Trial Sequential Methods for Meta-Analysis

ERIC Educational Resources Information Center

Kulinskaya, Elena; Wood, John

2014-01-01

Statistical methods for sequential meta-analysis have applications also for the design of new trials. Existing methods are based on group sequential methods developed for single trials and start with the calculation of a required information size. This works satisfactorily within the framework of fixed effects meta-analysis, but conceptual…

Bayesian analysis of rare events

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

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Comparison of methods for the analysis of relatively simple mediation models.

PubMed

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

Marginal fit and photoelastic stress analysis of CAD-CAM and overcast 3-unit implant-supported frameworks.

PubMed

Presotto, Anna Gabriella Camacho; Bhering, Cláudia Lopes Brilhante; Mesquita, Marcelo Ferraz; Barão, Valentim Adelino Ricardo

2017-03-01

Several studies have shown the superiority of computer-assisted design and computer-assisted manufacturing (CAD-CAM) technology compared with conventional casting. However, an advanced technology exists for casting procedures (the overcasting technique), which may serve as an acceptable and affordable alternative to CAD-CAM technology for fabricating 3-unit implant-supported fixed dental prostheses (FDPs). The purpose of this in vitro study was to evaluate, using quantitative photoelastic analysis, the effect of the prosthetic framework fabrication method (CAD-CAM and overcasting) on the marginal fit and stress transmitted to implants. The correlation between marginal fit and stress was also investigated. Three-unit implant-supported FDP frameworks were made using the CAD-CAM (n=10) and overcasting (n=10) methods. The frameworks were waxed to simulate a mandibular first premolar (PM region) to first molar (M region) FDP using overcast mini-abutment cylinders. The wax patterns were overcast (overcast experimental group) or scanned to obtain the frameworks (CAD-CAM control group). All frameworks were fabricated from cobalt-chromium (CoCr) alloy. The marginal fit was analyzed according to the single-screw test protocol, obtaining an average value for each region (M and PM) and each framework. The frameworks were tightened for the photoelastic model with standardized 10-Ncm torque. Stress was measured by quantitative photoelastic analysis. The results were submitted to the Student t test, 2-way ANOVA, and Pearson correlation test (α=.05). The framework fabrication method (FM) and evaluation site (ES; M and PM regions) did not affect the marginal fit values (P=.559 for FM and P=.065 for ES) and stress (P=.685 for FM and P=.468 for ES) in the implant-supported system. Positive correlations between marginal fit and stress were observed (CAD-CAM: r=0.922; P<.001; overcast: r=0.908; P<.001). CAD-CAM and overcasting methods present similar marginal fit and stress values for 3-unit FDP frameworks. The decreased marginal fit of frameworks induces greater stress in the implant-supported system. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

A novel bi-level meta-analysis approach: applied to biological pathway analysis.

PubMed

Nguyen, Tin; Tagett, Rebecca; Donato, Michele; Mitrea, Cristina; Draghici, Sorin

2016-02-01

The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. The R scripts are available on demand from the authors. sorin@wayne.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Systematic Approach for Quantitative Analysis of Multidisciplinary Design Optimization Framework

NASA Astrophysics Data System (ADS)

Kim, Sangho; Park, Jungkeun; Lee, Jeong-Oog; Lee, Jae-Woo

An efficient Multidisciplinary Design and Optimization (MDO) framework for an aerospace engineering system should use and integrate distributed resources such as various analysis codes, optimization codes, Computer Aided Design (CAD) tools, Data Base Management Systems (DBMS), etc. in a heterogeneous environment, and need to provide user-friendly graphical user interfaces. In this paper, we propose a systematic approach for determining a reference MDO framework and for evaluating MDO frameworks. The proposed approach incorporates two well-known methods, Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD), in order to provide a quantitative analysis of the qualitative criteria of MDO frameworks. Identification and hierarchy of the framework requirements and the corresponding solutions for the reference MDO frameworks, the general one and the aircraft oriented one were carefully investigated. The reference frameworks were also quantitatively identified using AHP and QFD. An assessment of three in-house frameworks was then performed. The results produced clear and useful guidelines for improvement of the in-house MDO frameworks and showed the feasibility of the proposed approach for evaluating an MDO framework without a human interference.

Parallel scalability and efficiency of vortex particle method for aeroelasticity analysis of bluff bodies

NASA Astrophysics Data System (ADS)

Tolba, Khaled Ibrahim; Morgenthal, Guido

2018-01-01

This paper presents an analysis of the scalability and efficiency of a simulation framework based on the vortex particle method. The code is applied for the numerical aerodynamic analysis of line-like structures. The numerical code runs on multicore CPU and GPU architectures using OpenCL framework. The focus of this paper is the analysis of the parallel efficiency and scalability of the method being applied to an engineering test case, specifically the aeroelastic response of a long-span bridge girder at the construction stage. The target is to assess the optimal configuration and the required computer architecture, such that it becomes feasible to efficiently utilise the method within the computational resources available for a regular engineering office. The simulations and the scalability analysis are performed on a regular gaming type computer.

