kumar

2017-06-01

In this paper, an analytical modeling and prediction of tensile and flexural strength of three dimensional micro-scaled novel coconut shell powder (CSP) reinforced epoxy polymer composites have been reported. The novel CSP has a specific mixing ratio of different coconut shell particle size. A comparison is made between obtained experimental strength and modified Guth model. The result shows a strong evidence for non-validation of modified Guth model for strength prediction. Consequently, a constitutive modeled equation named Singh model has been developed to predict the tensile and flexural strength of this novel CSP reinforced epoxy composite. Moreover, high resolution Raman spectrum shows that 40 % CSP reinforced epoxy composite has high dielectric constant to become an alternative material for capacitance whereas fractured surface morphology revealed that a strong bonding between novel CSP and epoxy polymer for the application as light weight composite materials in engineering.

Computational Simulation of the High Strain Rate Tensile Response of Polymer Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.

2002-01-01

A research program is underway to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. Under these types of loading conditions, the material response can be highly strain rate dependent and nonlinear. State variable constitutive equations based on a viscoplasticity approach have been developed to model the deformation of the polymer matrix. The constitutive equations are then combined with a mechanics of materials based micromechanics model which utilizes fiber substructuring to predict the effective mechanical and thermal response of the composite. To verify the analytical model, tensile stress-strain curves are predicted for a representative composite over strain rates ranging from around 1 x 10(exp -5)/sec to approximately 400/sec. The analytical predictions compare favorably to experimentally obtained values both qualitatively and quantitatively. Effective elastic and thermal constants are predicted for another composite, and compared to finite element results.

Towards an Airframe Noise Prediction Methodology: Survey of Current Approaches

NASA Technical Reports Server (NTRS)

Farassat, Fereidoun; Casper, Jay H.

2006-01-01

In this paper, we present a critical survey of the current airframe noise (AFN) prediction methodologies. Four methodologies are recognized. These are the fully analytic method, CFD combined with the acoustic analogy, the semi-empirical method and fully numerical method. It is argued that for the immediate need of the aircraft industry, the semi-empirical method based on recent high quality acoustic database is the best available method. The method based on CFD and the Ffowcs William- Hawkings (FW-H) equation with penetrable data surface (FW-Hpds ) has advanced considerably and much experience has been gained in its use. However, more research is needed in the near future particularly in the area of turbulence simulation. The fully numerical method will take longer to reach maturity. Based on the current trends, it is predicted that this method will eventually develop into the method of choice. Both the turbulence simulation and propagation methods need to develop more for this method to become useful. Nonetheless, the authors propose that the method based on a combination of numerical and analytical techniques, e.g., CFD combined with FW-H equation, should also be worked on. In this effort, the current symbolic algebra software will allow more analytical approaches to be incorporated into AFN prediction methods.

Experimental and analytical investigation of a modified ring cusp NSTAR engine

NASA Technical Reports Server (NTRS)

Sengupta, Anita

2005-01-01

A series of experimental measurements on a modified laboratory NSTAR engine were used to validate a zero dimensional analytical discharge performance model of a ring cusp ion thruster. The model predicts the discharge performance of a ring cusp NSTAR thruster as a function the magnetic field configuration, thruster geometry, and throttle level. Analytical formalisms for electron and ion confinement are used to predict the ionization efficiency for a given thruster design. Explicit determination of discharge loss and volume averaged plasma parameters are also obtained. The model was used to predict the performance of the nominal and modified three and four ring cusp 30-cm ion thruster configurations operating at the full power (2.3 kW) NSTAR throttle level. Experimental measurements of the modified engine configuration discharge loss compare well with the predicted value for propellant utilizations from 80 to 95%. The theory, as validated by experiment, indicates that increasing the magnetic strength of the minimum closed reduces maxwellian electron diffusion and electrostatically confines the ion population and subsequent loss to the anode wall. The theory also indicates that increasing the cusp strength and minimizing the cusp area improves primary electron confinement increasing the probability of an ionization collision prior to loss at the cusp.

Coupled rotor/fuselage dynamic analysis of the AH-1G helicopter and correlation with flight vibrations data

NASA Technical Reports Server (NTRS)

Corrigan, J. C.; Cronkhite, J. D.; Dompka, R. V.; Perry, K. S.; Rogers, J. P.; Sadler, S. G.

1989-01-01

Under a research program designated Design Analysis Methods for VIBrationS (DAMVIBS), existing analytical methods are used for calculating coupled rotor-fuselage vibrations of the AH-1G helicopter for correlation with flight test data from an AH-1G Operational Load Survey (OLS) test program. The analytical representation of the fuselage structure is based on a NASTRAN finite element model (FEM), which has been developed, extensively documented, and correlated with ground vibration test. One procedure that was used for predicting coupled rotor-fuselage vibrations using the advanced Rotorcraft Flight Simulation Program C81 and NASTRAN is summarized. Detailed descriptions of the analytical formulation of rotor dynamics equations, fuselage dynamic equations, coupling between the rotor and fuselage, and solutions to the total system of equations in C81 are included. Analytical predictions of hub shears for main rotor harmonics 2p, 4p, and 6p generated by C81 are used in conjunction with 2p OLS measured control loads and a 2p lateral tail rotor gearbox force, representing downwash impingement on the vertical fin, to excite the NASTRAN model. NASTRAN is then used to correlate with measured OLS flight test vibrations. Blade load comparisons predicted by C81 showed good agreement. In general, the fuselage vibration correlations show good agreement between anslysis and test in vibration response through 15 to 20 Hz.

Time-varying output performances of piezoelectric vibration energy harvesting under nonstationary random vibrations

NASA Astrophysics Data System (ADS)

Yoon, Heonjun; Kim, Miso; Park, Choon-Su; Youn, Byeng D.

2018-01-01

Piezoelectric vibration energy harvesting (PVEH) has received much attention as a potential solution that could ultimately realize self-powered wireless sensor networks. Since most ambient vibrations in nature are inherently random and nonstationary, the output performances of PVEH devices also randomly change with time. However, little attention has been paid to investigating the randomly time-varying electroelastic behaviors of PVEH systems both analytically and experimentally. The objective of this study is thus to make a step forward towards a deep understanding of the time-varying performances of PVEH devices under nonstationary random vibrations. Two typical cases of nonstationary random vibration signals are considered: (1) randomly-varying amplitude (amplitude modulation; AM) and (2) randomly-varying amplitude with randomly-varying instantaneous frequency (amplitude and frequency modulation; AM-FM). In both cases, this study pursues well-balanced correlations of analytical predictions and experimental observations to deduce the relationships between the time-varying output performances of the PVEH device and two primary input parameters, such as a central frequency and an external electrical resistance. We introduce three correlation metrics to quantitatively compare analytical prediction and experimental observation, including the normalized root mean square error, the correlation coefficient, and the weighted integrated factor. Analytical predictions are in an excellent agreement with experimental observations both mechanically and electrically. This study provides insightful guidelines for designing PVEH devices to reliably generate electric power under nonstationary random vibrations.

A genetic algorithm-based job scheduling model for big data analytics.

PubMed

Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

Analyses of ACPL thermal/fluid conditioning system

NASA Technical Reports Server (NTRS)

Stephen, L. A.; Usher, L. H.

1976-01-01

Results of engineering analyses are reported. Initial computations were made using a modified control transfer function where the systems performance was characterized parametrically using an analytical model. The analytical model was revised to represent the latest expansion chamber fluid manifold design, and systems performance predictions were made. Parameters which were independently varied in these computations are listed. Systems predictions which were used to characterize performance are primarily transient computer plots comparing the deviation between average chamber temperature and the chamber temperature requirement. Additional computer plots were prepared. Results of parametric computations with the latest fluid manifold design are included.

Small-World Network Spectra in Mean-Field Theory

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

2012-05-01

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

Accommodating subject and instrument variations in spectroscopic determinations

DOEpatents

Haas, Michael J [Albuquerque, NM; Rowe, Robert K [Corrales, NM; Thomas, Edward V [Albuquerque, NM

2006-08-29

A method and apparatus for measuring a biological attribute, such as the concentration of an analyte, particularly a blood analyte in tissue such as glucose. The method utilizes spectrographic techniques in conjunction with an improved instrument-tailored or subject-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate instrument-specific attributes, resulting in a calibration data set modeling intra-instrument or intra-subject variation. In a prediction phase, the prediction process is tailored for each target instrument separately using a minimal number of spectral measurements from each instrument or subject.

KC-135 winglet program overview

NASA Technical Reports Server (NTRS)

Barber, M. R.; Selegan, D.

1982-01-01

A joint NASA/USAF program was conducted to accomplish the following objectives: (1) evaluate the benefits that could be achieved from the application of winglets to KC-135 aircraft; and (2) determine the ability of wind tunnel tests and analytical analysis to predict winglet characteristics. The program included wind-tunnel development of a test winglet configuration; analytical predictions of the changes to the aircraft resulting from the application of the test winglet; and finally, flight tests of the developed configuration. Pressure distribution, loads, stability and control, buffet, fuel mileage, and flutter data were obtained to fulfill the objectives of the program.

Analytical Modeling of Plasma Arc Cutting of Steel Plate

NASA Astrophysics Data System (ADS)

Cimbala, John; Fisher, Lance; Settles, Gary; Lillis, Milan

2000-11-01

A transferred-arc plasma torch cuts steel plate, and in the process ejects a molten stream of iron and ferrous oxides ("ejecta"). Under non-optimum conditions - especially during low speed cuts and/or small-radius corner cuts - "dross" is formed. Dross is re-solidified molten metal that sticks to the underside of the cut and renders it rough. The present research is an attempt to analytically model this process, with the goal of predicting dross formation. With the aid of experimental data, a control volume formulation is used in a steady frame of reference to predict the mass flow of molten material inside the cut. Although simple, the model is three-dimensional, can predict the shear stress driving the molten material in the direction of the plasma jet, and can predict the velocity of molten material exiting the bottom of the plate. In order to predict formation of dross, a momentum balance is performed on the flowing melt, considering the resisting viscous and surface tension forces. Preliminary results are promising, and provide a potential means of predicting dross formation without resorting to detailed computational analyses.

Galaxy Formation At Extreme Redshifts: Semi-Analytic Model Predictions And Challenges For Observations

NASA Astrophysics Data System (ADS)

Yung, L. Y. Aaron; Somerville, Rachel S.

2017-06-01

The well-established Santa Cruz semi-analytic galaxy formation framework has been shown to be quite successful at explaining observations in the local Universe, as well as making predictions for low-redshift observations. Recently, metallicity-based gas partitioning and H2-based star formation recipes have been implemented in our model, replacing the legacy cold-gas based recipe. We then use our revised model to explore the high-redshift Universe and make predictions up to z = 15. Although our model is only calibrated to observations from the local universe, our predictions seem to match incredibly well with mid- to high-redshift observational constraints available-to-date, including rest-frame UV luminosity functions and the reionization history as constrained by CMB and IGM observations. We provide predictions for individual and statistical galaxy properties at a wide range of redshifts (z = 4 - 15), including objects that are too far or too faint to be detected with current facilities. And using our model predictions, we also provide forecasted luminosity functions and other observables for upcoming studies with JWST.

Modeling microelectrode biosensors: free-flow calibration can substantially underestimate tissue concentrations

PubMed Central

Wall, Mark J.

2016-01-01

Microelectrode amperometric biosensors are widely used to measure concentrations of analytes in solution and tissue including acetylcholine, adenosine, glucose, and glutamate. A great deal of experimental and modeling effort has been directed at quantifying the response of the biosensors themselves; however, the influence that the macroscopic tissue environment has on biosensor response has not been subjected to the same level of scrutiny. Here we identify an important issue in the way microelectrode biosensors are calibrated that is likely to have led to underestimations of analyte tissue concentrations. Concentration in tissue is typically determined by comparing the biosensor signal to that measured in free-flow calibration conditions. In a free-flow environment the concentration of the analyte at the outer surface of the biosensor can be considered constant. However, in tissue the analyte reaches the biosensor surface by diffusion through the extracellular space. Because the enzymes in the biosensor break down the analyte, a density gradient is set up resulting in a significantly lower concentration of analyte near the biosensor surface. This effect is compounded by the diminished volume fraction (porosity) and reduction in the diffusion coefficient due to obstructions (tortuosity) in tissue. We demonstrate this effect through modeling and experimentally verify our predictions in diffusive environments. NEW & NOTEWORTHY Microelectrode biosensors are typically calibrated in a free-flow environment where the concentrations at the biosensor surface are constant. However, when in tissue, the analyte reaches the biosensor via diffusion and so analyte breakdown by the biosensor results in a concentration gradient and consequently a lower concentration around the biosensor. This effect means that naive free-flow calibration will underestimate tissue concentration. We develop mathematical models to better quantify the discrepancy between the calibration and tissue environment and experimentally verify our key predictions. PMID:27927788

Modeling microelectrode biosensors: free-flow calibration can substantially underestimate tissue concentrations.

PubMed

Newton, Adam J H; Wall, Mark J; Richardson, Magnus J E

2017-03-01

Microelectrode amperometric biosensors are widely used to measure concentrations of analytes in solution and tissue including acetylcholine, adenosine, glucose, and glutamate. A great deal of experimental and modeling effort has been directed at quantifying the response of the biosensors themselves; however, the influence that the macroscopic tissue environment has on biosensor response has not been subjected to the same level of scrutiny. Here we identify an important issue in the way microelectrode biosensors are calibrated that is likely to have led to underestimations of analyte tissue concentrations. Concentration in tissue is typically determined by comparing the biosensor signal to that measured in free-flow calibration conditions. In a free-flow environment the concentration of the analyte at the outer surface of the biosensor can be considered constant. However, in tissue the analyte reaches the biosensor surface by diffusion through the extracellular space. Because the enzymes in the biosensor break down the analyte, a density gradient is set up resulting in a significantly lower concentration of analyte near the biosensor surface. This effect is compounded by the diminished volume fraction (porosity) and reduction in the diffusion coefficient due to obstructions (tortuosity) in tissue. We demonstrate this effect through modeling and experimentally verify our predictions in diffusive environments. NEW & NOTEWORTHY Microelectrode biosensors are typically calibrated in a free-flow environment where the concentrations at the biosensor surface are constant. However, when in tissue, the analyte reaches the biosensor via diffusion and so analyte breakdown by the biosensor results in a concentration gradient and consequently a lower concentration around the biosensor. This effect means that naive free-flow calibration will underestimate tissue concentration. We develop mathematical models to better quantify the discrepancy between the calibration and tissue environment and experimentally verify our key predictions. Copyright © 2017 the American Physiological Society.

Digital Analytics in Professional Work and Learning

ERIC Educational Resources Information Center

Edwards, Richard; Fenwick, Tara

2016-01-01

In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to…

Do the Critical Success Factors from Learning Analytics Predict Student Outcomes?

ERIC Educational Resources Information Center

Strang, Kenneth David

2016-01-01

This article starts with a detailed literature review of recent studies that focused on using learning analytics software or learning management system data to determine the nature of any relationships between online student activity and their academic outcomes within university-level business courses. The article then describes how data was…

Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

ERIC Educational Resources Information Center

Firat, Mehmet

2016-01-01

Two of the most important outcomes of learning analytics are predicting students' learning and providing effective feedback. Learning Management Systems (LMS), which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users' behaviors. The purpose…

Web Analytics Reveal User Behavior: TTU Libraries' Experience with Google Analytics

ERIC Educational Resources Information Center

Barba, Ian; Cassidy, Ryan; De Leon, Esther; Williams, B. Justin

2013-01-01

Proper planning and assessment surveys of projects for academic library Web sites will not always be predictive of real world use, no matter how many responses they might receive. In this case, multiple-phase development, librarian focus groups, and patron surveys performed before implementation of such a project inaccurately overrated utility and…

Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

ERIC Educational Resources Information Center

Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H.

2018-01-01

Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…

Using Keystroke Analytics to Improve Pass-Fail Classifiers

ERIC Educational Resources Information Center

Casey, Kevin

2017-01-01

Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…

Evidence for Holistic Representations of Ignored Images and Analytic Representations of Attended Images

ERIC Educational Resources Information Center

Thoma, Volker; Hummel, John E.; Davidoff, Jules

2004-01-01

According to the hybrid theory of object recognition (J. E. Hummel, 2001), ignored object images are represented holistically, and attended images are represented both holistically and analytically. This account correctly predicts patterns of visual priming as a function of translation, scale (B. J. Stankiewicz & J. E. Hummel, 2002), and…

Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective

ERIC Educational Resources Information Center

Dietz-Uhler, Beth; Hurn, Janet E.

2013-01-01

Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics…

An analytical method for designing low noise helicopter transmissions

NASA Technical Reports Server (NTRS)

Bossler, R. B., Jr.; Bowes, M. A.; Royal, A. C.

1978-01-01

The development and experimental validation of a method for analytically modeling the noise mechanism in the helicopter geared power transmission systems is described. This method can be used within the design process to predict interior noise levels and to investigate the noise reducing potential of alternative transmission design details. Examples are discussed.

Experimental and analytical characterization of triaxially braided textile composites

NASA Technical Reports Server (NTRS)

Masters, John E.; Fedro, Mark J.; Ifju, Peter G.

1993-01-01

There were two components, experimental and analytical, to this investigation of triaxially braided textile composite materials. The experimental portion of the study centered on measuring the materials' longitudinal and transverse tensile moduli, Poisson's ratio, and strengths. The identification of the damage mechanisms exhibited by these materials was also a prime objective of the experimental investigation. The analytical portion of the investigation utilized the Textile Composites Analysis (TECA) model to predict modulus and strength. The analytical and experimental results were compared to assess the effectiveness of the analysis. The figures contained in this paper reflect the presentation made at the conference. They may be divided into four sections: a definition of the material system tested; followed by a series of figures summarizing the experimental results (these figures contain results of a Moire interferometry study of the strain distribution in the material, examples and descriptions of the types of damage encountered in these materials, and a summary of the measured properties); a description of the TECA model follows the experimental results (this includes a series of predicted results and a comparison with measured values); and finally, a brief summary completes the paper.

Development of airframe design technology for crashworthiness.

NASA Technical Reports Server (NTRS)

Kruszewski, E. T.; Thomson, R. G.

1973-01-01

This paper describes the NASA portion of a joint FAA-NASA General Aviation Crashworthiness Program leading to the development of improved crashworthiness design technology. The objectives of the program are to develop analytical technology for predicting crashworthiness of structures, provide design improvements, and perform full-scale crash tests. The analytical techniques which are being developed both in-house and under contract are described, and typical results from these analytical programs are shown. In addition, the full-scale testing facility and test program are discussed.

Analytical determination of thermal conductivity of W-UO2 and W-UN CERMET nuclear fuels

NASA Astrophysics Data System (ADS)

Webb, Jonathan A.; Charit, Indrajit

2012-08-01

The thermal conductivity of tungsten based CERMET fuels containing UO2 and UN fuel particles are determined as a function of particle geometry, stabilizer fraction and fuel-volume fraction, by using a combination of an analytical approach and experimental data collected from literature. Thermal conductivity is estimated using the Bruggeman-Fricke model. This study demonstrates that thermal conductivities of various CERMET fuels can be analytically predicted to values that are very close to the experimentally determined ones.

Analytical and numerical techniques for predicting the interfacial stresses of wavy carbon nanotube/polymer composites

NASA Astrophysics Data System (ADS)

Yazdchi, K.; Salehi, M.; Shokrieh, M. M.

2009-03-01

By introducing a new simplified 3D representative volume element for wavy carbon nanotubes, an analytical model is developed to study the stress transfer in single-walled carbon nanotube-reinforced polymer composites. Based on the pull-out modeling technique, the effects of waviness, aspect ratio, and Poisson ratio on the axial and interfacial shear stresses are analyzed in detail. The results of the present analytical model are in a good agreement with corresponding results for straight nanotubes.

Net analyte signal standard addition method (NASSAM) as a novel spectrofluorimetric and spectrophotometric technique for simultaneous determination, application to assay of melatonin and pyridoxine

NASA Astrophysics Data System (ADS)

Asadpour-Zeynali, Karim; Bastami, Mohammad

2010-02-01

In this work a new modification of the standard addition method called "net analyte signal standard addition method (NASSAM)" is presented for the simultaneous spectrofluorimetric and spectrophotometric analysis. The proposed method combines the advantages of standard addition method with those of net analyte signal concept. The method can be applied for the determination of analyte in the presence of known interferents. The accuracy of the predictions against H-point standard addition method is not dependent on the shape of the analyte and interferent spectra. The method was successfully applied to simultaneous spectrofluorimetric and spectrophotometric determination of pyridoxine (PY) and melatonin (MT) in synthetic mixtures and in a pharmaceutical formulation.

An analytical and experimental evaluation of a Fresnel lens solar concentrator

NASA Technical Reports Server (NTRS)

Hastings, L. J.; Allums, S. A.; Cosby, R. M.

1976-01-01

An analytical and experimental evaluation of line focusing Fresnel lenses with application potential in the 200 to 370 C range was studied. Analytical techniques were formulated to assess the solar transmission and imaging properties of a grooves down lens. Experimentation was based on a 56 cm wide, f/1.0 lens. A Sun tracking heliostat provided a nonmoving solar source. Measured data indicated more spreading at the profile base than analytically predicted, resulting in a peak concentration 18 percent lower than the computed peak of 57. The measured and computed transmittances were 85 and 87 percent, respectively. Preliminary testing with a subsequent lens indicated that modified manufacturing techniques corrected the profile spreading problem and should enable improved analytical experimental correlation.

An analytical technique for predicting the characteristics of a flexible wing equipped with an active flutter-suppression system and comparison with wind-tunnel data

NASA Technical Reports Server (NTRS)

Abel, I.

1979-01-01

An analytical technique for predicting the performance of an active flutter-suppression system is presented. This technique is based on the use of an interpolating function to approximate the unsteady aerodynamics. The resulting equations are formulated in terms of linear, ordinary differential equations with constant coefficients. This technique is then applied to an aeroelastic model wing equipped with an active flutter-suppression system. Comparisons between wind-tunnel data and analysis are presented for the wing both with and without active flutter suppression. Results indicate that the wing flutter characteristics without flutter suppression can be predicted very well but that a more adequate model of wind-tunnel turbulence is required when the active flutter-suppression system is used.

Precession and circularization of elliptical space-tether motion

NASA Technical Reports Server (NTRS)

Chapel, Jim D.; Grosserode, Patrick

1993-01-01

In this paper, we present a simplified analytic model for predicting motion of long space tethers. The perturbation model developed here addresses skip rope motion, where each end of the tether is held in place and the middle of the tether swings with a motion similar to that of a child's skip rope. If the motion of the tether midpoint is elliptical rather than circular, precession of the ellipse complicates the procedures required to damp this motion. The simplified analytic model developed in this paper parametrically predicts the precession of elliptical skip rope motion. Furthermore, the model shows that elliptic skip rope motion will circularize when damping is present in the longitudinal direction. Compared with high-fidelity simulation results, this simplified model provides excellent predictions of these phenomena.

Potential energy surface and vibrational band origins of the triatomic lithium cation

NASA Astrophysics Data System (ADS)

Searles, Debra J.; Dunne, Simon J.; von Nagy-Felsobuki, Ellak I.

The 104 point CISD Li +3 potential energy surface and its analytical representation is reported. The calculations predict the minimum energy geometry to be an equilateral triangle of side RLiLi = 3.0 Å and of energy - 22.20506 E h. A fifth-order Morse—Dunham type analytical force field is used in the Carney—Porter normal co-ordinate vibrational Hamiltonian, the corresponding eigenvalue problem being solved variationally using a 560 configurational finite-element basis set. The predicted assignment of the vibrational band origins is in accord with that reported for H +3. Moreover, for 6Li +3 and 7Li +3 the lowest i.r. accessible band origin is the overlineν0,1,±1 predicted to be at 243.6 and 226.0 cm -1 respectively.

Rotor/Wing Interactions in Hover

NASA Technical Reports Server (NTRS)

Young, Larry A.; Derby, Michael R.

2002-01-01

Hover predictions of tiltrotor aircraft are hampered by the lack of accurate and computationally efficient models for rotor/wing interactional aerodynamics. This paper summarizes the development of an approximate, potential flow solution for the rotor-on-rotor and wing-on-rotor interactions. This analysis is based on actuator disk and vortex theory and the method of images. The analysis is applicable for out-of-ground-effect predictions. The analysis is particularly suited for aircraft preliminary design studies. Flow field predictions from this simple analytical model are validated against experimental data from previous studies. The paper concludes with an analytical assessment of the influence of rotor-on-rotor and wing-on-rotor interactions. This assessment examines the effect of rotor-to-wing offset distance, wing sweep, wing span, and flaperon incidence angle on tiltrotor inflow and performance.

An empirical model for calculation of the collimator contamination dose in therapeutic proton beams

NASA Astrophysics Data System (ADS)

Vidal, M.; De Marzi, L.; Szymanowski, H.; Guinement, L.; Nauraye, C.; Hierso, E.; Freud, N.; Ferrand, R.; François, P.; Sarrut, D.

2016-02-01

Collimators are used as lateral beam shaping devices in proton therapy with passive scattering beam lines. The dose contamination due to collimator scattering can be as high as 10% of the maximum dose and influences calculation of the output factor or monitor units (MU). To date, commercial treatment planning systems generally use a zero-thickness collimator approximation ignoring edge scattering in the aperture collimator and few analytical models have been proposed to take scattering effects into account, mainly limited to the inner collimator face component. The aim of this study was to characterize and model aperture contamination by means of a fast and accurate analytical model. The entrance face collimator scatter distribution was modeled as a 3D secondary dose source. Predicted dose contaminations were compared to measurements and Monte Carlo simulations. Measurements were performed on two different proton beam lines (a fixed horizontal beam line and a gantry beam line) with divergent apertures and for several field sizes and energies. Discrepancies between analytical algorithm dose prediction and measurements were decreased from 10% to 2% using the proposed model. Gamma-index (2%/1 mm) was respected for more than 90% of pixels. The proposed analytical algorithm increases the accuracy of analytical dose calculations with reasonable computation times.

Choice Defines Value: A Predictive Modeling Competition in Health Preference Research.

PubMed

Jakubczyk, Michał; Craig, Benjamin M; Barra, Mathias; Groothuis-Oudshoorn, Catharina G M; Hartman, John D; Huynh, Elisabeth; Ramos-Goñi, Juan M; Stolk, Elly A; Rand, Kim

2018-02-01

To identify which specifications and approaches to model selection better predict health preferences, the International Academy of Health Preference Research (IAHPR) hosted a predictive modeling competition including 18 teams from around the world. In April 2016, an exploratory survey was fielded: 4074 US respondents completed 20 out of 1560 paired comparisons by choosing between two health descriptions (e.g., longer life span vs. better health). The exploratory data were distributed to all teams. By July, eight teams had submitted their predictions for 1600 additional pairs and described their analytical approach. After these predictions had been posted online, a confirmatory survey was fielded (4148 additional respondents). The victorious team, "Discreetly Charming Econometricians," led by Michał Jakubczyk, achieved the smallest χ 2 , 4391.54 (a predefined criterion). Its primary scientific findings were that different models performed better with different pairs, that the value of life span is not constant proportional, and that logit models have poor predictive validity in health valuation. The results demonstrated the diversity and potential of new analytical approaches in health preference research and highlighted the importance of predictive validity in health valuation. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

An implementation of an aeroacoustic prediction model for broadband noise from a vertical axis wind turbine using a CFD informed methodology

NASA Astrophysics Data System (ADS)

Botha, J. D. M.; Shahroki, A.; Rice, H.

2017-12-01

This paper presents an enhanced method for predicting aerodynamically generated broadband noise produced by a Vertical Axis Wind Turbine (VAWT). The method improves on existing work for VAWT noise prediction and incorporates recently developed airfoil noise prediction models. Inflow-turbulence and airfoil self-noise mechanisms are both considered. Airfoil noise predictions are dependent on aerodynamic input data and time dependent Computational Fluid Dynamics (CFD) calculations are carried out to solve for the aerodynamic solution. Analytical flow methods are also benchmarked against the CFD informed noise prediction results to quantify errors in the former approach. Comparisons to experimental noise measurements for an existing turbine are encouraging. A parameter study is performed and shows the sensitivity of overall noise levels to changes in inflow velocity and inflow turbulence. Noise sources are characterised and the location and mechanism of the primary sources is determined, inflow-turbulence noise is seen to be the dominant source. The use of CFD calculations is seen to improve the accuracy of noise predictions when compared to the analytic flow solution as well as showing that, for inflow-turbulence noise sources, blade generated turbulence dominates the atmospheric inflow turbulence.

Formal and physical equivalence in two cases in contemporary quantum physics

NASA Astrophysics Data System (ADS)

Fraser, Doreen

2017-08-01

The application of analytic continuation in quantum field theory (QFT) is juxtaposed to T-duality and mirror symmetry in string theory. Analytic continuation-a mathematical transformation that takes the time variable t to negative imaginary time-it-was initially used as a mathematical technique for solving perturbative Feynman diagrams, and was subsequently the basis for the Euclidean approaches within mainstream QFT (e.g., Wilsonian renormalization group methods, lattice gauge theories) and the Euclidean field theory program for rigorously constructing non-perturbative models of interacting QFTs. A crucial difference between theories related by duality transformations and those related by analytic continuation is that the former are judged to be physically equivalent while the latter are regarded as physically inequivalent. There are other similarities between the two cases that make comparing and contrasting them a useful exercise for clarifying the type of argument that is needed to support the conclusion that dual theories are physically equivalent. In particular, T-duality and analytic continuation in QFT share the criterion for predictive equivalence that two theories agree on the complete set of expectation values and the mass spectra and the criterion for formal equivalence that there is a "translation manual" between the physically significant algebras of observables and sets of states in the two theories. The analytic continuation case study illustrates how predictive and formal equivalence are compatible with physical inequivalence, but not in the manner of standard underdetermination cases. Arguments for the physical equivalence of dual theories must cite considerations beyond predictive and formal equivalence. The analytic continuation case study is an instance of the strategy of developing a physical theory by extending the formal or mathematical equivalence with another physical theory as far as possible. That this strategy has resulted in developments in pure mathematics as well as theoretical physics is another feature that this case study has in common with dualities in string theory.

An experimental and analytical investigation of isolated rotor flap-lag stability in forward flight

NASA Technical Reports Server (NTRS)

Gaonkar, Gopal H.; Mcnulty, Michael J.

1985-01-01

For flap-lag stability of isolated rotors, experimental and analytical investigations are conducted in hover and forward flight on the adequacy of a linear quasi-steady aerodynamics theory with dynamic inflow. Forward flight effects on lag regressing mode are emphasized. Accordingly, a soft inplane hingeless rotor with three blades is tested at advance ratios as high as 0.55 and at shaft angles as high as 20 deg. The 1.62-m model rotor is untrimmed with an essentially unrestricted tilt of the tip path plane. By computerized symbolic manipulation, an analytical model is developed in substall to predict stability margins with mode indentification. It also predicts substall and stall regions to help explain the correlation between theory and data. The correlation shows both the strengths and weaknesses of the data and theory, and promotes further insights into areas in which further study is needed in substall and stall.

Three-Dimensional Dynamic Deformation Measurements Using Stereoscopic Imaging and Digital Speckle Photography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prentice, H. J.; Proud, W. G.

2006-07-28

A technique has been developed to determine experimentally the three-dimensional displacement field on the rear surface of a dynamically deforming plate. The technique combines speckle analysis with stereoscopy, using a modified angular-lens method: this incorporates split-frame photography and a simple method by which the effective lens separation can be adjusted and calibrated in situ. Whilst several analytical models exist to predict deformation in extended or semi-infinite targets, the non-trivial nature of the wave interactions complicates the generation and development of analytical models for targets of finite depth. By interrogating specimens experimentally to acquire three-dimensional strain data points, both analytical andmore » numerical model predictions can be verified more rigorously. The technique is applied to the quasi-static deformation of a rubber sheet and dynamically to Mild Steel sheets of various thicknesses.« less

Two-dimensional numerical simulation of a Stirling engine heat exchanger

NASA Technical Reports Server (NTRS)

Ibrahim, Mounir B.; Tew, Roy C.; Dudenhoefer, James E.

1989-01-01

The first phase of an effort to develop multidimensional models of Stirling engine components is described; the ultimate goal is to model an entire engine working space. More specifically, parallel plate and tubular heat exchanger models with emphasis on the central part of the channel (i.e., ignoring hydrodynamic and thermal end effects) are described. The model assumes: laminar, incompressible flow with constant thermophysical properties. In addition, a constant axial temperature gradient is imposed. The governing equations, describing the model, were solved using Crank-Nicloson finite-difference scheme. Model predictions were compared with analytical solutions for oscillating/reversing flow and heat transfer in order to check numerical accuracy. Excellent agreement was obtained for the model predictions with analytical solutions available for both flow in circular tubes and between parallel plates. Also the heat transfer computational results are in good agreement with the heat transfer analytical results for parallel plates.

Shape Phase Transition from Octupole Deformation to Octupole Vibrations: The Analytic Quadrupole Octupole Axially Symmetric Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonatsos, Dennis; Lenis, D.; Petrellis, D.

An analytic collective model in which the relative presence of the quadrupole and octupole deformations is determined by a parameter ({phi}0), while axial symmetry is obeyed, is developed. The model [to be called the Analytic Quadrupole Octupole Axially symmetric model (AQOA)] involves an infinite well potential, provides predictions for energy and B(EL) ratios which depend only on {phi}0, draws the border between the regions of octupole deformation and octupole vibrations in an essentially parameter-independent way, and in the actinide region describes well 226Th and 226Ra, for which experimental energy data are shown to suggest that they lie close to thismore » border. The similarity of the AQOA results with {phi}0 = 45 deg. for ground state band spectra and B(E2) transition rates to the predictions of the X(5) model is pointed out.« less

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics.

PubMed

Ni, Yongnian; Wang, Lin; Kokot, Serge

2011-01-01

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D - first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.

Experimental, Numerical, and Analytical Slosh Dynamics of Water and Liquid Nitrogen in a Spherical Tank

NASA Technical Reports Server (NTRS)

Storey, Jedediah Morse

2016-01-01

Understanding, predicting, and controlling fluid slosh dynamics is critical to safety and improving performance of space missions when a significant percentage of the spacecraft's mass is a liquid. Computational fluid dynamics simulations can be used to predict the dynamics of slosh, but these programs require extensive validation. Many experimental and numerical studies of water slosh have been conducted. However, slosh data for cryogenic liquids is lacking. Water and cryogenic liquid nitrogen are used in various ground-based tests with a spherical tank to characterize damping, slosh mode frequencies, and slosh forces. A single ring baffle is installed in the tank for some of the tests. Analytical models for slosh modes, slosh forces, and baffle damping are constructed based on prior work. Select experiments are simulated using a commercial CFD software, and the numerical results are compared to the analytical and experimental results for the purposes of validation and methodology-improvement.

3D analysis of eddy current loss in the permanent magnet coupling.

PubMed

Zhu, Zina; Meng, Zhuo

2016-07-01

This paper first presents a 3D analytical model for analyzing the radial air-gap magnetic field between the inner and outer magnetic rotors of the permanent magnet couplings by using the Amperian current model. Based on the air-gap field analysis, the eddy current loss in the isolation cover is predicted according to the Maxwell's equations. A 3D finite element analysis model is constructed to analyze the magnetic field spatial distributions and vector eddy currents, and then the simulation results obtained are analyzed and compared with the analytical method. Finally, the current losses of two types of practical magnet couplings are measured in the experiment to compare with the theoretical results. It is concluded that the 3D analytical method of eddy current loss in the magnet coupling is viable and could be used for the eddy current loss prediction of magnet couplings.

A methodology to enhance electromagnetic compatibility in joint military operations

NASA Astrophysics Data System (ADS)

Buckellew, William R.

The development and validation of an improved methodology to identify, characterize, and prioritize potential joint EMI (electromagnetic interference) interactions and identify and develop solutions to reduce the effects of the interference are discussed. The methodology identifies potential EMI problems using results from field operations, historical data bases, and analytical modeling. Operational expertise, engineering analysis, and testing are used to characterize and prioritize the potential EMI problems. Results can be used to resolve potential EMI during the development and acquisition of new systems and to develop engineering fixes and operational workarounds for systems already employed. The analytic modeling portion of the methodology is a predictive process that uses progressive refinement of the analysis and the operational electronic environment to eliminate noninterfering equipment pairs, defer further analysis on pairs lacking operational significance, and resolve the remaining EMI problems. Tests are conducted on equipment pairs to ensure that the analytical models provide a realistic description of the predicted interference.

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer.

PubMed

Coates, James; Jeyaseelan, Asha K; Ybarra, Norma; David, Marc; Faria, Sergio; Souhami, Luis; Cury, Fabio; Duclos, Marie; El Naqa, Issam

2015-04-01

We explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients. Sixty-two patients who underwent curative hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman-Kutcher-Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data. Integration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models. As a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Solution of magnetic field and eddy current problem induced by rotating magnetic poles (abstract)

NASA Astrophysics Data System (ADS)

Liu, Z. J.; Low, T. S.

1996-04-01

The magnetic field and eddy current problems induced by rotating permanent magnet poles occur in electromagnetic dampers, magnetic couplings, and many other devices. Whereas numerical techniques, for example, finite element methods can be exploited to study various features of these problems, such as heat generation and drag torque development, etc., the analytical solution is always of interest to the designers since it helps them to gain the insight into the interdependence of the parameters involved and provides an efficient tool for designing. Some of the previous work showed that the solution of the eddy current problem due to the linearly moving magnet poles can give satisfactory approximation for the eddy current problem due to rotating fields. However, in many practical cases, especially when the number of magnet poles is small, there is significant effect of flux focusing due to the geometry. The above approximation can therefore lead to marked errors in the theoretical predictions of the device performance. Bernot et al. recently described an analytical solution in a polar coordinate system where the radial field is excited by a time-varying source. A discussion of an analytical solution of the magnetic field and eddy current problems induced by moving magnet poles in radial field machines will be given in this article. The theoretical predictions obtained from this method is compared with the results obtained from finite element calculations. The validity of the method is also checked by the comparison of the theoretical predictions and the measurements from a test machine. It is shown that the introduced solution leads to a significant improvement in the air gap field prediction as compared with the results obtained from the analytical solution that models the eddy current problems induced by linearly moving magnet poles.

Neoclassical toroidal viscosity calculations in tokamaks using a δf Monte Carlo simulation and their verifications.

PubMed

Satake, S; Park, J-K; Sugama, H; Kanno, R

2011-07-29

Neoclassical toroidal viscosities (NTVs) in tokamaks are investigated using a δf Monte Carlo simulation, and are successfully verified with a combined analytic theory over a wide range of collisionality. A Monte Carlo simulation has been required in the study of NTV since the complexities in guiding-center orbits of particles and their collisions cannot be fully investigated by any means of analytic theories alone. Results yielded the details of the complex NTV dependency on particle precessions and collisions, which were predicted roughly in a combined analytic theory. Both numerical and analytic methods can be utilized and extended based on these successful verifications.

Predicted range expansion of Chinese tallow tree (Triadica sebifera) in forestlands of the southern United States

Treesearch

Hsiao-Hsuan Wang; William Grant; Todd Swannack; Jianbang Gan; William Rogers; Tomasz Koralewski; James Miller; John W. Taylor Jr.

2011-01-01

We present an integrated approach for predicting future range expansion of an invasive species (Chinese tallow tree) that incorporates statistical forecasting and analytical techniques within a spatially explicit, agent-based, simulation framework.

Thermal induced flow oscillations in heat exchangers for supercritical fluids

NASA Technical Reports Server (NTRS)

Friedly, J. C.; Manganaro, J. L.; Krueger, P. G.

1972-01-01

Analytical model has been developed to predict possible unstable behavior in supercritical heat exchangers. From complete model, greatly simplified stability criterion is derived. As result of this criterion, stability of heat exchanger system can be predicted in advance.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

NASA Astrophysics Data System (ADS)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling bicortical screws under a cantilever bending load.

PubMed

James, Thomas P; Andrade, Brendan A

2013-12-01

Cyclic loading of surgical plating constructs can precipitate bone screw failure. As the frictional contact between the plate and the bone is lost, cantilever bending loads are transferred from the plate to the head of the screw, which over time causes fatigue fracture from cyclic bending. In this research, analytical models using beam mechanics theory were developed to describe the elastic deflection of a bicortical screw under a statically applied load. Four analytical models were developed to simulate the various restraint conditions applicable to bicortical support of the screw. In three of the models, the cortical bone near the tip of the screw was simulated by classical beam constraints (1) simply supported, (2) cantilever, and (3) split distributed load. In the final analytical model, the cortices were treated as an elastic foundation, whereby the response of the constraint was proportional to screw deflection. To test the predictive ability of the new analytical models, 3.5 mm cortical bone screws were tested in a synthetic bone substitute. A novel instrument was developed to measure the bending deflection of screws under radial loads (225 N, 445 N, and 670 N) applied by a surrogate surgical plate at the head of the screw. Of the four cases considered, the analytical model utilizing an elastic foundation most accurately predicted deflection at the screw head, with an average difference of 19% between the measured and predicted results. Determination of the bending moments from the elastic foundation model revealed that a maximum moment of 2.3 N m occurred near the middle of the cortical wall closest to the plate. The location of the maximum bending moment along the screw axis was consistent with the fracture location commonly observed in clinical practice.

The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

ERIC Educational Resources Information Center

Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

2010-01-01

Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

Combustion of Nitramine Propellants

DTIC Science & Technology

1983-03-01

through development of a comprehensive analytical model. The ultimate goals are to enable prediction of deflagration rate over a wide pressure range...superior in burn rate prediction , both simple models fail in correlating existing temperature- sensitivity data. (2) In the second part, a...auxiliary condition to enable independent burn rate prediction ; improved melt phase model including decomposition-gas bubbles; model for far-field

Bellows flow-induced vibrations

NASA Technical Reports Server (NTRS)

Tygielski, P. J.; Smyly, H. M.; Gerlach, C. R.

1983-01-01

The bellows flow excitation mechanism and results of comprehensive test program are summarized. The analytical model for predicting bellows flow induced stress is refined. The model includes the effects of an upstream elbow, arbitrary geometry, and multiple piles. A refined computer code for predicting flow induced stress is described which allows life prediction if a material S-N diagram is available.

Analytical Modeling of Groundwater Seepages to St. Lucie Estuary

NASA Astrophysics Data System (ADS)

Lee, J.; Yeh, G.; Hu, G.

2008-12-01

In this paper, six analytical models describing hydraulic interaction of stream-aquifer systems were applied to St Lucie Estuary (SLE) River Estuaries. These are analytical solutions for: (1) flow from a finite aquifer to a canal, (2) flow from an infinite aquifer to a canal, (3) the linearized Laplace system in a seepage surface, (4) wave propagation in the aquifer, (5) potential flow through stratified unconfined aquifers, and (6) flow through stratified confined aquifers. Input data for analytical solutions were obtained from monitoring wells and river stages at seepage-meter sites. Four transects in the study area are available: Club Med, Harbour Ridge, Lutz/MacMillan, and Pendarvis Cove located in the St. Lucie River. The analytical models were first calibrated with seepage meter measurements and then used to estimate of groundwater discharges into St. Lucie River. From this process, analytical relationships between the seepage rate and river stages and/or groundwater tables were established to predict the seasonal and monthly variation in groundwater seepage into SLE. It was found the seepage rate estimations by analytical models agreed well with measured data for some cases but only fair for some other cases. This is not unexpected because analytical solutions have some inherently simplified assumptions, which may be more valid for some cases than the others. From analytical calculations, it is possible to predict approximate seepage rates in the study domain when the assumptions underlying these analytical models are valid. The finite and infinite aquifer models and the linearized Laplace method are good for sites Pendarvis Cove and Lutz/MacMillian, but fair for the other two sites. The wave propagation model gave very good agreement in phase but only fairly agreement in magnitude for all four sites. The stratified unconfined and confined aquifer models gave similarly good agreements with measurements at three sites but poorly at the Club Med site. None of the analytical models presented here can fit the data at this site. To give better estimates at all sites numerical modeling that couple river hydraulics and groundwater flow involving less simplifications of and assumptions for the system may have to be adapted.

Aeroelastic Analysis for Rotorcraft

NASA Technical Reports Server (NTRS)

Johnson, W.

1982-01-01

Aeroelastic-analysis computer program incorporates an analytical model of aeroelastic behavior of wide range of rotorcraft. Such an analytical model is desirable for both pretest predictions and posttest correlations. Program can be applied in investigations of isolated rotor aeroelasticity and helicopter-flight dynamics and could be employed as basis for more-extensive investigations or aeroelastic behavior, such as automatic control system design.

Automotive Design

NASA Technical Reports Server (NTRS)

1991-01-01

Analytical Design Service Corporation, Ann Arbor, MI, used NASTRAN (a NASA Structural Analysis program that analyzes a design and predicts how parts will perform) in tests of transmissions, engine cooling systems, internal engine parts, and body components. They also use it to design future automobiles. Analytical software can save millions by allowing computer simulated analysis of performance even before prototypes are built.

A Meta-Analytic Review of Components Associated with Parent Training Program Effectiveness

ERIC Educational Resources Information Center

Kaminski, Jennifer Wyatt; Valle, Linda Anne; Filene, Jill H.; Boyle, Cynthia L.

2008-01-01

This component analysis used meta-analytic techniques to synthesize the results of 77 published evaluations of parent training programs (i.e., programs that included the active acquisition of parenting skills) to enhance behavior and adjustment in children aged 0-7. Characteristics of program content and delivery method were used to predict effect…

Comparison of analytical and predictive methods for water, protein, fat, sugar, and gross energy in marine mammal milk.

PubMed

Oftedal, O T; Eisert, R; Barrell, G K

2014-01-01

Mammalian milks may differ greatly in composition from cow milk, and these differences may affect the performance of analytical methods. High-fat, high-protein milks with a preponderance of oligosaccharides, such as those produced by many marine mammals, present a particular challenge. We compared the performance of several methods against reference procedures using Weddell seal (Leptonychotes weddellii) milk of highly varied composition (by reference methods: 27-63% water, 24-62% fat, 8-12% crude protein, 0.5-1.8% sugar). A microdrying step preparatory to carbon-hydrogen-nitrogen (CHN) gas analysis slightly underestimated water content and had a higher repeatability relative standard deviation (RSDr) than did reference oven drying at 100°C. Compared with a reference macro-Kjeldahl protein procedure, the CHN (or Dumas) combustion method had a somewhat higher RSDr (1.56 vs. 0.60%) but correlation between methods was high (0.992), means were not different (CHN: 17.2±0.46% dry matter basis; Kjeldahl 17.3±0.49% dry matter basis), there were no significant proportional or constant errors, and predictive performance was high. A carbon stoichiometric procedure based on CHN analysis failed to adequately predict fat (reference: Röse-Gottlieb method) or total sugar (reference: phenol-sulfuric acid method). Gross energy content, calculated from energetic factors and results from reference methods for fat, protein, and total sugar, accurately predicted gross energy as measured by bomb calorimetry. We conclude that the CHN (Dumas) combustion method and calculation of gross energy are acceptable analytical approaches for marine mammal milk, but fat and sugar require separate analysis by appropriate analytic methods and cannot be adequately estimated by carbon stoichiometry. Some other alternative methods-low-temperature drying for water determination; Bradford, Lowry, and biuret methods for protein; the Folch and the Bligh and Dyer methods for fat; and enzymatic and reducing sugar methods for total sugar-appear likely to produce substantial error in marine mammal milks. It is important that alternative analytical methods be properly validated against a reference method before being used, especially for mammalian milks that differ greatly from cow milk in analyte characteristics and concentrations. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

EVALUATION OF ACID DEPOSITION MODELS USING PRINCIPAL COMPONENT SPACES

EPA Science Inventory

An analytical technique involving principal components analysis is proposed for use in the evaluation of acid deposition models. elationships among model predictions are compared to those among measured data, rather than the more common one-to-one comparison of predictions to mea...

Recent α decay half-lives and analytic expression predictions including superheavy nuclei

NASA Astrophysics Data System (ADS)

Royer, G.; Zhang, H. F.

2008-03-01

New recent experimental α decay half-lives have been compared with the results obtained from previously proposed formulas depending only on the mass and charge numbers of the α emitter and the Qα value. For the heaviest nuclei they are also compared with calculations using the Density-Dependent M3Y (DDM3Y) effective interaction and the Viola-Seaborg-Sobiczewski (VSS) formulas. The correct agreement allows us to make predictions for the α decay half-lives of other still unknown superheavy nuclei from these analytic formulas using the extrapolated Qα of G. Audi, A. H. Wapstra, and C. Thibault [Nucl. Phys. A729, 337 (2003)].

Brownian systems with spatially inhomogeneous activity

NASA Astrophysics Data System (ADS)

Sharma, A.; Brader, J. M.

2017-09-01

We generalize the Green-Kubo approach, previously applied to bulk systems of spherically symmetric active particles [J. Chem. Phys. 145, 161101 (2016), 10.1063/1.4966153], to include spatially inhomogeneous activity. The method is applied to predict the spatial dependence of the average orientation per particle and the density. The average orientation is given by an integral over the self part of the Van Hove function and a simple Gaussian approximation to this quantity yields an accurate analytical expression. Taking this analytical result as input to a dynamic density functional theory approximates the spatial dependence of the density in good agreement with simulation data. All theoretical predictions are validated using Brownian dynamics simulations.

The Derivation of the Gradient of the Acoustic Pressure on a Moving Surface for Application to the Fast Scattering Code (FSC)

NASA Technical Reports Server (NTRS)

Farassat, F.; Brentner, Kenneth S.

2005-01-01

In this paper we develop an analytic expression for calculation of the the acoustic pressure from a rotating blade on a moving surface for application to the Fast Scattering Code (FSC). The analytic result is intended to be used in the helicopter noise prediction code PSU-WOPWOP. One of the goals of the derivation is obtaining a result that will not use any more information than are needed for the prediction of the thickness and loading noise. The result derived here achieves this goal and its incorporation in PSU-WOPWOP is straight forward and attainable.

Big Data and Predictive Analytics: Applications in the Care of Children.

PubMed

Suresh, Srinivasan

2016-04-01

Emerging changes in the United States' healthcare delivery model have led to renewed interest in data-driven methods for managing quality of care. Analytics (Data plus Information) plays a key role in predictive risk assessment, clinical decision support, and various patient throughput measures. This article reviews the application of a pediatric risk score, which is integrated into our hospital's electronic medical record, and provides an early warning sign for clinical deterioration. Dashboards that are a part of disease management systems, are a vital tool in peer benchmarking, and can help in reducing unnecessary variations in care. Copyright © 2016 Elsevier Inc. All rights reserved.

The Lessons Oscar Taught Us: Data Science and Media & Entertainment.

PubMed

Gold, Michael; McClarren, Ryan; Gaughan, Conor

2013-06-01

Farsite Group, a data science firm based in Columbus, Ohio, launched a highly visible campaign in early 2013 to use predictive analytics to forecast the winners of the 85th Annual Academy Awards. The initiative was fun and exciting for the millions of Oscar viewers, but it also illustrated how data science could be further deployed in the media and entertainment industries. This article explores the current and potential use cases for big data and predictive analytics in those industries. It further discusses how the Farsite Forecast was built, as well as how the model was iterated, how the projections performed, and what lessons were learned in the process.

The effect of inclined soil layers on surface vibration from underground railways using a semi-analytical approach

NASA Astrophysics Data System (ADS)

Jones, S.; Hunt, H.

2009-08-01

Ground vibration due to underground railways is a significant source of disturbance for people living or working near the subways. The numerical models used to predict vibration levels have inherent uncertainty which must be understood to give confidence in the predictions. A semi-analytical approach is developed herein to investigate the effect of soil layering on the surface vibration of a halfspace where both soil properties and layer inclination angles are varied. The study suggests that both material properties and inclination angle of the layers have significant effect (± 10dB) on the surface vibration response.

Mechanical properties of triaxially braided composites: Experimental and analytical results

NASA Technical Reports Server (NTRS)

Masters, John E.; Foye, Raymond L.; Pastore, Christopher M.; Gowayed, Yasser A.

1992-01-01

This paper investigates the unnotched tensile properties of two-dimensional triaxial braid reinforced composites from both an experimental and analytical viewpoint. The materials are graphite fibers in an epoxy matrix. Three different reinforcing fiber architectures were considered. Specimens were cut from resin transfer molded (RTM) composite panels made from each braid. There were considerable differences in the observed elastic constants from different size strain gage and extensometer readings. Larger strain gages gave more consistent results and correlated better with the extensometer readings. Experimental strains correlated reasonably well with analytical predictions in the longitudinal, zero degree, fiber direction but not in the transverse direction. Tensile strength results were not always predictable even in reinforcing directions. Minor changes in braid geometry led to disproportionate strength variations. The unit cell structure of the triaxial braid was discussed with the assistence of computer analysis of the microgeometry. Photomicrographs of the braid geometry were used to improve upon the computer graphics representations of unit cells. These unit cells were used to predict the elastic moduli with various degrees of sophistication. The simple and the complex analyses were generally in agreement but none adequately matched the experimental results for all the braids.

Mechanical properties of triaxially braided composites: Experimental and analytical results

NASA Technical Reports Server (NTRS)

Masters, John E.; Foye, Raymond L.; Pastore, Christopher M.; Gowayed, Yasser A.

1992-01-01

The unnotched tensile properties of 2-D triaxial braid reinforced composites from both an experimental and an analytical viewpoint are studied. The materials are graphite fibers in an epoxy matrix. Three different reinforcing fiber architectures were considered. Specimens were cut from resin transfer molded (RTM) composite panels made from each braid. There were considerable differences in the observed elastic constants from different size strain gage and extensometer reading. Larger strain gages gave more consistent results and correlated better with the extensometer reading. Experimental strains correlated reasonably well with analytical predictions in the longitudinal, 0 degrees, fiber direction but not in the transverse direction. Tensile strength results were not always predictable even in reinforcing directions. Minor changes in braid geometry led to disproportionate strength variations. The unit cell structure of the triaxial braid was discussed with the assistance of computer analysis of the microgeometry. Photomicrographs of braid geometry were used to improve upon the computer graphics representations of unit cells. These unit cells were used to predict the elastic moduli with various degrees of sophistication. The simple and the complex analyses were generally in agreement but none adequately matched the experimental results for all the braids.

Mechanics of the tapered interference fit in dental implants.

PubMed

Bozkaya, Dinçer; Müftü, Sinan

2003-11-01

In evaluation of the long-term success of a dental implant, the reliability and the stability of the implant-abutment interface plays a great role. Tapered interference fits provide a reliable connection method between the abutment and the implant. In this work, the mechanics of the tapered interference fits were analyzed using a closed-form formula and the finite element (FE) method. An analytical solution, which is used to predict the contact pressure in a straight interference, was modified to predict the contact pressure in the tapered implant-abutment interface. Elastic-plastic FE analysis was used to simulate the implant and abutment material behavior. The validity and the applicability of the analytical solution were investigated by comparisons with the FE model for a range of problem parameters. It was shown that the analytical solution could be used to determine the pull-out force and loosening-torque with 5-10% error. Detailed analysis of the stress distribution due to tapered interference fit, in a commercially available, abutment-implant system was carried out. This analysis shows that plastic deformation in the implant limits the increase in the pull-out force that would have been otherwise predicted by higher interference values.

Sustained prediction ability of net analyte preprocessing methods using reduced calibration sets. Theoretical and experimental study involving the spectrophotometric analysis of multicomponent mixtures.

PubMed

Goicoechea, H C; Olivieri, A C

2001-07-01

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evaluated and compared with that of partial least-squares (PLS-1). Minimisation of the calibration predicted error sum of squares (PRESS) as a function of a moving spectral window helped to select appropriate working spectral ranges for both methods. The comparison of NAP and PLS results was carried out using two tests: (1) the elliptical joint confidence region for the slope and intercept of a predicted versus actual concentrations plot for a large validation set of samples and (2) the D-optimality criterion concerning the information content of the calibration data matrix. Extensive simulations and experimental validation showed that, unlike PLS, the NAP method is able to furnish highly satisfactory results when the calibration set is reduced from a full four-component central composite to a fractional central composite, as expected from the modelling requirements of net analyte based methods.

Assessment and prediction of drying shrinkage cracking in bonded mortar overlays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beushausen, Hans, E-mail: hans.beushausen@uct.ac.za; Chilwesa, Masuzyo

2013-11-15

Restrained drying shrinkage cracking was investigated on composite beams consisting of substrate concrete and bonded mortar overlays, and compared to the performance of the same mortars when subjected to the ring test. Stress development and cracking in the composite specimens were analytically modeled and predicted based on the measurement of relevant time-dependent material properties such as drying shrinkage, elastic modulus, tensile relaxation and tensile strength. Overlay cracking in the composite beams could be very well predicted with the analytical model. The ring test provided a useful qualitative comparison of the cracking performance of the mortars. The duration of curing wasmore » found to only have a minor influence on crack development. This was ascribed to the fact that prolonged curing has a beneficial effect on tensile strength at the onset of stress development, but is in the same time not beneficial to the values of tensile relaxation and elastic modulus. -- Highlights: •Parameter study on material characteristics influencing overlay cracking. •Analytical model gives good quantitative indication of overlay cracking. •Ring test presents good qualitative indication of overlay cracking. •Curing duration has little effect on overlay cracking.« less

Analytical solutions of hypersonic type IV shock - shock interactions

NASA Astrophysics Data System (ADS)

Frame, Michael John

An analytical model has been developed to predict the effects of a type IV shock interaction at high Mach numbers. This interaction occurs when an impinging oblique shock wave intersects the most normal portion of a detached bow shock. The flowfield which develops is complicated and contains an embedded jet of supersonic flow, which may be unsteady. The jet impinges on the blunt body surface causing very high pressure and heating loads. Understanding this type of interaction is vital to the designers of cowl lips and leading edges on air- breathing hypersonic vehicles. This analytical model represents the first known attempt at predicting the geometry of the interaction explicitly, without knowing beforehand the jet dimensions, including the length of the transmitted shock where the jet originates. The model uses a hyperbolic equation for the bow shock and by matching mass continuity, flow directions and pressure throughout the flowfield, a prediction of the interaction geometry can be derived. The model has been shown to agree well with the flowfield patterns and properties of experiments and CFD, but the prediction for where the peak pressure is located, and its value, can be significantly in error due to a lack of sophistication in the model of the jet fluid stagnation region. Therefore it is recommended that this region of the flowfield be modeled in more detail and more accurate experimental and CFD measurements be used for validation. However, the analytical model has been shown to be a fast and economic prediction tool, suitable for preliminary design, or for understanding the interactions effects, including the basic physics of the interaction, such as the jet unsteadiness. The model has been used to examine a wide parametric space of possible interactions, including different Mach number, impinging shock strength and location, and cylinder radius. It has also been used to examine the interaction on power-law shaped blunt bodies, a possible candidate for hypersonic leading edges. The formation of vortices at the termination shock of the supersonic jet has been modeled using the analytical method. The vortices lead to deflections in the jet terminating flow, and the presence of the cylinder surface seems to causes the vortices to break off the jet resulting in an oscillation in the jet flow.

Mathematical and field analysis of longitudinal reservoir infill

NASA Astrophysics Data System (ADS)

Ke, W. T.; Capart, H.

2016-12-01

In reservoirs, severe problems are caused by infilled sediment deposits. In long term, the sediment accumulation reduces the capacity of reservoir storage and flood control benefits. In the short term, the sediment deposits influence the intakes of water-supply and hydroelectricity generation. For the management of reservoir, it is important to understand the deposition process and then to predict the sedimentation in reservoir. To investigate the behaviors of sediment deposits, we propose a one-dimensional simplified theory derived by the Exner equation to predict the longitudinal sedimentation distribution in idealized reservoirs. The theory models the reservoir infill geomorphic actions for three scenarios: delta progradation, near-dam bottom deposition, and final infill. These yield three kinds of self-similar analytical solutions for the reservoir bed profiles, under different boundary conditions. Three analytical solutions are composed by error function, complementary error function, and imaginary error function, respectively. The theory is also computed by finite volume method to test the analytical solutions. The theoretical and numerical predictions are in good agreement with one-dimensional small-scale laboratory experiment. As the theory is simple to apply with analytical solutions and numerical computation, we propose some applications to simulate the long-profile evolution of field reservoirs and focus on the infill sediment deposit volume resulting the uplift of near-dam bottom elevation. These field reservoirs introduced here are Wushe Reservoir, Tsengwen Reservoir, Mudan Reservoir in Taiwan, Lago Dos Bocas in Puerto Rico, and Sakuma Dam in Japan.

Comparison of thermal analytic model with experimental test results for 30-sentimeter-diameter engineering model mercury ion thruster

NASA Technical Reports Server (NTRS)

Oglebay, J. C.

1977-01-01

A thermal analytic model for a 30-cm engineering model mercury-ion thruster was developed and calibrated using the experimental test results of tests of a pre-engineering model 30-cm thruster. A series of tests, performed later, simulated a wide range of thermal environments on an operating 30-cm engineering model thruster, which was instrumented to measure the temperature distribution within it. The modified analytic model is described and analytic and experimental results compared for various operating conditions. Based on the comparisons, it is concluded that the analytic model can be used as a preliminary design tool to predict thruster steady-state temperature distributions for stage and mission studies and to define the thermal interface bewteen the thruster and other elements of a spacecraft.

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Beutter, Brent R.; Stone, Leland S.

2000-01-01

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

A Dynamic Calibration Method for Experimental and Analytical Hub Load Comparison

NASA Technical Reports Server (NTRS)

Kreshock, Andrew R.; Thornburgh, Robert P.; Wilbur, Matthew L.

2017-01-01

This paper presents the results from an ongoing effort to produce improved correlation between analytical hub force and moment prediction and those measured during wind-tunnel testing on the Aeroelastic Rotor Experimental System (ARES), a conventional rotor testbed commonly used at the Langley Transonic Dynamics Tunnel (TDT). A frequency-dependent transformation between loads at the rotor hub and outputs of the testbed balance is produced from frequency response functions measured during vibration testing of the system. The resulting transformation is used as a dynamic calibration of the balance to transform hub loads predicted by comprehensive analysis into predicted balance outputs. In addition to detailing the transformation process, this paper also presents a set of wind-tunnel test cases, with comparisons between the measured balance outputs and transformed predictions from the comprehensive analysis code CAMRAD II. The modal response of the testbed is discussed and compared to a detailed finite-element model. Results reveal that the modal response of the testbed exhibits a number of characteristics that make accurate dynamic balance predictions challenging, even with the use of the balance transformation.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

Predictive simulation of guide-wave structural health monitoring

NASA Astrophysics Data System (ADS)

Giurgiutiu, Victor

2017-04-01

This paper presents an overview of recent developments on predictive simulation of guided wave structural health monitoring (SHM) with piezoelectric wafer active sensor (PWAS) transducers. The predictive simulation methodology is based on the hybrid global local (HGL) concept which allows fast analytical simulation in the undamaged global field and finite element method (FEM) simulation in the local field around and including the damage. The paper reviews the main results obtained in this area by researchers of the Laboratory for Active Materials and Smart Structures (LAMSS) at the University of South Carolina, USA. After thematic introduction and research motivation, the paper covers four main topics: (i) presentation of the HGL analysis; (ii) analytical simulation in 1D and 2D; (iii) scatter field generation; (iv) HGL examples. The paper ends with summary, discussion, and suggestions for future work.

Development and validation of a numerical acoustic analysis program for aircraft interior noise prediction

NASA Astrophysics Data System (ADS)

Garcea, Ralph; Leigh, Barry; Wong, R. L. M.

Reduction of interior noise in propeller-driven aircraft, to levels comparable with those obtained in jet transports, has become a leading factor in the early design stages of the new generation turboprops- and may be essential if these new designs are to succeed. The need for an analytical capability to predict interior noise is accepted throughout the turboprop aircraft industry. To this end, an analytical noise prediction program, which incorporates the SYSNOISE numerical acoustic analysis software, is under development at de Havilland. The discussion contained herein looks at the development program and how it was used in a design sensitivity analysis to optimize the structural design of the aircraft cabin for the purpose of reducing interior noise levels. This report also summarizes the validation of the SYSNOISE package using numerous classical cases from the literature.

Separation of very hydrophobic analytes by micellar electrokinetic chromatography IV. Modeling of the effective electrophoretic mobility from carbon number equivalents and octanol-water partition coefficients.

PubMed

Huhn, Carolin; Pyell, Ute

2008-07-11

It is investigated whether those relationships derived within an optimization scheme developed previously to optimize separations in micellar electrokinetic chromatography can be used to model effective electrophoretic mobilities of analytes strongly differing in their properties (polarity and type of interaction with the pseudostationary phase). The modeling is based on two parameter sets: (i) carbon number equivalents or octanol-water partition coefficients as analyte descriptors and (ii) four coefficients describing properties of the separation electrolyte (based on retention data for a homologous series of alkyl phenyl ketones used as reference analytes). The applicability of the proposed model is validated comparing experimental and calculated effective electrophoretic mobilities. The results demonstrate that the model can effectively be used to predict effective electrophoretic mobilities of neutral analytes from the determined carbon number equivalents or from octanol-water partition coefficients provided that the solvation parameters of the analytes of interest are similar to those of the reference analytes.

26th International Symposium on Ballistics

DTIC Science & Technology

2011-09-16

judicious use of analytical predictions correlated with ballistic testing and post - test failure morphology investigations. •Our approach...ballistic predictions. The numerical predictions correlate well with the damage pattern. Post - Test Morphology Simulation Imbedded Steel Plate Removed Post ... Test •Numerical simulation of damage to embedded steel plate compares well with the post - test plate morphology •Multi-strike modeling in work

S-2 stage 1/25 scale model base region thermal environment test. Volume 1: Test results, comparison with theory and flight data

NASA Technical Reports Server (NTRS)

Sadunas, J. A.; French, E. P.; Sexton, H.

1973-01-01

A 1/25 scale model S-2 stage base region thermal environment test is presented. Analytical results are included which reflect the effect of engine operating conditions, model scale, turbo-pump exhaust gas injection on base region thermal environment. Comparisons are made between full scale flight data, model test data, and analytical results. The report is prepared in two volumes. The description of analytical predictions and comparisons with flight data are presented. Tabulation of the test data is provided.

Analytical and experimental validation of the Oblique Detonation Wave Engine concept

NASA Technical Reports Server (NTRS)

Adelman, Henry G.; Cambier, Jean-Luc; Menees, Gene P.; Balboni, John A.

1988-01-01

The Oblique Detonation Wave Engine (ODWE) for hypersonic flight has been analytically studied by NASA using the CFD codes which fully couple finite rate chemistry with fluid dynamics. Fuel injector designs investigated included wall and strut injectors, and the in-stream strut injectors were chosen to provide good mixing with minimal stagnation pressure losses. Plans for experimentally validating the ODWE concept in an arc-jet hypersonic wind tunnel are discussed. Measurements of the flow field properties behind the oblique wave will be compared to analytical predictions.

Uncertainty of relative sensitivity factors in glow discharge mass spectrometry

NASA Astrophysics Data System (ADS)

Meija, Juris; Methven, Brad; Sturgeon, Ralph E.

2017-10-01

The concept of the relative sensitivity factors required for the correction of the measured ion beam ratios in pin-cell glow discharge mass spectrometry is examined in detail. We propose a data-driven model for predicting the relative response factors, which relies on a non-linear least squares adjustment and analyte/matrix interchangeability phenomena. The model provides a self-consistent set of response factors for any analyte/matrix combination of any element that appears as either an analyte or matrix in at least one known response factor.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Analytical prediction of sub-surface thermal history in translucent tissue phantoms during plasmonic photo-thermotherapy (PPTT).

PubMed

Dhar, Purbarun; Paul, Anup; Narasimhan, Arunn; Das, Sarit K

2016-12-01

Knowledge of thermal history and/or distribution in biological tissues during laser based hyperthermia is essential to achieve necrosis of tumour/carcinoma cells. A semi-analytical model to predict sub-surface thermal distribution in translucent, soft, tissue mimics has been proposed. The model can accurately predict the spatio-temporal temperature variations along depth and the anomalous thermal behaviour in such media, viz. occurrence of sub-surface temperature peaks. Based on optical and thermal properties, the augmented temperature and shift of the peak positions in case of gold nanostructure mediated tissue phantom hyperthermia can be predicted. Employing inverse approach, the absorption coefficient of nano-graphene infused tissue mimics is determined from the peak temperature and found to provide appreciably accurate predictions along depth. Furthermore, a simplistic, dimensionally consistent correlation to theoretically determine the position of the peak in such media is proposed and found to be consistent with experiments and computations. The model shows promise in predicting thermal distribution induced by lasers in tissues and deduction of therapeutic hyperthermia parameters, thereby assisting clinical procedures by providing a priori estimates. Copyright © 2016 Elsevier Ltd. All rights reserved.

Long Term Mean Local Time of the Ascending Node Prediction

NASA Technical Reports Server (NTRS)

McKinley, David P.

2007-01-01

Significant error has been observed in the long term prediction of the Mean Local Time of the Ascending Node on the Aqua spacecraft. This error of approximately 90 seconds over a two year prediction is a complication in planning and timing of maneuvers for all members of the Earth Observing System Afternoon Constellation, which use Aqua's MLTAN as the reference for their inclination maneuvers. It was determined that the source of the prediction error was the lack of a solid Earth tide model in the operational force models. The Love Model of the solid Earth tide potential was used to derive analytic corrections to the inclination and right ascension of the ascending node of Aqua's Sun-synchronous orbit. Additionally, it was determined that the resonance between the Sun and orbit plane of the Sun-synchronous orbit is the primary driver of this error. The analytic corrections have been added to the operational force models for the Aqua spacecraft reducing the two-year 90-second error to less than 7 seconds.

Evaluation of analytical procedures for prediction of turbulent boundary layers on a porous wall

NASA Technical Reports Server (NTRS)

Towne, C. E.

1974-01-01

An analytical study has been made to determine how well current boundary layer prediction techniques work when there is mass transfer normal to the wall. The data that were considered in this investigation were for two-dimensional, incompressible, turbulent boundary layers with suction and blowing. Some of the bleed data were taken in an adverse pressure gradient. An integral prediction method was used three different porous wall skin friction relations, in addition to a solid-surface relation for the suction cases. A numerical prediction method was also used. Comparisons were made between theoretical and experimental skin friction coefficients, displacement and momentum thicknesses, and velocity profiles. The integral method with one of the porous wall skin friction laws gave very good agreement with data for most of the cases considered. The use of the solid-surface skin friction law caused the integral to overpredict the effectiveness of the bleed. The numerical techniques also worked well for most of the cases.

Importance of aggregation and small ice crystals in cirrus clouds, based on observations and an ice particle growth model

NASA Technical Reports Server (NTRS)

Mitchell, David L.; Chai, Steven K.; Dong, Yayi; Arnott, W. Patrick; Hallett, John

1993-01-01

The 1 November 1986 FIRE I case study was used to test an ice particle growth model which predicts bimodal size spectra in cirrus clouds. The model was developed from an analytically based model which predicts the height evolution of monomodal ice particle size spectra from the measured ice water content (IWC). Size spectra from the monomodal model are represented by a gamma distribution, N(D) = N(sub o)D(exp nu)exp(-lambda D), where D = ice particle maximum dimension. The slope parameter, lambda, and the parameter N(sub o) are predicted from the IWC through the growth processes of vapor diffusion and aggregation. The model formulation is analytical, computationally efficient, and well suited for incorporation into larger models. The monomodal model has been validated against two other cirrus cloud case studies. From the monomodal size spectra, the size distributions which determine concentrations of ice particles less than about 150 mu m are predicted.

Study of cavitating inducer instabilities

NASA Technical Reports Server (NTRS)

Young, W. E.; Murphy, R.; Reddecliff, J. M.

1972-01-01

An analytic and experimental investigation into the causes and mechanisms of cavitating inducer instabilities was conducted. Hydrofoil cascade tests were performed, during which cavity sizes were measured. The measured data were used, along with inducer data and potential flow predictions, to refine an analysis for the prediction of inducer blade suction surface cavitation cavity volume. Cavity volume predictions were incorporated into a linearized system model, and instability predictions for an inducer water test loop were generated. Inducer tests were conducted and instability predictions correlated favorably with measured instability data.

Experimental Validation of the Transverse Shear Behavior of a Nomex Core for Sandwich Panels

NASA Astrophysics Data System (ADS)

Farooqi, M. I.; Nasir, M. A.; Ali, H. M.; Ali, Y.

2017-05-01

This work deals with determination of the transverse shear moduli of a Nomex® honeycomb core of sandwich panels. Their out-of-plane shear characteristics depend on the transverse shear moduli of the honeycomb core. These moduli were determined experimentally, numerically, and analytically. Numerical simulations were performed by using a unit cell model and three analytical approaches. Analytical calculations showed that two of the approaches provided reasonable predictions for the transverse shear modulus as compared with experimental results. However, the approach based upon the classical lamination theory showed large deviations from experimental data. Numerical simulations also showed a trend similar to that resulting from the analytical models.

Studies of the Speed Stability of a Tandem Helicopter in Forward Flight

NASA Technical Reports Server (NTRS)

Tapscott, Robert J; Amer, Kenneth B

1956-01-01

Flight-test measurements, related analytical studies, and corresponding pilots' opinions of the speed stability of tandem-rotor helicopter are presented. An undesirable instability, evidenced by rearward stick motion with increasing forward speed at constant power, is indicated to be caused by variations with speed of the front-rotor downwash at the rear rotor. An analytical expression for predicting changes in speed stability caused by changes in rotor geometry is derived and constants for use with the analytical expression are presented in chart form. Means for improving stability with speed are studied both analytically and experimentally. The test results also give some information as to the flow conditions at the rear rotor.

Verification of a magnetic island in gyro-kinetics by comparison with analytic theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zarzoso, D., E-mail: david.zarzoso-fernandez@polytechnique.org; Casson, F. J.; Poli, E.

A rotating magnetic island is imposed in the gyrokinetic code GKW, when finite differences are used for the radial direction, in order to develop the predictions of analytic tearing mode theory and understand its limitations. The implementation is verified against analytics in sheared slab geometry with three numerical tests that are suggested as benchmark cases for every code that imposes a magnetic island. The convergence requirements to properly resolve physics around the island separatrix are investigated. In the slab geometry, at low magnetic shear, binormal flows inside the island can drive Kelvin-Helmholtz instabilities which prevent the formation of the steadymore » state for which the analytic theory is formulated.« less

Forecasting hotspots using predictive visual analytics approach

DOEpatents

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Study of the elastic behavior of synthetic lightweight aggregates (SLAs)

NASA Astrophysics Data System (ADS)

Jin, Na

Synthetic lightweight aggregates (SLAs), composed of coal fly ash and recycled plastics, represent a resilient construction material that could be a key aspect to future sustainable development. This research focuses on a prediction of the elastic modulus of SLA, assumed as a homogenous and isotropic composite of particulates of high carbon fly ash (HCFA) and a matrix of plastics (HDPE, LDPE, PS and mixture of plastics), with the emphasis on SLAs made of HCFA and PS. The elastic moduli of SLA with variable fly ash volume fractions are predicted based on finite element analyses (FEA) performed using the computer programs ABAQUS and PLAXIS. The effect of interface friction (roughness) between phases and other computation parameters; e.g., loading strain, stiffness of component, element type and boundary conditions, are included in these analyses. Analytical models and laboratory tests provide a baseline for comparison. Overall, results indicate ABAQUS generates elastic moduli closer to those predicted by well-established analytical models than moduli predicted from PLAXIS, especially for SLAs with lower fly ash content. In addition, an increase in roughness, loading strain indicated increase of SLAs stiffness, especially as fly ash content increases. The elastic moduli obtained from unconfined compression generally showed less elastic moduli than those obtained from analytical and ABAQUS 3D predictions. This may be caused by possible existence of pre-failure surface in specimen and the directly interaction between HCFA particles. Recommendations for the future work include laboratory measurements of SLAs moduli and FEM modeling that considers various sizes and random distribution of HCFA particles in SLAs.

Attitude Control of Flexible Structures.

DTIC Science & Technology

1990-09-01

arm has been determined experimentally and compared with analytical * predictions obtained by using the GIFTS finite element analysis program. The...frequencies of the flexible arm have been determined experimentally and compared with analytical predictiens obtained by using the GIFTS finite element...exception of the first mode. Table V shows the difference between the frequencies obtained from the GIFTS program and the experimental values. TABLE

Development of Advanced Life Prediction Tools for Elastic-Plastic Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

Gregg, Wayne; McGill, Preston; Swanson, Greg; Wells, Doug; Throckmorton, D. A. (Technical Monitor)

2001-01-01

The objective of this viewgraph presentation is to develop a systematic approach to improving the fracture control process, including analytical tools, standards, guidelines, and awareness. Analytical tools specifically for elastic-plastic fracture analysis is a regime that is currently empirical for the Space Shuttle External Tank (ET) and is handled by simulated service testing of pre-cracked panels.

Review of Thawing Time Prediction Models Depending
on Process Conditions and Product Characteristics

PubMed Central

Kluza, Franciszek; Spiess, Walter E. L.; Kozłowicz, Katarzyna

2016-01-01

Summary Determining thawing times of frozen foods is a challenging problem as the thermophysical properties of the product change during thawing. A number of calculation models and solutions have been developed. The proposed solutions range from relatively simple analytical equations based on a number of assumptions to a group of empirical approaches that sometimes require complex calculations. In this paper analytical, empirical and graphical models are presented and critically reviewed. The conditions of solution, limitations and possible applications of the models are discussed. The graphical and semi--graphical models are derived from numerical methods. Using the numerical methods is not always possible as running calculations takes time, whereas the specialized software and equipment are not always cheap. For these reasons, the application of analytical-empirical models is more useful for engineering. It is demonstrated that there is no simple, accurate and feasible analytical method for thawing time prediction. Consequently, simplified methods are needed for thawing time estimation of agricultural and food products. The review reveals the need for further improvement of the existing solutions or development of new ones that will enable accurate determination of thawing time within a wide range of practical conditions of heat transfer during processing. PMID:27904387

Variability in the Propagation Phase of CFD-Based Noise Prediction: Summary of Results From Category 8 of the BANC-III Workshop

NASA Technical Reports Server (NTRS)

Lopes, Leonard; Redonnet, Stephane; Imamura, Taro; Ikeda, Tomoaki; Zawodny, Nikolas; Cunha, Guilherme

2015-01-01

The usage of Computational Fluid Dynamics (CFD) in noise prediction typically has been a two part process: accurately predicting the flow conditions in the near-field and then propagating the noise from the near-field to the observer. Due to the increase in computing power and the cost benefit when weighed against wind tunnel testing, the usage of CFD to estimate the local flow field of complex geometrical structures has become more routine. Recently, the Benchmark problems in Airframe Noise Computation (BANC) workshops have provided a community focus on accurately simulating the local flow field near the body with various CFD approaches. However, to date, little effort has been given into assessing the impact of the propagation phase of noise prediction. This paper includes results from the BANC-III workshop which explores variability in the propagation phase of CFD-based noise prediction. This includes two test cases: an analytical solution of a quadrupole source near a sphere and a computational solution around a nose landing gear. Agreement between three codes was very good for the analytic test case, but CFD-based noise predictions indicate that the propagation phase can introduce 3dB or more of variability in noise predictions.

A Demonstration of Regression False Positive Selection in Data Mining

ERIC Educational Resources Information Center

Pinder, Jonathan P.

2014-01-01

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

Analyzing Flows In Rocket Nuclear Reactors

NASA Technical Reports Server (NTRS)

Clark, J. S.; Walton, J. T.; Mcguire, M.

1994-01-01

CAC is analytical prediction program to study heat-transfer and fluid-flow characteristics of circular coolant passage. Predicts, as function of time, axial and radial fluid conditions, temperatures of passage walls, rates of flow in each coolant passage, and approximate maximum material temperatures. Written in ANSI standard FORTRAN 77.

Technique for Predicting the Radio Frequency Field Strength Inside an Enclosure

NASA Technical Reports Server (NTRS)

Hallett, Michael P.; Reddell, Jerry P.

1997-01-01

This technical memo represents a simple analytical technique for predicting the Radio Frequency (RF) field inside an enclosed volume in which radio frequency occurs. The technique was developed to predict the RF field strength within a launch vehicle fairing in which some payloads desire to launch with their telemetry transmitter radiating. This technique considers both the launch vehicle and the payload aspects.

Theoretical and experimental evaluation of the effects of an argon gas mixture on the pressure drop through adult tracheobronchial airway replicas.

PubMed

Litwin, Patrick D; Reis Dib, Anna Luisa; Chen, John; Noga, Michelle; Finlay, Warren H; Martin, Andrew R

2017-06-14

Argon has the potential to be a novel inhaled therapeutic agent, owing to the neuroprotective and organoprotective properties demonstrated in preclinical studies. Before human trials are performed, an understanding of varying gas properties on airway resistance during inhalation is essential. This study predicts the effect of an 80% argon/20% oxygen gas mixture on the pressure drop through conducting airways, and by extension the airway resistance, and then verifies these predictions experimentally using 3-D printed adult tracheobronchial airway replicas. The predicted pressure drop was calculated using established analytical models of airway resistance, incorporating the change in viscosity and density of the 80% argon/20% oxygen mixture versus that of air. Predicted pressure drop for the argon mixture increased by approximately 29% compared to that for air. The experimental results were consistent with this prediction for inspiratory flows ranging from 15 to 90slpm. These results indicate that established analytical models may be used to predict increases in conducting airway resistance for argon/oxygen mixtures, compared with air. Such predictions are valuable in predicting average patient response to breathing argon/oxygen mixtures, and in selecting or designing delivery systems for use in administration of argon/oxygen mixtures to critically ill or injured patients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Recent {alpha} decay half-lives and analytic expression predictions including superheavy nuclei

DOE Office of Scientific and Technical Information (OSTI.GOV)

Royer, G.; Zhang, H. F.

New recent experimental {alpha} decay half-lives have been compared with the results obtained from previously proposed formulas depending only on the mass and charge numbers of the {alpha} emitter and the Q{sub {alpha}} value. For the heaviest nuclei they are also compared with calculations using the Density-Dependent M3Y (DDM3Y) effective interaction and the Viola-Seaborg-Sobiczewski (VSS) formulas. The correct agreement allows us to make predictions for the {alpha} decay half-lives of other still unknown superheavy nuclei from these analytic formulas using the extrapolated Q{sub {alpha}} of G. Audi, A. H. Wapstra, and C. Thibault [Nucl. Phys. A729, 337 (2003)].

Estimation of the curvature of the solid liquid interface during Bridgman crystal growth

NASA Astrophysics Data System (ADS)

Barat, Catherine; Duffar, Thierry; Garandet, Jean-Paul

1998-11-01

An approximate solution for the solid/liquid interface curvature due to the crucible effect in crystal growth is derived from simple heat flux considerations. The numerical modelling of the problem carried out with the help of the finite element code FIDAP supports the predictions of our analytical expression and allows to identify its range of validity. Experimental interface curvatures, measured in gallium antimonide samples grown by the vertical Bridgman method, are seen to compare satisfactorily to analytical and numerical results. Other literature data are also in fair agreement with the predictions of our models in the case where the amount of heat carried by the crucible is small compared to the overall heat flux.

Computer modeling of a two-junction, monolithic cascade solar cell

NASA Technical Reports Server (NTRS)

Lamorte, M. F.; Abbott, D.

1979-01-01

The theory and design criteria for monolithic, two-junction cascade solar cells are described. The departure from the conventional solar cell analytical method and the reasons for using the integral form of the continuity equations are briefly discussed. The results of design optimization are presented. The energy conversion efficiency that is predicted for the optimized structure is greater than 30% at 300 K, AMO and one sun. The analytical method predicts device performance characteristics as a function of temperature. The range is restricted to 300 to 600 K. While the analysis is capable of determining most of the physical processes occurring in each of the individual layers, only the more significant device performance characteristics are presented.

Fast semi-analytical method for precise prediction of ion energy distribution functions and sheath electric field in multi-frequency capacitively coupled plasmas

NASA Astrophysics Data System (ADS)

Chen, Wencong; Zhang, Xi; Diao, Dongfeng

2018-05-01

We propose a fast semi-analytical method to predict ion energy distribution functions and sheath electric field in multi-frequency capacitively coupled plasmas, which are difficult to measure in commercial plasma reactors. In the intermediate frequency regime, the ion density within the sheath is strongly modulated by the low-frequency sheath electric field, making the time-independent ion density assumption employed in conventional models invalid. Our results are in a good agreement with experimental measurements and computer simulations. The application of this method will facilitate the understanding of ion–material interaction mechanisms and development of new-generation plasma etching devices.

Correction coefficient for see-through labyrinth seal

NASA Astrophysics Data System (ADS)

Hasnedl, Dan; Epikaridis, Premysl; Slama, Vaclav

In a steam turbine design, the flow-part design and blade shapes are influenced by the design mass-flow through each turbine stage. If it would be possible to predict this mass-flow more precisely, it will result in optimized design and therefore an efficiency benefit. This article is concerned with improving the prediction of losses caused by the seal leakage. In the common simulation of the thermodynamic cycle of a steam turbine, analytical formulas are used in order to simulate the seal leakage. Therefore, this article describes an improvement of analytical formulas used in a turbine heat balance calculation. The results are verified by numerical simulations and experimental data from the steam test rig.

Big Data and Analytics in Healthcare.

PubMed

Tan, S S-L; Gao, G; Koch, S

2015-01-01

This editorial is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and "big data") to improve health outcomes and reduce costs. However, the nature of healthcare data, and especially big data, presents unique challenges in processing and analyzing big data in healthcare. This Focus Theme aims to disseminate some novel approaches to address these challenges. More specifically, approaches ranging from efficient methods of processing large clinical data to predictive models that could generate better predictions from healthcare data are presented.

Clinical judgement in the era of big data and predictive analytics.

PubMed

Chin-Yee, Benjamin; Upshur, Ross

2018-06-01

Clinical judgement is a central and longstanding issue in the philosophy of medicine which has generated significant interest over the past few decades. In this article, we explore different approaches to clinical judgement articulated in the literature, focusing in particular on data-driven, mathematical approaches which we contrast with narrative, virtue-based approaches to clinical reasoning. We discuss the tension between these different clinical epistemologies and further explore the implications of big data and machine learning for a philosophy of clinical judgement. We argue for a pluralistic, integrative approach, and demonstrate how narrative, virtue-based clinical reasoning will remain indispensable in an era of big data and predictive analytics. © 2017 John Wiley & Sons, Ltd.

Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

NASA Astrophysics Data System (ADS)

Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

2015-07-01

Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

Is experiential-intuitive cognitive style more inclined to err on conjunction fallacy than analytical-rational cognitive style?

PubMed

Lu, Yong

2015-01-01

In terms of prediction by Epstein's integrative theory of personality, cognitive-experiential self-theory (CEST), those people with experiential-intuitive cognitive style are more inclined to induce errors than the other people with analytical-rational cognitive style in the conjunction fallacy (two events that can occur together are seen as more likely than at least one of the two events). We tested this prediction in a revised Linda problem. The results revealed that rational and experiential cognitive styles do not statistically influence the propensity for committing the conjunction fallacy, which is contrary to the CEST's predictions. Based on the assumption that the rational vs. experiential processing is a personality trait with comparatively stabile specialities, these findings preliminarily indicate that those people who are characterized by "rational thinking" are not more inclined to use Bayes' deduction than the other people who are labeled by "intuitive thinking" or by "poor thinking."

Description of a Generalized Analytical Model for the Micro-dosimeter Response

NASA Technical Reports Server (NTRS)

Badavi, Francis F.; Stewart-Sloan, Charlotte R.; Xapsos, Michael A.; Shinn, Judy L.; Wilson, John W.; Hunter, Abigail

2007-01-01

An analytical prediction capability for space radiation in Low Earth Orbit (LEO), correlated with the Space Transportation System (STS) Shuttle Tissue Equivalent Proportional Counter (TEPC) measurements, is presented. The model takes into consideration the energy loss straggling and chord length distribution of the TEPC detector, and is capable of predicting energy deposition fluctuations in a micro-volume by incoming ions through both direct and indirect ionic events. The charged particle transport calculations correlated with STS 56, 51, 110 and 114 flights are accomplished by utilizing the most recent version (2005) of the Langley Research Center (LaRC) deterministic ionized particle transport code High charge (Z) and Energy TRaNsport WZETRN), which has been extensively validated with laboratory beam measurements and available space flight data. The agreement between the TEPC model prediction (response function) and the TEPC measured differential and integral spectra in lineal energy (y) domain is promising.

An elastic-plastic contact model for line contact structures

NASA Astrophysics Data System (ADS)

Zhu, Haibin; Zhao, Yingtao; He, Zhifeng; Zhang, Ruinan; Ma, Shaopeng

2018-06-01

Although numerical simulation tools are now very powerful, the development of analytical models is very important for the prediction of the mechanical behaviour of line contact structures for deeply understanding contact problems and engineering applications. For the line contact structures widely used in the engineering field, few analytical models are available for predicting the mechanical behaviour when the structures deform plastically, as the classic Hertz's theory would be invalid. Thus, the present study proposed an elastic-plastic model for line contact structures based on the understanding of the yield mechanism. A mathematical expression describing the global relationship between load history and contact width evolution of line contact structures was obtained. The proposed model was verified through an actual line contact test and a corresponding numerical simulation. The results confirmed that this model can be used to accurately predict the elastic-plastic mechanical behaviour of a line contact structure.

Cardiac data mining (CDM); organization and predictive analytics on biomedical (cardiac) data

NASA Astrophysics Data System (ADS)

Bilal, M. Musa; Hussain, Masood; Basharat, Iqra; Fatima, Mamuna

2013-10-01

Data mining and data analytics has been of immense importance to many different fields as we witness the evolution of data sciences over recent years. Biostatistics and Medical Informatics has proved to be the foundation of many modern biological theories and analysis techniques. These are the fields which applies data mining practices along with statistical models to discover hidden trends from data that comprises of biological experiments or procedures on different entities. The objective of this research study is to develop a system for the efficient extraction, transformation and loading of such data from cardiologic procedure reports given by Armed Forces Institute of Cardiology. It also aims to devise a model for the predictive analysis and classification of this data to some important classes as required by cardiologists all around the world. This includes predicting patient impressions and other important features.

On the predictability of outliers in ensemble forecasts

NASA Astrophysics Data System (ADS)

Siegert, S.; Bröcker, J.; Kantz, H.

2012-03-01

In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.

Diagnostics of seeded RF plasmas: An experimental study related to the gaseous core reactor

NASA Technical Reports Server (NTRS)

Thompson, S. D.; Clement, J. D.; Williams, J. R.

1974-01-01

Measurements of the temperature profiles in an RF argon plasma were made over magnetic field intensities ranging from 20 amp turns/cm to 80 amp turns/cm. The results were compared with a one-dimensional numerical treatment of the governing equations and with an approximate closed form analytical solution that neglected radiation losses. The average measured temperatures in the plasma compared well with the numerical treatment, though the experimental profile showed less of an off center temperature peak than predicted by theory. This may be a result of the complex turbulent flow pattern present in the experimental torch and not modeled in the numerical treatment. The radiation term cannot be neglected for argon at the power levels investigated. The closed form analytical approximation that neglected radiation led to temperature predictions on the order of 1000 K to 2000 K higher than measured or predicted by the numerical treatment which considered radiation losses.

Predicting adverse hemodynamic events in critically ill patients.

PubMed

Yoon, Joo H; Pinsky, Michael R

2018-06-01

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Transport composite fuselage technology: Impact dynamics and acoustic transmission

NASA Technical Reports Server (NTRS)

Jackson, A. C.; Balena, F. J.; Labarge, W. L.; Pei, G.; Pitman, W. A.; Wittlin, G.

1986-01-01

A program was performed to develop and demonstrate the impact dynamics and acoustic transmission technology for a composite fuselage which meets the design requirements of a 1990 large transport aircraft without substantial weight and cost penalties. The program developed the analytical methodology for the prediction of acoustic transmission behavior of advanced composite stiffened shell structures. The methodology predicted that the interior noise level in a composite fuselage due to turbulent boundary layer will be less than in a comparable aluminum fuselage. The verification of these analyses will be performed by NASA Langley Research Center using a composite fuselage shell fabricated by filament winding. The program also developed analytical methodology for the prediction of the impact dynamics behavior of lower fuselage structure constructed with composite materials. Development tests were performed to demonstrate that the composite structure designed to the same operating load requirement can have at least the same energy absorption capability as aluminum structure.

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change

PubMed Central

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-01-01

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change. PMID:27621443

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change.

PubMed

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-09-27

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change.

Light-Frame Wall Systems: Performance and Predictability.

Treesearch

David S. Gromala

1983-01-01

This paper compares results of all wall tests with analytical predictions of performance.Conventional wood-stud walls of one configuration failed at bending loads that were 4 to 6 times design load.The computer model overpredicted wall strength by and average of 10 percent and deflection by an average of 6 percent.

Conditions for Effective Application of Analysis of Symmetrically-Predicted Endogenous Subgroups

ERIC Educational Resources Information Center

Peck, Laura R.

2015-01-01

Several analytic strategies exist for opening up the "black box" to reveal more about what drives policy and program impacts. This article focuses on one of these strategies: the Analysis of Symmetrically-Predicted Endogenous Subgroups (ASPES). ASPES uses exogenous baseline data to identify endogenously-defined subgroups, keeping the…

Does Fear Reactivity during Exposure Predict Panic Symptom Reduction?

ERIC Educational Resources Information Center

Meuret, Alicia E.; Seidel, Anke; Rosenfield, Benjamin; Hofmann, Stefan G.; Rosenfield, David

2012-01-01

Objective: Fear reactivity during exposure is a commonly used indicator of learning and overall therapy outcome. The objective of this study was to assess the predictive value of fear reactivity during exposure using multimodal indicators and an advanced analytical design. We also investigated the degree to which treatment condition (cognitive…

An analytical framework to assist decision makers in the use of forest ecosystem model predictions

USDA-ARS?s Scientific Manuscript database

The predictions of most terrestrial ecosystem models originate from deterministic simulations. Relatively few uncertainty evaluation exercises in model outputs are performed by either model developers or users. This issue has important consequences for decision makers who rely on models to develop n...

Structural fire design : wood

Treesearch

E. L. Schaffer

Analytical procedures to predict the fire endurance of structural wood members have been developed worldwide. This research is reviewed for capability to predict the results of tests in North America and what considerations are necessary to apply the information here. Critical research needs suggested include: (1) Investigation of load levels used in reported tests,...

Predicting Precipitation in Darwin: An Experiment with Markov Chains

ERIC Educational Resources Information Center

Boncek, John; Harden, Sig

2009-01-01

As teachers of first-year college mathematics and science students, the authors are constantly on the lookout for simple classroom exercises that improve their students' analytical and computational skills. In this article, the authors outline a project entitled "Predicting Precipitation in Darwin." In this project, students: (1) analyze…

Development of an improved capability for predicting the response of highway bridges : final report.

DOT National Transportation Integrated Search

1986-01-01

This study compared experimental and analytical stress and deflection response of a simply-supported highway bridge as measured from a field test and as predicted from a finite-element analysis. The field test was conducted on one span of a six-span ...

Radar cross sections of standard and complex shape targets

NASA Technical Reports Server (NTRS)

Sohel, M. S.

1974-01-01

The theoretical, analytical, and experimental results are described for radar cross sections (RCS) of different-shaped targets. Various techniques for predicting RCS are given, and RCS of finite standard targets are presented. Techniques used to predict the RCS of complex targets are made, and the RCS complex shapes are provided.

Study of high altitude plume impingement

NASA Technical Reports Server (NTRS)

Wojciechowski, C. J.; Penny, M. M.; Prozan, R. J.; Seymour, D.; Greenwood, T. F.

1972-01-01

Computer program has been developed as analytical tool to predict severity of effects of exhaust of rocket engines on adjacent spacecraft surfaces. Program computes forces, moments, pressures, and heating rates on surfaces immersed in or subjected to exhaust plume environments. Predictions will be useful in design of systems where such problems are anticipated.

Managing knowledge business intelligence: A cognitive analytic approach

NASA Astrophysics Data System (ADS)

Surbakti, Herison; Ta'a, Azman

2017-10-01

The purpose of this paper is to identify and analyze integration of Knowledge Management (KM) and Business Intelligence (BI) in order to achieve competitive edge in context of intellectual capital. Methodology includes review of literatures and analyzes the interviews data from managers in corporate sector and models established by different authors. BI technologies have strong association with process of KM for attaining competitive advantage. KM have strong influence from human and social factors and turn them to the most valuable assets with efficient system run under BI tactics and technologies. However, the term of predictive analytics is based on the field of BI. Extracting tacit knowledge is a big challenge to be used as a new source for BI to use in analyzing. The advanced approach of the analytic methods that address the diversity of data corpus - structured and unstructured - required a cognitive approach to provide estimative results and to yield actionable descriptive, predictive and prescriptive results. This is a big challenge nowadays, and this paper aims to elaborate detail in this initial work.

Analytical and experimental study of mean flow and turbulence characteristics inside the passages of an axial flow inducer

NASA Technical Reports Server (NTRS)

Gorton, C. A.; Lakshminarayana, B.

1974-01-01

The effort conducted to gather additional understanding of the complex inviscid and viscid effects existing within the passages of a three-bladed axial flow inducer operating at a flow coefficient of 0.065 is summarized. The experimental investigations included determination of the blade static pressure and blade limiting streamline angle distributions, and measurement of the three components of mean velocity, turbulence intensities and turbulence stresses at locations inside the inducer blade passage utilizing a rotating three-sensor hotwire probe. Applicable equations were derived for the hotwire data reduction analysis and solved numerically to obtain the appropriate flow parameters. Analytical investigations were conducted to predict the three-dimensional inviscid flow in the inducer by numerically solving the exact equations of motion, and to approximately predict the three-dimensional viscid flow by incorporating the dominant viscous terms into the exact equations. The analytical results are compared with the experimental measurements and design values where appropriate.

Analytical and Experimental Evaluation of the Heat Transfer Distribution over the Surfaces of Turbine Vanes

NASA Technical Reports Server (NTRS)

Hylton, L. D.; Mihelc, M. S.; Turner, E. R.; Nealy, D. A.; York, R. E.

1983-01-01

Three airfoil data sets were selected for use in evaluating currently available analytical models for predicting airfoil surface heat transfer distributions in a 2-D flow field. Two additional airfoils, representative of highly loaded, low solidity airfoils currently being designed, were selected for cascade testing at simulated engine conditions. Some 2-D analytical methods were examined and a version of the STAN5 boundary layer code was chosen for modification. The final form of the method utilized a time dependent, transonic inviscid cascade code coupled to a modified version of the STAN5 boundary layer code featuring zero order turbulence modeling. The boundary layer code is structured to accommodate a full spectrum of empirical correlations addressing the coupled influences of pressure gradient, airfoil curvature, and free-stream turbulence on airfoil surface heat transfer distribution and boundary layer transitional behavior. Comparison of pedictions made with the model to the data base indicates a significant improvement in predictive capability.

In-flight Evaluation of Aerodynamic Predictions of an Air-launched Space Booster

NASA Technical Reports Server (NTRS)

Curry, Robert E.; Mendenhall, Michael R.; Moulton, Bryan

1992-01-01

Several analytical aerodynamic design tools that were applied to the Pegasus (registered trademark) air-launched space booster were evaluated using flight measurements. The study was limited to existing codes and was conducted with limited computational resources. The flight instrumentation was constrained to have minimal impact on the primary Pegasus missions. Where appropriate, the flight measurements were compared with computational data. Aerodynamic performance and trim data from the first two flights were correlated with predictions. Local measurements in the wing and wing-body interference region were correlated with analytical data. This complex flow region includes the effect of aerothermal heating magnification caused by the presence of a corner vortex and interaction of the wing leading edge shock and fuselage boundary layer. The operation of the first two missions indicates that the aerodynamic design approach for Pegasus was adequate, and data show that acceptable margins were available. Additionally, the correlations provide insight into the capabilities of these analytical tools for more complex vehicles in which the design margins may be more stringent.

In-flight evaluation of aerodynamic predictions of an air-launched space booster

NASA Technical Reports Server (NTRS)

Curry, Robert E.; Mendenhall, Michael R.; Moulton, Bryan

1993-01-01

Several analytical aerodynamic design tools that were applied to the Pegasus air-launched space booster were evaluated using flight measurements. The study was limited to existing codes and was conducted with limited computational resources. The flight instrumentation was constrained to have minimal impact on the primary Pegasus missions. Where appropriate, the flight measurements were compared with computational data. Aerodynamic performance and trim data from the first two flights were correlated with predictions. Local measurements in the wing and wing-body interference region were correlated with analytical data. This complex flow region includes the effect of aerothermal heating magnification caused by the presence of a corner vortex and interaction of the wing leading edge shock and fuselage boundary layer. The operation of the first two missions indicates that the aerodynamic design approach for Pegasus was adequate, and data show that acceptable margins were available. Additionally, the correlations provide insight into the capabilities of these analytical tools for more complex vehicles in which design margins may be more stringent.

Viscoplastic deformations and compressive damage in an A359/SiC{sub p} metal-matrix composite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Y.; Ramesh, K.T.; Chin, E.S.C.

2000-04-19

Recent work by the authors has examined the high-strain-rate compression of a metal-matrix composite consisting of an A359 Al alloy matrix reinforced by 20 vol.% of silicon carbide particulates (SiC{sub p}). The work-hardening that is observed in the experiments is much lower than that predicted by analytical and computational models which assume perfect particle-matrix interfaces and undamaged particles. In this work, the authors show that the discrepancy is a result of particle damage that develops within the A359/SiC{sub p} composite under compression. The evolution of particle damage has been characterized using quantitative microscopy, and is shown to be a functionmore » of the applied strain. A simple analytical model that incorporates evolving damage within the composite is proposed, and it is shown that the analytical predictions are consistent with the experimental observations over a wide range of strain rates.« less

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

PubMed

Pellegrino Vidal, Rocío B; Allegrini, Franco; Olivieri, Alejandro C

2018-03-20

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of Time Response of Electrowetting

NASA Astrophysics Data System (ADS)

Lee, Seung Jun; Hong, Jiwoo; Kang, Kwan Hyoung

2009-11-01

It is very important to predict the time response of electrowetting-based devices, such as liquid lenses, reflective displays, and optical switches. We investigated the time response of electrowetting, based on an analytical and a numerical method, to find out characteristic scales and a scaling law for the switching time. For this, spreading process of a sessile droplet was analyzed based on the domain perturbation method. First, we considered the case of weakly viscous fluids. The analytical result for the spreading process was compared with experimental results, which showed very good agreement in overall time response. It was shown that the overall dynamics is governed by P2 shape mode. We derived characteristic scales combining the droplet volume, density, and surface tension. The overall dynamic process was scaled quite well by the scales. A scaling law was derived from the analytical solution and was verified experimentally. We also suggest a scaling law for highly viscous liquids, based on results of numerical analysis for the electrowetting-actuated spreading process.

Numerical and Analytical Solutions of Hypersonic Interactions Involving Surface Property Discontinuities

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.; Inger, George R.

1999-01-01

The local viscous-inviscid interaction field generated by a wall temperature jump on a flat plate in supersonic flow and on the windside of a Reusable Launch Vehicle in hypersonic flow is studied in detail by both a Navier-Stokes numerical code and an analytical triple-deck model. Treatment of the rapid heat transfer changes both upstream and downstream of the jump is included. Closed form relationships derived from the triple-deck theory are presented. The analytically predicted pressure and heating variations including upstream influence are found to be in generally good agreement with the Computational Fluid Dynamic (CFD) predictions. These analyses not only clarify the interactive physics involved but also are useful in preliminary design of thermal protection systems and as an insertable module to improve CFD code efficiency when applied to such small-scale interaction problems. The analyses only require conditions at the wall and boundary-layer edge which are easily extracted from a baseline, constant wall temperature, CFD solution.

Thermal conductivity of microporous layers: Analytical modeling and experimental validation

NASA Astrophysics Data System (ADS)

Andisheh-Tadbir, Mehdi; Kjeang, Erik; Bahrami, Majid

2015-11-01

A new compact relationship is developed for the thermal conductivity of the microporous layer (MPL) used in polymer electrolyte fuel cells as a function of pore size distribution, porosity, and compression pressure. The proposed model is successfully validated against experimental data obtained from a transient plane source thermal constants analyzer. The thermal conductivities of carbon paper samples with and without MPL were measured as a function of load (1-6 bars) and the MPL thermal conductivity was found between 0.13 and 0.17 W m-1 K-1. The proposed analytical model predicts the experimental thermal conductivities within 5%. A correlation generated from the analytical model was used in a multi objective genetic algorithm to predict the pore size distribution and porosity for an MPL with optimized thermal conductivity and mass diffusivity. The results suggest that an optimized MPL, in terms of heat and mass transfer coefficients, has an average pore size of 122 nm and 63% porosity.

Analytical and experimental evaluation of the heat transfer distribution over the surfaces of turbine vanes

NASA Astrophysics Data System (ADS)

Hylton, L. D.; Mihelc, M. S.; Turner, E. R.; Nealy, D. A.; York, R. E.

1983-05-01

Three airfoil data sets were selected for use in evaluating currently available analytical models for predicting airfoil surface heat transfer distributions in a 2-D flow field. Two additional airfoils, representative of highly loaded, low solidity airfoils currently being designed, were selected for cascade testing at simulated engine conditions. Some 2-D analytical methods were examined and a version of the STAN5 boundary layer code was chosen for modification. The final form of the method utilized a time dependent, transonic inviscid cascade code coupled to a modified version of the STAN5 boundary layer code featuring zero order turbulence modeling. The boundary layer code is structured to accommodate a full spectrum of empirical correlations addressing the coupled influences of pressure gradient, airfoil curvature, and free-stream turbulence on airfoil surface heat transfer distribution and boundary layer transitional behavior. Comparison of pedictions made with the model to the data base indicates a significant improvement in predictive capability.

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Investigation of characteristics of feed system instabilities

NASA Technical Reports Server (NTRS)

Vaage, R. D.; Fidler, L. E.; Zehnle, R. A.

1972-01-01

The relationship between the structural and feed system natural frequencies in structure-propulsion system coupled longitudinal oscillations (pogo) is investigated. The feed system frequencies are usually very dependent upon the compressibility (compliance) of cavitation bubbles that exist to some extent in all operating turbopumps. This document includes: a complete review of cavitation mechanisms; development of a turbopump cavitation compliance model; an accumulation and analysis of all available cavitation compliance test data; and a correlation of empirical-analytical results. The analytical model is based on the analysis of flow relative to a set of cascaded blades, having any described shape, and assumes phase changes occur under conditions of isentropic equilibrium. Analytical cavitation compliance predictions for the J-2 LOX, F-1 LOX, H-1 LOX and LR87 oxidizer turbopump inducers do not compare favorably with test data. The model predicts much less cavitation than is derived from the test data. This implies that mechanisms other than blade cavitation contribute significantly to the total amount of turbopump cavitation.

Understanding Fast and Robust Thermo-osmotic Flows through Carbon Nanotube Membranes: Thermodynamics Meets Hydrodynamics.

PubMed

Fu, Li; Merabia, Samy; Joly, Laurent

2018-04-19

Following our recent theoretical prediction of the giant thermo-osmotic response of the water-graphene interface, we explore the practical implementation of waste heat harvesting with carbon-based membranes, focusing on model membranes of carbon nanotubes (CNT). To that aim, we combine molecular dynamics simulations and an analytical model considering the details of hydrodynamics in the membrane and at the tube entrances. The analytical model and the simulation results match quantitatively, highlighting the need to take into account both thermodynamics and hydrodynamics to predict thermo-osmotic flows through membranes. We show that, despite viscous entrance effects and a thermal short-circuit mechanism, CNT membranes can generate very fast thermo-osmotic flows, which can overcome the osmotic pressure of seawater. We then show that in small tubes confinement has a complex effect on the flow and can even reverse the flow direction. Beyond CNT membranes, our analytical model can guide the search for other membranes to generate fast and robust thermo-osmotic flows.

Pressurization of cryogens - A review of current technology and its applicability to low-gravity conditions

NASA Technical Reports Server (NTRS)

Van Dresar, N. T.

1992-01-01

A review of technology, history, and current status for pressurized expulsion of cryogenic tankage is presented. Use of tank pressurization to expel cryogenic fluid will continue to be studied for future spacecraft applications over a range of operating conditions in the low-gravity environment. The review examines experimental test results and analytical model development for quiescent and agitated conditions in normal-gravity followed by a discussion of pressurization and expulsion in low-gravity. Validated, 1-D, finite difference codes exist for the prediction of pressurant mass requirements within the range of quiescent normal-gravity test data. To date, the effects of liquid sloshing have been characterized by tests in normal-gravity, but analytical models capable of predicting pressurant gas requirements remain unavailable. Efforts to develop multidimensional modeling capabilities in both normal and low-gravity have recently occurred. Low-gravity cryogenic fluid transfer experiments are needed to obtain low-gravity pressurized expulsion data. This data is required to guide analytical model development and to verify code performance.

Pressurization of cryogens: A review of current technology and its applicability to low-gravity conditions

NASA Technical Reports Server (NTRS)

Vandresar, N. T.

1992-01-01

A review of technology, history, and current status for pressurized expulsion of cryogenic tankage is presented. Use of tank pressurization to expel cryogenic fluids will continue to be studied for future spacecraft applications over a range of operating conditions in the low-gravity environment. The review examines experimental test results and analytical model development for quiescent and agitated conditions in normal-gravity, followed by a discussion of pressurization and expulsion in low-gravity. Validated, 1-D, finite difference codes exist for the prediction of pressurant mass requirements within the range of quiescent normal-gravity test data. To date, the effects of liquid sloshing have been characterized by tests in normal-gravity, but analytical models capable of predicting pressurant gas requirements remain unavailable. Efforts to develop multidimensional modeling capabilities in both normal and low-gravity have recently occurred. Low-gravity cryogenic fluid transfer experiments are needed to obtain low-gravity pressurized expulsion data. This data is required to guide analytical model development and to verify code performance.

Modeling of classical swirl injector dynamics

NASA Astrophysics Data System (ADS)

Ismailov, Maksud M.

The knowledge of the dynamics of a swirl injector is crucial in designing a stable liquid rocket engine. Since the swirl injector is a complex fluid flow device in itself, not much work has been conducted to describe its dynamics either analytically or by using computational fluid dynamics techniques. Even the experimental observation is limited up to date. Thus far, there exists an analytical linear theory by Bazarov [1], which is based on long-wave disturbances traveling on the free surface of the injector core. This theory does not account for variation of the nozzle reflection coefficient as a function of disturbance frequency, and yields a response function which is strongly dependent on the so called artificial viscosity factor. This causes an uncertainty in designing an injector for the given operational combustion instability frequencies in the rocket engine. In this work, the author has studied alternative techniques to describe the swirl injector response, both analytically and computationally. In the analytical part, by using the linear small perturbation analysis, the entire phenomenon of unsteady flow in swirl injectors is dissected into fundamental components, which are the phenomena of disturbance wave refraction and reflection, and vortex chamber resonance. This reveals the nature of flow instability and the driving factors leading to maximum injector response. In the computational part, by employing the nonlinear boundary element method (BEM), the author sets the boundary conditions such that they closely simulate those in the analytical part. The simulation results then show distinct peak responses at frequencies that are coincident with those resonant frequencies predicted in the analytical part. Moreover, a cold flow test of the injector related to this study also shows a clear growth of instability with its maximum amplitude at the first fundamental frequency predicted both by analytical methods and BEM. It shall be noted however that Bazarov's theory does not predict the resonant peaks. Overall this methodology provides clearer understanding of the injector dynamics compared to Bazarov's. Even though the exact value of response is not possible to obtain at this stage of theoretical, computational, and experimental investigation, this methodology sets the starting point from where the theoretical description of reflection/refraction, resonance, and their interaction between each other may be refined to higher order to obtain its more precise value.

Learning Analytics at Low Cost: At-Risk Student Prediction with Clicker Data and Systematic Proactive Interventions

ERIC Educational Resources Information Center

Choi, Samuel P. M.; Lam, S. S.; Li, Kam Cheong; Wong, Billy T. M.

2018-01-01

While learning analytics (LA) practices have been shown to be practical and effective, most of them require a huge amount of data and effort. This paper reports a case study which demonstrates the feasibility of practising LA at a low cost for instructors to identify at-risk students in an undergraduate business quantitative methods course.…

Charge conservation in electronegativity equalization and its implications for the electrostatic properties of fluctuating-charge models.

PubMed

Chen, Jiahao; Martínez, Todd J

2009-07-28

An analytical solution of fluctuating-charge models using Gaussian elimination allows us to isolate the contribution of charge conservation effects in determining the charge distribution. We use this analytical solution to calculate dipole moments and polarizabilities and show that charge conservation plays a critical role in maintaining the correct translational invariance of the electrostatic properties predicted by these models.

An Advanced Analytical Chemistry Experiment Using Gas Chromatography-Mass Spectrometry, MATLAB, and Chemometrics to Predict Biodiesel Blend Percent Composition

ERIC Educational Resources Information Center

Pierce, Karisa M.; Schale, Stephen P.; Le, Trang M.; Larson, Joel C.

2011-01-01

We present a laboratory experiment for an advanced analytical chemistry course where we first focus on the chemometric technique partial least-squares (PLS) analysis applied to one-dimensional (1D) total-ion-current gas chromatography-mass spectrometry (GC-TIC) separations of biodiesel blends. Then, we focus on n-way PLS (n-PLS) applied to…

Testing a 1-D Analytical Salt Intrusion Model and the Predictive Equation in Malaysian Estuaries

NASA Astrophysics Data System (ADS)

Gisen, Jacqueline Isabella; Savenije, Hubert H. G.

2013-04-01

Little is known about the salt intrusion behaviour in Malaysian estuaries. Study on this topic sometimes requires large amounts of data especially if a 2-D or 3-D numerical models are used for analysis. In poor data environments, 1-D analytical models are more appropriate. For this reason, a fully analytical 1-D salt intrusion model, based on the theory of Savenije in 2005, was tested in three Malaysian estuaries (Bernam, Selangor and Muar) because it is simple and requires minimal data. In order to achieve that, site surveys were conducted in these estuaries during the dry season (June-August) at spring tide by moving boat technique. Data of cross-sections, water levels and salinity were collected, and then analysed with the salt intrusion model. This paper demonstrates a good fit between the simulated and observed salinity distribution for all three estuaries. Additionally, the calibrated Van der Burgh's coefficient K, Dispersion coefficient D0, and salt intrusion length L, for the estuaries also displayed a reasonable correlations with those calculated from the predictive equations. This indicates that not only is the salt intrusion model valid for the case studies in Malaysia but also the predictive model. Furthermore, the results from this study describe the current state of the estuaries with which the Malaysian water authority in Malaysia can make decisions on limiting water abstraction or dredging. Keywords: salt intrusion, Malaysian estuaries, discharge, predictive model, dispersion

Debris flow runup on vertical barriers and adverse slopes

USGS Publications Warehouse

Iverson, Richard M.; George, David L.; Logan, Matthew

2016-01-01

Runup of debris flows against obstacles in their paths is a complex process that involves profound flow deceleration and redirection. We investigate the dynamics and predictability of runup by comparing results from large-scale laboratory experiments, four simple analytical models, and a depth-integrated numerical model (D-Claw). The experiments and numerical simulations reveal the important influence of unsteady, multidimensional flow on runup, and the analytical models highlight key aspects of the underlying physics. Runup against a vertical barrier normal to the flow path is dominated by rapid development of a shock, or jump in flow height, associated with abrupt deceleration of the flow front. By contrast, runup on sloping obstacles is initially dominated by a smooth flux of mass and momentum from the flow body to the flow front, which precedes shock development and commonly increases the runup height. D-Claw simulations that account for the emergence of shocks show that predicted runup heights vary systematically with the adverse slope angle and also with the Froude number and degree of liquefaction (or effective basal friction) of incoming flows. They additionally clarify the strengths and limitations of simplified analytical models. Numerical simulations based on a priori knowledge of the evolving dynamics of incoming flows yield quite accurate runup predictions. Less predictive accuracy is attained in ab initio simulations that compute runup based solely on knowledge of static debris properties in a distant debris flow source area. Nevertheless, the paucity of inputs required in ab initio simulations enhances their prospective value in runup forecasting.

Evidence-based pathology in its second decade: toward probabilistic cognitive computing.

PubMed

Marchevsky, Alberto M; Walts, Ann E; Wick, Mark R

2017-03-01

Evidence-based pathology advocates using a combination of best available data ("evidence") from the literature and personal experience for the diagnosis, estimation of prognosis, and assessment of other variables that impact individual patient care. Evidence-based pathology relies on systematic reviews of the literature, evaluation of the quality of evidence as categorized by evidence levels and statistical tools such as meta-analyses, estimates of probabilities and odds, and others. However, it is well known that previously "statistically significant" information usually does not accurately forecast the future for individual patients. There is great interest in "cognitive computing" in which "data mining" is combined with "predictive analytics" designed to forecast future events and estimate the strength of those predictions. This study demonstrates the use of IBM Watson Analytics software to evaluate and predict the prognosis of 101 patients with typical and atypical pulmonary carcinoid tumors in which Ki-67 indices have been determined. The results obtained with this system are compared with those previously reported using "routine" statistical software and the help of a professional statistician. IBM Watson Analytics interactively provides statistical results that are comparable to those obtained with routine statistical tools but much more rapidly, with considerably less effort and with interactive graphics that are intuitively easy to apply. It also enables analysis of natural language variables and yields detailed survival predictions for patient subgroups selected by the user. Potential applications of this tool and basic concepts of cognitive computing are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Broadband Noise Predictions Based on a New Aeroacoustic Formulation

NASA Technical Reports Server (NTRS)

Casper, J.; Farassat, F.

2002-01-01

A new analytic result in acoustics called 'Formulation 1B,' proposed by Farassat, is used to compute the loading noise from an unsteady surface pressure distribution on a thin airfoil in the time domain. This formulation is a new solution of the Ffowcs Williams-Hawkings equation with the loading source term. The formulation contains a far-field surface integral that depends on the time derivative and the surface gradient of the pressure on the airfoil, as well as a contour integral on the boundary of the airfoil surface. As a first test case, the new formulation is used to compute the noise radiated from a flat plate, moving through a sinusoidal gust of constant frequency. The unsteady surface pressure for this test case is specified analytically from a result that is based on linear airfoil theory. This test case is used to examine the velocity scaling properties of Formulation 1B, and to demonstrate its equivalence to Formulation 1A, of Farassat. The new acoustic formulation, again with an analytic surface pressure, is then used to predict broadband noise radiated from an airfoil immersed in homogeneous turbulence. The results are compared with experimental data previously reported by Paterson and Amiet. Good agreement between predictions and measurements is obtained. The predicted results also agree very well with those of Paterson and Amiet, who used a frequency-domain approach. Finally, an alternative form of Formulation 1B is described for statistical analysis of broadband noise.

Retention prediction and separation optimization under multilinear gradient elution in liquid chromatography with Microsoft Excel macros.

PubMed

Fasoula, S; Zisi, Ch; Gika, H; Pappa-Louisi, A; Nikitas, P

2015-05-22

A package of Excel VBA macros have been developed for modeling multilinear gradient retention data obtained in single or double gradient elution mode by changing organic modifier(s) content and/or eluent pH. For this purpose, ten chromatographic models were used and four methods were adopted for their application. The methods were based on (a) the analytical expression of the retention time, provided that this expression is available, (b) the retention times estimated using the Nikitas-Pappa approach, (c) the stepwise approximation, and (d) a simple numerical approximation involving the trapezoid rule for integration of the fundamental equation for gradient elution. For all these methods, Excel VBA macros have been written and implemented using two different platforms; the fitting and the optimization platform. The fitting platform calculates not only the adjustable parameters of the chromatographic models, but also the significance of these parameters and furthermore predicts the analyte elution times. The optimization platform determines the gradient conditions that lead to the optimum separation of a mixture of analytes by using the Solver evolutionary mode, provided that proper constraints are set in order to obtain the optimum gradient profile in the minimum gradient time. The performance of the two platforms was tested using experimental and artificial data. It was found that using the proposed spreadsheets, fitting, prediction, and optimization can be performed easily and effectively under all conditions. Overall, the best performance is exhibited by the analytical and Nikitas-Pappa's methods, although the former cannot be used under all circumstances. Copyright © 2015 Elsevier B.V. All rights reserved.

The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations.

PubMed

Mackay, Michael M

2016-09-01

This article offers a correlation matrix of meta-analytic estimates between various employee job attitudes (i.e., Employee engagement, job satisfaction, job involvement, and organizational commitment) and indicators of employee effectiveness (i.e., Focal performance, contextual performance, turnover intention, and absenteeism). The meta-analytic correlations in the matrix are based on over 1100 individual studies representing over 340,000 employees. Data was collected worldwide via employee self-report surveys. Structural path analyses based on the matrix, and the interpretation of the data, can be found in "Investigating the incremental validity of employee engagement in the prediction of employee effectiveness: a meta-analytic path analysis" (Mackay et al., 2016) [1].

Design and Analysis of a Preconcentrator for the ChemLab

DOE Office of Scientific and Technical Information (OSTI.GOV)

WONG,CHUNGNIN C.; FLEMMING,JEB H.; MANGINELL,RONALD P.

2000-07-17

Preconcentration is a critical analytical procedure when designing a microsystem for trace chemical detection, because it can purify a sample mixture and boost the small analyte concentration to a much higher level allowing a better analysis. This paper describes the development of a micro-fabricated planar preconcentrator for the {mu}ChemLab{trademark} at Sandia. To guide the design, an analytical model to predict the analyte transport, adsorption and resorption process in the preconcentrator has been developed. Experiments have also been conducted to analyze the adsorption and resorption process and to validate the model. This combined effort of modeling, simulation, and testing has ledmore » us to build a reliable, efficient preconcentrator with good performance.« less

An analytical and experimental evaluation of the plano-cylindrical Fresnel lens solar concentrator

NASA Technical Reports Server (NTRS)

Hastings, L. J.; Allums, S. L.; Cosby, R. M.

1976-01-01

Plastic Fresnel lenses for solar concentration are attractive because of potential for low-cost mass production. An analytical and experimental evaluation of line-focusing Fresnel lenses with application potential in the 200 to 370 C range is reported. Analytical techniques were formulated to assess the solar transmission and imaging properties of a grooves-down lens. Experimentation was based primarily on a 56 cm-wide lens with f-number 1.0. A sun-tracking heliostat provided a non-moving solar source. Measured data indicated more spreading at the profile base than analytically predicted. The measured and computed transmittances were 85 and 87% respectively. Preliminary testing with a second lens (1.85 m) indicated that modified manufacturing techniques corrected the profile spreading problem.

Analytical and simulator study of advanced transport

NASA Technical Reports Server (NTRS)

Levison, W. H.; Rickard, W. W.

1982-01-01

An analytic methodology, based on the optimal-control pilot model, was demonstrated for assessing longitidunal-axis handling qualities of transport aircraft in final approach. Calibration of the methodology is largely in terms of closed-loop performance requirements, rather than specific vehicle response characteristics, and is based on a combination of published criteria, pilot preferences, physical limitations, and engineering judgment. Six longitudinal-axis approach configurations were studied covering a range of handling qualities problems, including the presence of flexible aircraft modes. The analytical procedure was used to obtain predictions of Cooper-Harper ratings, a solar quadratic performance index, and rms excursions of important system variables.

Parametric study of minimum reactor mass in energy-storage dc-to-dc converters

NASA Technical Reports Server (NTRS)

Wong, R. C.; Owen, H. A., Jr.; Wilson, T. G.

1981-01-01

Closed-form analytical solutions for the design equations of a minimum-mass reactor for a two-winding voltage-or-current step-up converter are derived. A quantitative relationship between the three parameters - minimum total reactor mass, maximum output power, and switching frequency - is extracted from these analytical solutions. The validity of the closed-form solution is verified by a numerical minimization procedure. A computer-aided design procedure using commercially available toroidal cores and magnet wires is also used to examine how the results from practical designs follow the predictions of the analytical solutions.

Controlling the spectral shape of nonlinear Thomson scattering with proper laser chirping

DOE PAGES

Rykovanov, S. G.; Geddes, C. G. R.; Schroeder, C. B.; ...

2016-03-18

Effects of nonlinearity in Thomson scattering of a high intensity laser pulse from electrons are analyzed. Analytic expressions for laser pulse shaping in frequency (chirping) are obtained which control spectrum broadening for high laser pulse intensities. These analytic solutions allow prediction of the spectral form and required laser parameters to avoid broadening. Results of analytical and numerical calculations agree well. The control over the scattered radiation bandwidth allows narrow bandwidth sources to be produced using high scattering intensities, which in turn greatly improves scattering yield for future x- and gamma-ray sources.

Improvement of analytical dynamic models using modal test data

NASA Technical Reports Server (NTRS)

Berman, A.; Wei, F. S.; Rao, K. V.

1980-01-01

A method developed to determine maximum changes in analytical mass and stiffness matrices to make them consistent with a set of measured normal modes and natural frequencies is presented. The corrected model will be an improved base for studies of physical changes, boundary condition changes, and for prediction of forced responses. The method features efficient procedures not requiring solutions of the eigenvalue problem, and the ability to have more degrees of freedom than the test data. In addition, modal displacements are obtained for all analytical degrees of freedom, and the frequency dependence of the coordinate transformations is properly treated.

Maximum of a Fractional Brownian Motion: Analytic Results from Perturbation Theory.

PubMed

Delorme, Mathieu; Wiese, Kay Jörg

2015-11-20

Fractional Brownian motion is a non-Markovian Gaussian process X_{t}, indexed by the Hurst exponent H. It generalizes standard Brownian motion (corresponding to H=1/2). We study the probability distribution of the maximum m of the process and the time t_{max} at which the maximum is reached. They are encoded in a path integral, which we evaluate perturbatively around a Brownian, setting H=1/2+ϵ. This allows us to derive analytic results beyond the scaling exponents. Extensive numerical simulations for different values of H test these analytical predictions and show excellent agreement, even for large ϵ.

An analytical framework to assist decision makers in the use of forest ecosystem model predictions

USGS Publications Warehouse

Larocque, Guy R.; Bhatti, Jagtar S.; Ascough, J.C.; Liu, J.; Luckai, N.; Mailly, D.; Archambault, L.; Gordon, Andrew M.

2011-01-01

The predictions from most forest ecosystem models originate from deterministic simulations. However, few evaluation exercises for model outputs are performed by either model developers or users. This issue has important consequences for decision makers using these models to develop natural resource management policies, as they cannot evaluate the extent to which predictions stemming from the simulation of alternative management scenarios may result in significant environmental or economic differences. Various numerical methods, such as sensitivity/uncertainty analyses, or bootstrap methods, may be used to evaluate models and the errors associated with their outputs. However, the application of each of these methods carries unique challenges which decision makers do not necessarily understand; guidance is required when interpreting the output generated from each model. This paper proposes a decision flow chart in the form of an analytical framework to help decision makers apply, in an orderly fashion, different steps involved in examining the model outputs. The analytical framework is discussed with regard to the definition of problems and objectives and includes the following topics: model selection, identification of alternatives, modelling tasks and selecting alternatives for developing policy or implementing management scenarios. Its application is illustrated using an on-going exercise in developing silvicultural guidelines for a forest management enterprise in Ontario, Canada.

Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units.

PubMed

Yun Chen; Hui Yang

2014-01-01

The rapid advancements of biomedical instrumentation and healthcare technology have resulted in data-rich environments in hospitals. However, the meaningful information extracted from rich datasets is limited. There is a dire need to go beyond current medical practices, and develop data-driven methods and tools that will enable and help (i) the handling of big data, (ii) the extraction of data-driven knowledge, (iii) the exploitation of acquired knowledge for optimizing clinical decisions. This present study focuses on the prediction of mortality rates in Intensive Care Units (ICU) using patient-specific healthcare recordings. It is worth mentioning that postsurgical monitoring in ICU leads to massive datasets with unique properties, e.g., variable heterogeneity, patient heterogeneity, and time asyncronization. To cope with the challenges in ICU datasets, we developed the postsurgical decision support system with a series of analytical tools, including data categorization, data pre-processing, feature extraction, feature selection, and predictive modeling. Experimental results show that the proposed data-driven methodology outperforms traditional approaches and yields better results based on the evaluation of real-world ICU data from 4000 subjects in the database. This research shows great potentials for the use of data-driven analytics to improve the quality of healthcare services.

A crack-closure model for predicting fatigue-crack growth under aircraft spectrum loading

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.

1981-01-01

The development and application of an analytical model of cycle crack growth is presented that includes the effects of crack closure. The model was used to correlate crack growth rates under constant amplitude loading and to predict crack growth under aircraft spectrum loading on 2219-T851 aluminum alloy sheet material. The predicted crack growth lives agreed well with experimental data. The ratio of predicted to experimental lives ranged from 0.66 to 1.48. These predictions were made using data from an ASTM E24.06.01 Round Robin.

Analytical Modeling for the Bending Resonant Frequency of Multilayered Microresonators with Variable Cross-Section

PubMed Central

Herrera-May, Agustín L.; Aguilera-Cortés, Luz A.; Plascencia-Mora, Hector; Rodríguez-Morales, Ángel L.; Lu, Jian

2011-01-01

Multilayered microresonators commonly use sensitive coating or piezoelectric layers for detection of mass and gas. Most of these microresonators have a variable cross-section that complicates the prediction of their fundamental resonant frequency (generally of the bending mode) through conventional analytical models. In this paper, we present an analytical model to estimate the first resonant frequency and deflection curve of single-clamped multilayered microresonators with variable cross-section. The analytical model is obtained using the Rayleigh and Macaulay methods, as well as the Euler-Bernoulli beam theory. Our model is applied to two multilayered microresonators with piezoelectric excitation reported in the literature. Both microresonators are composed by layers of seven different materials. The results of our analytical model agree very well with those obtained from finite element models (FEMs) and experimental data. Our analytical model can be used to determine the suitable dimensions of the microresonator’s layers in order to obtain a microresonator that operates at a resonant frequency necessary for a particular application. PMID:22164071

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure-Retention Relationships and Design of Experiments.

PubMed

Taraji, Maryam; Haddad, Paul R; Amos, Ruth I J; Talebi, Mohammad; Szucs, Roman; Dolan, John W; Pohl, Chris A

2017-02-07

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R 2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.

Learning and cognitive styles in web-based learning: theory, evidence, and application.

PubMed

Cook, David A

2005-03-01

Cognitive and learning styles (CLS) have long been investigated as a basis to adapt instruction and enhance learning. Web-based learning (WBL) can reach large, heterogenous audiences, and adaptation to CLS may increase its effectiveness. Adaptation is only useful if some learners (with a defined trait) do better with one method and other learners (with a complementary trait) do better with another method (aptitude-treatment interaction). A comprehensive search of health professions education literature found 12 articles on CLS in computer-assisted learning and WBL. Because so few reports were found, research from non-medical education was also included. Among all the reports, four CLS predominated. Each CLS construct was used to predict relationships between CLS and WBL. Evidence was then reviewed to support or refute these predictions. The wholist-analytic construct shows consistent aptitude-treatment interactions consonant with predictions (wholists need structure, a broad-before-deep approach, and social interaction, while analytics need less structure and a deep-before-broad approach). Limited evidence for the active-reflective construct suggests aptitude-treatment interaction, with active learners doing better with interactive learning and reflective learners doing better with methods to promote reflection. As predicted, no consistent interaction between the concrete-abstract construct and computer format was found, but one study suggests that there is interaction with instructional method. Contrary to predictions, no interaction was found for the verbal-imager construct. Teachers developing WBL activities should consider assessing and adapting to accommodate learners defined by the wholist-analytic and active-reflective constructs. Other adaptations should be considered experimental. Further WBL research could clarify the feasibility and effectiveness of assessing and adapting to CLS.

Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs.

PubMed

Bayliss, Elizabeth A; Powers, J David; Ellis, Jennifer L; Barrow, Jennifer C; Strobel, MaryJo; Beck, Arne

2016-01-01

Identifying care needs for newly enrolled or newly insured individuals is important under the Affordable Care Act. Systematically collected patient-reported information can potentially identify subgroups with specific care needs prior to service use. We conducted a retrospective cohort investigation of 6,047 individuals who completed a 10-question needs assessment upon initial enrollment in Kaiser Permanente Colorado (KPCO), a not-for-profit integrated delivery system, through the Colorado State Individual Exchange. We used responses from the Brief Health Questionnaire (BHQ), to develop a predictive model for cost for receiving care in the top 25 percent, then applied cluster analytic techniques to identify different high-cost subpopulations. Per-member, per-month cost was measured from 6 to 12 months following BHQ response. BHQ responses significantly predictive of high-cost care included self-reported health status, functional limitations, medication use, presence of 0-4 chronic conditions, self-reported emergency department (ED) use during the prior year, and lack of prior insurance. Age, gender, and deductible-based insurance product were also predictive. The largest possible range of predicted probabilities of being in the top 25 percent of cost was 3.5 percent to 96.4 percent. Within the top cost quartile, examples of potentially actionable clusters of patients included those with high morbidity, prior utilization, depression risk and financial constraints; those with high morbidity, previously uninsured individuals with few financial constraints; and relatively healthy, previously insured individuals with medication needs. Applying sequential predictive modeling and cluster analytic techniques to patient-reported information can identify subgroups of individuals within heterogeneous populations who may benefit from specific interventions to optimize initial care delivery.

Practical limitations on the use of diurnal temperature signals to quantify groundwater upwelling

USGS Publications Warehouse

Briggs, Martin A.; Lautz, Laura K.; Buckley, Sean F.; Lane, John W.

2014-01-01

Groundwater upwelling to streams creates unique habitat by influencing stream water quality and temperature; upwelling zones also serve as vectors for contamination when groundwater is degraded. Temperature time series data acquired along vertical profiles in the streambed have been applied to simple analytical models to determine rates of vertical fluid flux. These models are based on the downward propagation characteristics (amplitude attenuation and phase-lag) of the surface diurnal signal. Despite the popularity of these models, there are few published characterizations of moderate-to-strong upwelling. We attribute this limitation to the thermodynamics of upwelling, under which the downward conductive signal transport from the streambed interface occurs opposite the upward advective fluid flux. Governing equations describing the advection–diffusion of heat within the streambed predict that under upwelling conditions, signal amplitude attenuation will increase, but, counterintuitively, phase-lag will decrease. Therefore the extinction (measurable) depth of the diurnal signal is very shallow, but phase lag is also short, yielding low signal to noise ratio and poor model sensitivity. Conversely, amplitude attenuation over similar sensor spacing is strong, yielding greater potential model sensitivity. Here we present streambed thermal time series over a range of moderate to strong upwelling sites in the Quashnet River, Cape Cod, Massachusetts. The predicted inverse relationship between phase-lag and rate of upwelling was observed in the field data over a range of conditions, but the observed phase-lags were consistently shorter than predicted. Analytical solutions for fluid flux based on signal amplitude attenuation return results consistent with numerical models and physical seepage meters, but the phase-lag analytical model results are generally unreasonable. Through numerical modeling we explore reasons why phase-lag may have been over-predicted by the analytical models, and develop guiding relations of diurnal temperature signal extinction depth based on stream diurnal signal amplitude, upwelling magnitude, and streambed thermal properties that will be useful in designing future experiments.

Nearshore Tsunami Inundation Model Validation: Toward Sediment Transport Applications

USGS Publications Warehouse

Apotsos, Alex; Buckley, Mark; Gelfenbaum, Guy; Jaffe, Bruce; Vatvani, Deepak

2011-01-01

Model predictions from a numerical model, Delft3D, based on the nonlinear shallow water equations are compared with analytical results and laboratory observations from seven tsunami-like benchmark experiments, and with field observations from the 26 December 2004 Indian Ocean tsunami. The model accurately predicts the magnitude and timing of the measured water levels and flow velocities, as well as the magnitude of the maximum inundation distance and run-up, for both breaking and non-breaking waves. The shock-capturing numerical scheme employed describes well the total decrease in wave height due to breaking, but does not reproduce the observed shoaling near the break point. The maximum water levels observed onshore near Kuala Meurisi, Sumatra, following the 26 December 2004 tsunami are well predicted given the uncertainty in the model setup. The good agreement between the model predictions and the analytical results and observations demonstrates that the numerical solution and wetting and drying methods employed are appropriate for modeling tsunami inundation for breaking and non-breaking long waves. Extension of the model to include sediment transport may be appropriate for long, non-breaking tsunami waves. Using available sediment transport formulations, the sediment deposit thickness at Kuala Meurisi is predicted generally within a factor of 2.

Broadband Noise Prediction When Turbulence Simulation Is Available - Derivation of Formulation 2B and Its Statistical Analysis

NASA Technical Reports Server (NTRS)

Farassat, Fereidoun; Casper, Jay H.

2012-01-01

We show that a simple modification of Formulation 1 of Farassat results in a new analytic expression that is highly suitable for broadband noise prediction when extensive turbulence simulation is available. This result satisfies all the stringent requirements, such as permitting the use of the exact geometry and kinematics of the moving body, that we have set as our goal in the derivation of useful acoustic formulas for the prediction of rotating blade and airframe noise. We also derive a simple analytic expression for the autocorrelation of the acoustic pressure that is valid in the near and far fields. Our analysis is based on the time integral of the acoustic pressure that can easily be obtained at any resolution for any observer time interval and digitally analyzed for broadband noise prediction. We have named this result as Formulation 2B of Farassat. One significant consequence of Formulation 2B is the derivation of the acoustic velocity potential for the thickness and loading terms of the Ffowcs Williams-Hawkings (FW-H) equation. This will greatly enhance the usefulness of the Fast Scattering Code (FSC) by providing a high fidelity boundary condition input for scattering predictions.

Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

NASA Astrophysics Data System (ADS)

ElSaid, AbdElRahman Ahmed

This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

A method of predicting flow rates required to achieve anti-icing performance with a porous leading edge ice protection system

NASA Technical Reports Server (NTRS)

Kohlman, D. L.; Albright, A. E.

1983-01-01

An analytical method was developed for predicting minimum flow rates required to provide anti-ice protection with a porous leading edge fluid ice protection system. The predicted flow rates compare with an average error of less than 10 percent to six experimentally determined flow rates from tests in the NASA Icing Research Tunnel on a general aviation wing section.

Predicting Sasang Constitution Using Body-Shape Information

PubMed Central

Jang, Eunsu; Do, Jun-Hyeong; Jin, HeeJeong; Park, KiHyun; Ku, Boncho; Lee, Siwoo; Kim, Jong Yeol

2012-01-01

Objectives. Body measurement plays a pivotal role not only in the diagnosis of disease but also in the classification of typology. Sasang constitutional medicine, which is one of the forms of Traditional Korean Medicine, is considered to be strongly associated with body shape. We attempted to determine whether a Sasang constitutional analytic tool based on body shape information (SCAT-B) could predict Sasang constitution (SC). Methods. After surveying 23 Oriental medical clinics, 2,677 subjects were recruited and body shape information was collected. The SCAT-Bs for males and females were developed using multinomial logistic regression. Stepwise forward-variable selection was applied using the score statistic and Wald's test. Results. The predictive rates of the SCAT-B for Tae-eumin (TE), Soeumin (SE), and Soyangin (SY) types in males and females were 80.2%, 56.9%, and 37.7% (males) and 69.3%, 38.9%, and 50.0% (females) in the training set and were 74%, 70.1%, and 35% (males), and 67.4%, 66.3%, and 53.7% (females) in the test set, respectively. Higher constitutional probability scores showed a trend for association with higher predictability. Conclusions. This study shows that the Sasang constitutional analytic tool, which is based on body shape information, may be relatively highly predictive of TE type but may be less predictive when used for SY type. PMID:22792124

Long Term Evolution of Planetary Systems with a Terrestrial Planet and a Giant Planet

NASA Technical Reports Server (NTRS)

Georgakarakos, Nikolaos; Dobbs-Dixon, Ian; Way, Michael J.

2016-01-01

We study the long term orbital evolution of a terrestrial planet under the gravitational perturbations of a giant planet. In particular, we are interested in situations where the two planets are in the same plane and are relatively close. We examine both possible configurations: the giant planet orbit being either outside or inside the orbit of the smaller planet. The perturbing potential is expanded to high orders and an analytical solution of the terrestrial planetary orbit is derived. The analytical estimates are then compared against results from the numerical integration of the full equations of motion and we find that the analytical solution works reasonably well. An interesting finding is that the new analytical estimates improve greatly the predictions for the timescales of the orbital evolution of the terrestrial planet compared to an octupole order expansion. Finally, we briefly discuss possible applications of the analytical estimates in astrophysical problems.

Systematization of the mass spectra for speciation of inorganic salts with static secondary ion mass spectrometry.

PubMed

Van Ham, Rita; Van Vaeck, Luc; Adams, Freddy C; Adriaens, Annemie

2004-05-01

The analytical use of mass spectra from static secondary ion mass spectrometry for the molecular identification of inorganic analytes in real life surface layers and microobjects requires an empirical insight in the signals to be expected from a given compound. A comprehensive database comprising over 50 salts has been assembled to complement prior data on oxides. The present study allows the systematic trends in the relationship between the detected signals and molecular composition of the analyte to be delineated. The mass spectra provide diagnostic information by means of atomic ions, structural fragments, molecular ions, and adduct ions of the analyte neutrals. The prediction of mass spectra from a given analyte must account for the charge state of the ions in the salt, the formation of oxide-type neutrals from oxy salts, and the occurrence of oxidation-reduction processes.

Analytical study of the heat loss attenuation by clothing on thermal manikins under radiative heat loads.

PubMed

Den Hartog, Emiel A; Havenith, George

2010-01-01

For wearers of protective clothing in radiation environments there are no quantitative guidelines available for the effect of a radiative heat load on heat exchange. Under the European Union funded project ThermProtect an analytical effort was defined to address the issue of radiative heat load while wearing protective clothing. As within the ThermProtect project much information has become available from thermal manikin experiments in thermal radiation environments, these sets of experimental data are used to verify the analytical approach. The analytical approach provided a good prediction of the heat loss in the manikin experiments, 95% of the variance was explained by the model. The model has not yet been validated at high radiative heat loads and neglects some physical properties of the radiation emissivity. Still, the analytical approach provides a pragmatic approach and may be useful for practical implementation in protective clothing standards for moderate thermal radiation environments.

Aquatic concentrations of chemical analytes compared to ...

EPA Pesticide Factsheets

We describe screening level estimates of potential aquatic toxicity posed by 227 chemical analytes that were measured in 25 ambient water samples collected as part of a joint USGS/USEPA drinking water plant study. Measured concentrations were compared to biological effect concentration (EC) estimates, including USEPA aquatic life criteria, effective plasma concentrations of pharmaceuticals, published toxicity data summarized in the USEPA ECOTOX database, and chemical structure-based predictions. Potential dietary exposures were estimated using a generic 3-tiered food web accumulation scenario. For many analytes, few or no measured effect data were found, and for some analytes, reporting limits exceeded EC estimates, limiting the scope of conclusions. Results suggest occasional occurrence above ECs for copper, aluminum, strontium, lead, uranium, and nitrate. Sparse effect data for manganese, antimony, and vanadium suggest that these analytes may occur above ECs, but additional effect data would be desirable to corroborate EC estimates. These conclusions were not affected by bioaccumulation estimates. No organic analyte concentrations were found to exceed EC estimates, but ten analytes had concentrations in excess of 1/10th of their respective EC: triclocarban, norverapamil, progesterone, atrazine, metolachlor, triclosan, para-nonylphenol, ibuprofen, venlafaxine, and amitriptyline, suggesting more detailed characterization of these analytes. Purpose: to provide sc

Aquatic concentrations of chemical analytes compared to ecotoxicity estimates

USGS Publications Warehouse

Kostich, Mitchell S.; Flick, Robert W.; Angela L. Batt,; Mash, Heath E.; Boone, J. Scott; Furlong, Edward T.; Kolpin, Dana W.; Glassmeyer, Susan T.

2017-01-01

We describe screening level estimates of potential aquatic toxicity posed by 227 chemical analytes that were measured in 25 ambient water samples collected as part of a joint USGS/USEPA drinking water plant study. Measured concentrations were compared to biological effect concentration (EC) estimates, including USEPA aquatic life criteria, effective plasma concentrations of pharmaceuticals, published toxicity data summarized in the USEPA ECOTOX database, and chemical structure-based predictions. Potential dietary exposures were estimated using a generic 3-tiered food web accumulation scenario. For many analytes, few or no measured effect data were found, and for some analytes, reporting limits exceeded EC estimates, limiting the scope of conclusions. Results suggest occasional occurrence above ECs for copper, aluminum, strontium, lead, uranium, and nitrate. Sparse effect data for manganese, antimony, and vanadium suggest that these analytes may occur above ECs, but additional effect data would be desirable to corroborate EC estimates. These conclusions were not affected by bioaccumulation estimates. No organic analyte concentrations were found to exceed EC estimates, but ten analytes had concentrations in excess of 1/10th of their respective EC: triclocarban, norverapamil, progesterone, atrazine, metolachlor, triclosan, para-nonylphenol, ibuprofen, venlafaxine, and amitriptyline, suggesting more detailed characterization of these analytes.

Generalized analytical solutions to multispecies transport equations with scale-dependent dispersion coefficients subject to time-dependent boundary conditions

NASA Astrophysics Data System (ADS)

Chen, J. S.; Chiang, S. Y.; Liang, C. P.

2017-12-01

It is essential to develop multispecies transport analytical models based on a set of advection-dispersion equations (ADEs) coupled with sequential first-order decay reactions for the synchronous prediction of plume migrations of both parent and its daughter species of decaying contaminants such as radionuclides, dissolved chlorinated organic compounds, pesticides and nitrogen. Although several analytical models for multispecies transport have already been reported, those currently available in the literature have primarily been derived based on ADEs with constant dispersion coefficients. However, there have been a number of studies demonstrating that the dispersion coefficients increase with the solute travel distance as a consequence of variation in the hydraulic properties of the porous media. This study presents novel analytical models for multispecies transport with distance-dependent dispersion coefficients. The correctness of the derived analytical models is confirmed by comparing them against the numerical models. Results show perfect agreement between the analytical and numerical models. Comparison of our new analytical model for multispecies transport with scale-dependent dispersion to an analytical model with constant dispersion is made to illustrate the effects of the dispersion coefficients on the multispecies transport of decaying contaminants.

Aquatic concentrations of chemical analytes compared to ecotoxicity estimates.

PubMed

Kostich, Mitchell S; Flick, Robert W; Batt, Angela L; Mash, Heath E; Boone, J Scott; Furlong, Edward T; Kolpin, Dana W; Glassmeyer, Susan T

2017-02-01

We describe screening level estimates of potential aquatic toxicity posed by 227 chemical analytes that were measured in 25 ambient water samples collected as part of a joint USGS/USEPA drinking water plant study. Measured concentrations were compared to biological effect concentration (EC) estimates, including USEPA aquatic life criteria, effective plasma concentrations of pharmaceuticals, published toxicity data summarized in the USEPA ECOTOX database, and chemical structure-based predictions. Potential dietary exposures were estimated using a generic 3-tiered food web accumulation scenario. For many analytes, few or no measured effect data were found, and for some analytes, reporting limits exceeded EC estimates, limiting the scope of conclusions. Results suggest occasional occurrence above ECs for copper, aluminum, strontium, lead, uranium, and nitrate. Sparse effect data for manganese, antimony, and vanadium suggest that these analytes may occur above ECs, but additional effect data would be desirable to corroborate EC estimates. These conclusions were not affected by bioaccumulation estimates. No organic analyte concentrations were found to exceed EC estimates, but ten analytes had concentrations in excess of 1/10th of their respective EC: triclocarban, norverapamil, progesterone, atrazine, metolachlor, triclosan, para-nonylphenol, ibuprofen, venlafaxine, and amitriptyline, suggesting more detailed characterization of these analytes. Published by Elsevier B.V.

An analytical and experimental investigation of sandwich composites subjected to low-velocity impact

NASA Astrophysics Data System (ADS)

Anderson, Todd Alan

1999-12-01

This study involves an experimental and analytical investigation of low-velocity impact phenomenon in sandwich composite structures. The analytical solution of a three-dimensional finite-geometry multi-layer specially orthotropic panel subjected to static and transient transverse loading cases is presented. The governing equations of the static and dynamic formulations are derived from Reissner's functional and solved by enforcing the continuity of traction and displacement components between adjacent layers. For the dynamic loading case, the governing equations are solved by applying Fourier or Laplace transformation in time. Additionally, the static solution is extended to solve the contact problem between the sandwich laminate and a rigid sphere. An iterative method is employed to determine the sphere's unknown contact area and pressure distribution. A failure criterion is then applied to the sandwich laminate's stress and strain field to predict impact damage. The analytical accuracy of the present study is verified through comparisons with finite element models, other analyses, and through experimentation. Low-velocity impact tests were conducted to characterize the type and extent of the damage observed in a variety of sandwich configurations with graphite/epoxy face sheets and foam or honeycomb cores. Correlation of the residual indentation and cross-sectional views of the impacted specimens provides a criterion for the extent of damage. Quasi-static indentation tests are also performed and show excellent agreement when compared with the analytical predictions. Finally, piezoelectric polyvinylidene fluoride (PVF2) film sensors are found to be effective in detecting low-velocity impact.

Analytical modeling and experimental validation of a magnetorheological mount

NASA Astrophysics Data System (ADS)

Nguyen, The; Ciocanel, Constantin; Elahinia, Mohammad

2009-03-01

Magnetorheological (MR) fluid has been increasingly researched and applied in vibration isolation devices. To date, the suspension system of several high performance vehicles has been equipped with MR fluid based dampers and research is ongoing to develop MR fluid based mounts for engine and powertrain isolation. MR fluid based devices have received attention due to the MR fluid's capability to change its properties in the presence of a magnetic field. This characteristic places MR mounts in the class of semiactive isolators making them a desirable substitution for the passive hydraulic mounts. In this research, an analytical model of a mixed-mode MR mount was constructed. The magnetorheological mount employs flow (valve) mode and squeeze mode. Each mode is powered by an independent electromagnet, so one mode does not affect the operation of the other. The analytical model was used to predict the performance of the MR mount with different sets of parameters. Furthermore, in order to produce the actual prototype, the analytical model was used to identify the optimal geometry of the mount. The experimental phase of this research was carried by fabricating and testing the actual MR mount. The manufactured mount was tested to evaluate the effectiveness of each mode individually and in combination. The experimental results were also used to validate the ability of the analytical model in predicting the response of the MR mount. Based on the observed response of the mount a suitable controller can be designed for it. However, the control scheme is not addressed in this study.

Comparison of theoretically predicted lateral-directional aerodynamic characteristics with full-scale wind tunnel data on the ATLIT airplane

NASA Technical Reports Server (NTRS)

Griswold, M.; Roskam, J.

1980-01-01

An analytical method is presented for predicting lateral-directional aerodynamic characteristics of light twin engine propeller-driven airplanes. This method is applied to the Advanced Technology Light Twin Engine airplane. The calculated characteristics are correlated against full-scale wind tunnel data. The method predicts the sideslip derivatives fairly well, although angle of attack variations are not well predicted. Spoiler performance was predicted somewhat high but was still reasonable. The rudder derivatives were not well predicted, in particular the effect of angle of attack. The predicted dynamic derivatives could not be correlated due to lack of experimental data.

Outgassing and dimensional changes of polymer matrix composites in space

NASA Technical Reports Server (NTRS)

Tennyson, R. C.; Matthews, R.

1993-01-01

A thermal-vacuum outgassing model and test protocol for predicting outgassing times and dimensional changes for polymer matrix composites is described. Experimental results derived from a 'control' sample are used to provide the basis for analytical predictions to compare with the outgassing response of Long Duration Exposure Facility (LDEF) flight samples.

Predicting Plausible Impacts of Sets of Climate and Land Use Change Scenarios on Water Resources

EPA Science Inventory

Global changes in climate and land use can alTect the quantity and quality of water resources. Hence, we need a methodology to predict these ramifications. Using the Little Miami River (LMR) watershed as a case study, this paper describes a spatial analytical approach integrating...

Composite Solid Propellant Predictability and Quality Assurance

NASA Technical Reports Server (NTRS)

Ramohalli, Kumar

1989-01-01

Reports are presented at the meeting at the University of Arizona on the study of predictable and reliable solid rocket motors. The following subject areas were covered: present state and trends in the research of solid propellants; the University of Arizona program in solid propellants, particularly in mixing (experimental and analytical results are presented).

Predictive Data Tools Find Uses in Schools

ERIC Educational Resources Information Center

Sparks, Sarah D.

2011-01-01

The use of analytic tools to predict student performance is exploding in higher education, and experts say the tools show even more promise for K-12 schools, in everything from teacher placement to dropout prevention. Use of such statistical techniques is hindered in precollegiate schools, however, by a lack of researchers trained to help…

Using landscape disturbance and succession models to support forest management

Treesearch

Eric J. Gustafson; Brian R. Sturtevant; Anatoly S. Shvidenko; Robert M. Scheller

2010-01-01

Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales. These interactions can be of such complexity that predictions of future forest ecosystem states are beyond the analytical capability of the human mind. Landscape disturbance and succession models (LDSM) are predictive and...

An Other Perspective on Personality: Meta-Analytic Integration of Observers' Accuracy and Predictive Validity

ERIC Educational Resources Information Center

Connelly, Brian S.; Ones, Deniz S.

2010-01-01

The bulk of personality research has been built from self-report measures of personality. However, collecting personality ratings from other-raters, such as family, friends, and even strangers, is a dramatically underutilized method that allows better explanation and prediction of personality's role in many domains of psychology. Drawing…

Does Psychopathy Predict Institutional Misconduct among Adults?: A Meta-Analytic Investigation

ERIC Educational Resources Information Center

Guy, Laura S.; Edens, John F.; Anthony, Christine; Douglas, Kevin S.

2005-01-01

Narrative reviews have raised several questions regarding the predictive validity of the Hare Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 2003) and related scales in institutional settings. In this meta-analysis, the authors coded 273 effect sizes to investigate the association between the Hare scales and a hierarchy of increasingly specific…

A Comparison of Tension and Compression Creep in a Polymeric Composite and the Effects of Physical Aging on Creep Behavior

NASA Technical Reports Server (NTRS)

Gates, Thomas S.; Veazie, David R.; Brinson, L. Catherine

1996-01-01

Experimental and analytical methods were used to investigate the similarities and differences of the effects of physical aging on creep compliance of IM7/K3B composite loaded in tension and compression. Two matrix dominated loading modes, shear and transverse, were investigated for two load cases, tension and compression. The tests, run over a range of sub-glass transition temperatures, provided material constants, material master curves and aging related parameters. Comparing results from the short-term data indicated that although trends in the data with respect to aging time and aging temperature are similar, differences exist due to load direction and mode. The analytical model used for predicting long-term behavior using short-term data as input worked equally as well for the tension or compression loaded cases. Comparison of the loading modes indicated that the predictive model provided more accurate long term predictions for the shear mode as compared to the transverse mode. Parametric studies showed the usefulness of the predictive model as a tool for investigating long-term performance and compliance acceleration due to temperature.

A Proposed Method to Predict Preterm Birth Using Clinical Data, Standard Maternal Serum Screening, and Cholesterol

PubMed Central

ALLEMAN, Brandon W.; SMITH, Amanda R.; BYERS, Heather M.; BEDELL, Bruce; RYCKMAN, Kelli K.; MURRAY, Jeffrey C.; BOROWSKI, Kristi S.

2013-01-01

Objective To create a predictive model for preterm birth (PTB) from available clinical data and serum analytes. Study Design Serum analytes, routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB. Results Serum screening markers remained significant predictors of PTB even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first trimester total cholesterol (TC), TC change between trimesters and second trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB. Conclusions Using clinical and serum screening data a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step towards identifying additional women who may benefit from new or currently available interventions. PMID:23500456

Analysis on Experimental Investigation and Mathematical Modeling of Incompressible Flow Through Ceramic Foam Filters

NASA Astrophysics Data System (ADS)

Akbarnejad, Shahin; Jonsson, Lage Tord Ingemar; Kennedy, Mark William; Aune, Ragnhild Elizabeth; Jönsson, Pӓr Göran

2016-08-01

This paper presents experimental results of pressure drop measurements on 30, 50, and 80 pores per inch (PPI) commercial alumina ceramic foam filters (CFF) and compares the obtained pressure drop profiles to numerically modeled values. In addition, it is aimed at investigating the adequacy of the mathematical correlations used in the analytical and the computational fluid dynamics (CFD) simulations. It is shown that the widely used correlations for predicting pressure drop in porous media continuously under-predict the experimentally obtained pressure drop profiles. For analytical predictions, the negative deviations from the experimentally obtained pressure drop using the unmodified Ergun and Dietrich equations could be as high as 95 and 74 pct, respectively. For the CFD predictions, the deviation to experimental results is in the range of 84.3 to 88.5 pct depending on filter PPI. Better results can be achieved by applying the Forchheimer second-order drag term instead of the Brinkman-Forchheimer drag term. Thus, the final deviation of the CFD model estimates lie in the range of 0.3 to 5.5 pct compared to the measured values.

Analytical model of flame spread in full-scale room/corner tests (ISO9705)

Treesearch

Mark Dietenberger; Ondrej Grexa

1999-01-01

A physical, yet analytical, model of fire growth has predicted flame spread and rate of heat release (RHR) for an ISO9705 test scenario using bench-scale data from the cone calorimeter. The test scenario simulated was the propane ignition burner at the comer with a 100/300 kW program and the specimen lined on the walls only. Four phases of fire growth were simulated....

Amplitudes of doping striations: comparison of numerical calculations and analytical approaches

NASA Astrophysics Data System (ADS)

Jung, T.; Müller, G.

1997-02-01

Transient, axisymmetric numerical calculations of the heat and species transport including convection were performed for a simplified vertical gradient freeze (Bridgman) process with bottom seeding for GaAs. Periodical oscillations were superimposed onto the transient heater temperature profile. The amplitudes of the resulting oscillations of the growth rate and the dopant concentration (striations) in the growing crystals are compared with the predictions of analytical models.

Coronal heating by the resonant absorption of Alfven waves - Importance of the global mode and scaling laws

NASA Technical Reports Server (NTRS)

Steinolfson, Richard S.; Davila, Joseph M.

1993-01-01

Numerical simulations of the MHD equations for a fully compressible, low-beta, resistive plasma are used to study the resonance absorption process for the heating of coronal active region loops. Comparisons with more approximate analytic models show that the major predictions of the analytic theories are, to a large extent, confirmed by the numerical computations. The simulations demonstrate that the dissipation occurs primarily in a thin resonance layer. Some of the analytically predicted features verified by the simulations are (a) the position of the resonance layer within the initial inhomogeneity; (b) the importance of the global mode for a large range of loop densities; (c) the dependence of the resonance layer thickness and the steady-state heating rate on the dissipation coefficient; and (d) the time required for the resonance layer to form. In contrast with some previous analytic and simulation results, the time for the loop to reach a steady state is found to be the phase-mixing time rather than a dissipation time. This disagreement is shown to result from neglect of the existence of the global mode in some of the earlier analyses. The resonant absorption process is also shown to behave similar to a classical driven harmonic oscillator.

Evaluation of strength and failure of brittle rock containing initial cracks under lithospheric conditions

NASA Astrophysics Data System (ADS)

Li, Xiaozhao; Qi, Chengzhi; Shao, Zhushan; Ma, Chao

2018-02-01

Natural brittle rock contains numerous randomly distributed microcracks. Crack initiation, growth, and coalescence play a predominant role in evaluation for the strength and failure of brittle rocks. A new analytical method is proposed to predict the strength and failure of brittle rocks containing initial microcracks. The formulation of this method is based on an improved wing crack model and a suggested micro-macro relation. In this improved wing crack model, the parameter of crack angle is especially introduced as a variable, and the analytical stress-crack relation considering crack angle effect is obtained. Coupling the proposed stress-crack relation and the suggested micro-macro relation describing the relation between crack growth and axial strain, the stress-strain constitutive relation is obtained to predict the rock strength and failure. Considering different initial microcrack sizes, friction coefficients and confining pressures, effects of crack angle on tensile wedge force acting on initial crack interface are studied, and effects of crack angle on stress-strain constitutive relation of rocks are also analyzed. The strength and crack initiation stress under different crack angles are discussed, and the value of most disadvantaged angle triggering crack initiation and rock failure is founded. The analytical results are similar to the published study results. Rationality of this proposed analytical method is verified.

Correlation of finite element free vibration predictions using random vibration test data. M.S. Thesis - Cleveland State Univ.

NASA Technical Reports Server (NTRS)

Chambers, Jeffrey A.

1994-01-01

Finite element analysis is regularly used during the engineering cycle of mechanical systems to predict the response to static, thermal, and dynamic loads. The finite element model (FEM) used to represent the system is often correlated with physical test results to determine the validity of analytical results provided. Results from dynamic testing provide one means for performing this correlation. One of the most common methods of measuring accuracy is by classical modal testing, whereby vibratory mode shapes are compared to mode shapes provided by finite element analysis. The degree of correlation between the test and analytical mode shapes can be shown mathematically using the cross orthogonality check. A great deal of time and effort can be exhausted in generating the set of test acquired mode shapes needed for the cross orthogonality check. In most situations response data from vibration tests are digitally processed to generate the mode shapes from a combination of modal parameters, forcing functions, and recorded response data. An alternate method is proposed in which the same correlation of analytical and test acquired mode shapes can be achieved without conducting the modal survey. Instead a procedure is detailed in which a minimum of test information, specifically the acceleration response data from a random vibration test, is used to generate a set of equivalent local accelerations to be applied to the reduced analytical model at discrete points corresponding to the test measurement locations. The static solution of the analytical model then produces a set of deformations that once normalized can be used to represent the test acquired mode shapes in the cross orthogonality relation. The method proposed has been shown to provide accurate results for both a simple analytical model as well as a complex space flight structure.

Numerically calibrated model for propagation of a relativistic unmagnetized jet in dense media

NASA Astrophysics Data System (ADS)

Harrison, Richard; Gottlieb, Ore; Nakar, Ehud

2018-06-01

Relativistic jets reside in high-energy astrophysical systems of all scales. Their interaction with the surrounding media is critical as it determines the jet evolution, observable signature, and feedback on the environment. During its motion, the interaction of the jet with the ambient media inflates a highly pressurized cocoon, which under certain conditions collimates the jet and strongly affects its propagation. Recently, Bromberg et al. derived a general simplified (semi-)analytic solution for the evolution of the jet and the cocoon in case of an unmagnetized jet that propagates in a medium with a range of density profiles. In this work we use a large suite of 2D and 3D relativistic hydrodynamic simulations in order to test the validity and accuracy of this model. We discuss the similarities and differences between the analytic model and numerical simulations and also, to some extent, between 2D and 3D simulations. Our main finding is that although the analytic model is highly simplified, it properly predicts the evolution of the main ingredients of the jet-cocoon system, including its temporal evolution and the transition between various regimes (e.g. collimated to uncollimated). The analytic solution predicts a jet head velocity that is faster by a factor of about 3 compared to the simulations, as long as the head velocity is Newtonian. We use the results of the simulations to calibrate the analytic model which significantly increases its accuracy. We provide an applet that calculates semi-analytically the propagation of a jet in an arbitrary density profile defined by the user at http://www.astro.tau.ac.il/˜ore/propagation.html.

A methodology for the assessment of manned flight simulator fidelity

NASA Technical Reports Server (NTRS)

Hess, Ronald A.; Malsbury, Terry N.

1989-01-01

A relatively simple analytical methodology for assessing the fidelity of manned flight simulators for specific vehicles and tasks is offered. The methodology is based upon an application of a structural model of the human pilot, including motion cue effects. In particular, predicted pilot/vehicle dynamic characteristics are obtained with and without simulator limitations. A procedure for selecting model parameters can be implemented, given a probable pilot control strategy. In analyzing a pair of piloting tasks for which flight and simulation data are available, the methodology correctly predicted the existence of simulator fidelity problems. The methodology permitted the analytical evaluation of a change in simulator characteristics and indicated that a major source of the fidelity problems was a visual time delay in the simulation.

[Fever and petechial exanthema in children].

PubMed

Soult Rubio, J A; Navarro González, J; Olano Claret, P

1992-11-01

In an attempt to determine clinical and analytical predictive parameters of a possible grave disease, we have carried out a retrospective study of 172 children admitted to our hospital with fever and petechiae as initial symptoms. The ages ranged between 1 month and 10 years. Even though we have not found a clinical symptom or analysis sufficiently sensitive as to predict all grave diseases, the general clinical state of the child associated with either a high or low white cell count and an abnormal coagulation study should be alert signals for a serious infectious disease. On the contrary, if the clinical and analytical parameters are within normal limits the risk of a grave disease is low. We emphasize the high incidence of meningococcal disease (26%).

Turbine Vane External Heat Transfer. Volume 1: Analytical and Experimental Evaluation of Surface Heat Transfer Distributions with Leading Edge Showerhead Film Cooling

NASA Technical Reports Server (NTRS)

Turner, E. R.; Wilson, M. D.; Hylton, L. D.; Kaufman, R. M.

1985-01-01

Progress in predictive design capabilities for external heat transfer to turbine vanes was summarized. A two dimensional linear cascade (previously used to obtain vane surface heat transfer distributions on nonfilm cooled airfoils) was used to examine the effect of leading edge shower head film cooling on downstream heat transfer. The data were used to develop and evaluate analytical models. Modifications to the two dimensional boundary layer model are described. The results were used to formulate and test an effective viscosity model capable of predicting heat transfer phenomena downstream of the leading edge film cooling array on both the suction and pressure surfaces, with and without mass injection.

Trends & Controversies: Sociocultural Predictive Analytics and Terrorism Deterrence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; McGrath, Liam R.

2011-08-12

The use of predictive analytics to model terrorist rhetoric is highly instrumental in developing a strategy to deter terrorism. Traditional (e.g. Cold-War) deterrence methods are ineffective with terrorist groups such as al Qaida. Terrorists typically regard the prospect of death or loss of property as acceptable consequences of their struggle. Deterrence by threat of punishment is therefore fruitless. On the other hand, isolating terrorists from the community that may sympathize with their cause can have a decisive deterring outcome. Without the moral backing of a supportive audience, terrorism cannot be successfully framed as a justifiable political strategy and recruiting ismore » curtailed. Ultimately, terrorism deterrence is more effectively enforced by exerting influence to neutralize the communicative reach of terrorists.« less

An Economical Semi-Analytical Orbit Theory for Retarded Satellite Motion About an Oblate Planet

NASA Technical Reports Server (NTRS)

Gordon, R. A.

1980-01-01

Brouwer and Brouwer-Lyddanes' use of the Von Zeipel-Delaunay method is employed to develop an efficient analytical orbit theory suitable for microcomputers. A succinctly simple pseudo-phenomenologically conceptualized algorithm is introduced which accurately and economically synthesizes modeling of drag effects. The method epitomizes and manifests effortless efficient computer mechanization. Simulated trajectory data is employed to illustrate the theory's ability to accurately accommodate oblateness and drag effects for microcomputer ground based or onboard predicted orbital representation. Real tracking data is used to demonstrate that the theory's orbit determination and orbit prediction capabilities are favorably adaptable to and are comparable with results obtained utilizing complex definitive Cowell method solutions on satellites experiencing significant drag effects.

Modeling walker synchronization on the Millennium Bridge.

PubMed

Eckhardt, Bruno; Ott, Edward; Strogatz, Steven H; Abrams, Daniel M; McRobie, Allan

2007-02-01

On its opening day the London Millennium footbridge experienced unexpected large amplitude wobbling subsequent to the migration of pedestrians onto the bridge. Modeling the stepping of the pedestrians on the bridge as phase oscillators, we obtain a model for the combined dynamics of people and the bridge that is analytically tractable. It provides predictions for the phase dynamics of individual walkers and for the critical number of people for the onset of oscillations. Numerical simulations and analytical estimates reproduce the linear relation between pedestrian force and bridge velocity as observed in experiments. They allow prediction of the amplitude of bridge motion, the rate of relaxation to the synchronized state and the magnitude of the fluctuations due to a finite number of people.

Correlation of analytical and experimental hot structure vibration results

NASA Technical Reports Server (NTRS)

Kehoe, Michael W.; Deaton, Vivian C.

1993-01-01

High surface temperatures and temperature gradients can affect the vibratory characteristics and stability of aircraft structures. Aircraft designers are relying more on finite-element model analysis methods to ensure sufficient vehicle structural dynamic stability throughout the desired flight envelope. Analysis codes that predict these thermal effects must be correlated and verified with experimental data. Experimental modal data for aluminum, titanium, and fiberglass plates heated at uniform, nonuniform, and transient heating conditions are presented. The data show the effect of heat on each plate's modal characteristics, a comparison of predicted and measured plate vibration frequencies, the measured modal damping, and the effect of modeling material property changes and thermal stresses on the accuracy of the analytical results at nonuniform and transient heating conditions.

An improved method for predicting the lightning performance of high and extra-high-voltage substation shielding

NASA Astrophysics Data System (ADS)

Vinh, T.

1980-08-01

There is a need for better and more effective lightning protection for transmission and switching substations. In the past, a number of empirical methods were utilized to design systems to protect substations and transmission lines from direct lightning strokes. The need exists for convenient analytical lightning models adequate for engineering usage. In this study, analytical lightning models were developed along with a method for improved analysis of the physical properties of lightning through their use. This method of analysis is based upon the most recent statistical field data. The result is an improved method for predicting the occurrence of sheilding failure and for designing more effective protection for high and extra high voltage substations from direct strokes.

Acoustic solitons in waveguides with Helmholtz resonators: transmission line approach.

PubMed

Achilleos, V; Richoux, O; Theocharis, G; Frantzeskakis, D J

2015-02-01

We report experimental results and study theoretically soliton formation and propagation in an air-filled acoustic waveguide side loaded with Helmholtz resonators. We propose a theoretical modeling of the system, which relies on a transmission-line approach, leading to a nonlinear dynamical lattice model. The latter allows for an analytical description of the various soliton solutions for the pressure, which are found by means of dynamical systems and multiscale expansion techniques. These solutions include Boussinesq-like and Korteweg-de Vries pulse-shaped solitons that are observed in the experiment, as well as nonlinear Schrödinger envelope solitons, that are predicted theoretically. The analytical predictions are in excellent agreement with direct numerical simulations and in qualitative agreement with the experimental observations.

Comparison of Analysis with Test for Static Loading of Two Hypersonic Inflatable Aerodynamic Decelerator Concepts

NASA Technical Reports Server (NTRS)

Lyle, Karen H.

2015-01-01

Acceptance of new spacecraft structural architectures and concepts requires validated design methods to minimize the expense involved with technology demonstration via flight-testing. Hypersonic Inflatable Aerodynamic Decelerator (HIAD) architectures are attractive for spacecraft deceleration because they are lightweight, store compactly, and utilize the atmosphere to decelerate a spacecraft during entry. However, designers are hesitant to include these inflatable approaches for large payloads or spacecraft because of the lack of flight validation. This publication summarizes results comparing analytical results with test data for two concepts subjected to representative entry, static loading. The level of agreement and ability to predict the load distribution is considered sufficient to enable analytical predictions to be used in the design process.

The liquid fuel jet in subsonic crossflow

NASA Technical Reports Server (NTRS)

Nguyen, T. T.; Karagozian, A. R.

1990-01-01

An analytical/numerical model is described which predicts the behavior of nonreacting and reacting liquid jets injected transversely into subsonic cross flow. The compressible flowfield about the elliptical jet cross section is solved at various locations along the jet trajectory by analytical means for free-stream local Mach number perpendicular to jet cross section smaller than 0.3 and by numerical means for free-stream local Mach number perpendicular to jet cross section in the range 0.3-1.0. External and internal boundary layers along the jet cross section are solved by integral and numerical methods, and the mass losses due to boundary layer shedding, evaporation, and combustion are calculated and incorporated into the trajectory calculation. Comparison of predicted trajectories is made with limited experimental observations.

PARAMO: A Parallel Predictive Modeling Platform for Healthcare Analytic Research using Electronic Health Records

PubMed Central

Ng, Kenney; Ghoting, Amol; Steinhubl, Steven R.; Stewart, Walter F.; Malin, Bradley; Sun, Jimeng

2014-01-01

Objective Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: 1) cohort construction, 2) feature construction, 3) cross-validation, 4) feature selection, and 5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. Methods To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which 1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, 2) schedules the tasks in a topological ordering of the graph, and 3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. Results We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3 hours in parallel compared to 9 days if running sequentially. Conclusion This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers. PMID:24370496