Knowledge Discovery from Vibration Measurements

PubMed Central

Li, Jian; Wang, Daoyao

2014-01-01

The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is composed by the second order structural parameter identification, statistical control chart analysis, and system reliability analysis is then presented. To examine the performance of this SHM method, real sensor data measured from a lab size steel bridge model structure are used. The developed four-step framework of SHM has the potential to clarify the process of SHM to facilitate the further development of SHM techniques. PMID:24574933

Regularized Generalized Canonical Correlation Analysis

ERIC Educational Resources Information Center

Tenenhaus, Arthur; Tenenhaus, Michel

2011-01-01

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and…

A framework for characterizing eHealth literacy demands and barriers.

PubMed

Chan, Connie V; Kaufman, David R

2011-11-17

Consumer eHealth interventions are of a growing importance in the individual management of health and health behaviors. However, a range of access, resources, and skills barriers prevent health care consumers from fully engaging in and benefiting from the spectrum of eHealth interventions. Consumers may engage in a range of eHealth tasks, such as participating in health discussion forums and entering information into a personal health record. eHealth literacy names a set of skills and knowledge that are essential for productive interactions with technology-based health tools, such as proficiency in information retrieval strategies, and communicating health concepts effectively. We propose a theoretical and methodological framework for characterizing complexity of eHealth tasks, which can be used to diagnose and describe literacy barriers and inform the development of solution strategies. We adapted and integrated two existing theoretical models relevant to the analysis of eHealth literacy into a single framework to systematically categorize and describe task demands and user performance on tasks needed by health care consumers in the information age. The method derived from the framework is applied to (1) code task demands using a cognitive task analysis, and (2) code user performance on tasks. The framework and method are applied to the analysis of a Web-based consumer eHealth task with information-seeking and decision-making demands. We present the results from the in-depth analysis of the task performance of a single user as well as of 20 users on the same task to illustrate both the detailed analysis and the aggregate measures obtained and potential analyses that can be performed using this method. The analysis shows that the framework can be used to classify task demands as well as the barriers encountered in user performance of the tasks. Our approach can be used to (1) characterize the challenges confronted by participants in performing the tasks, (2) determine the extent to which application of the framework to the cognitive task analysis can predict and explain the problems encountered by participants, and (3) inform revisions to the framework to increase accuracy of predictions. The results of this illustrative application suggest that the framework is useful for characterizing task complexity and for diagnosing and explaining barriers encountered in task completion. The framework and analytic approach can be a potentially powerful generative research platform to inform development of rigorous eHealth examination and design instruments, such as to assess eHealth competence, to design and evaluate consumer eHealth tools, and to develop an eHealth curriculum.

A multi-fidelity framework for physics based rotor blade simulation and optimization

NASA Astrophysics Data System (ADS)

Collins, Kyle Brian

New helicopter rotor designs are desired that offer increased efficiency, reduced vibration, and reduced noise. Rotor Designers in industry need methods that allow them to use the most accurate simulation tools available to search for these optimal designs. Computer based rotor analysis and optimization have been advanced by the development of industry standard codes known as "comprehensive" rotorcraft analysis tools. These tools typically use table look-up aerodynamics, simplified inflow models and perform aeroelastic analysis using Computational Structural Dynamics (CSD). Due to the simplified aerodynamics, most design studies are performed varying structural related design variables like sectional mass and stiffness. The optimization of shape related variables in forward flight using these tools is complicated and results are viewed with skepticism because rotor blade loads are not accurately predicted. The most accurate methods of rotor simulation utilize Computational Fluid Dynamics (CFD) but have historically been considered too computationally intensive to be used in computer based optimization, where numerous simulations are required. An approach is needed where high fidelity CFD rotor analysis can be utilized in a shape variable optimization problem with multiple objectives. Any approach should be capable of working in forward flight in addition to hover. An alternative is proposed and founded on the idea that efficient hybrid CFD methods of rotor analysis are ready to be used in preliminary design. In addition, the proposed approach recognizes the usefulness of lower fidelity physics based analysis and surrogate modeling. Together, they are used with high fidelity analysis in an intelligent process of surrogate model building of parameters in the high fidelity domain. Closing the loop between high and low fidelity analysis is a key aspect of the proposed approach. This is done by using information from higher fidelity analysis to improve predictions made with lower fidelity models. This thesis documents the development of automated low and high fidelity physics based rotor simulation frameworks. The low fidelity framework uses a comprehensive code with simplified aerodynamics. The high fidelity model uses a parallel processor capable CFD/CSD methodology. Both low and high fidelity frameworks include an aeroacoustic simulation for prediction of noise. A synergistic process is developed that uses both the low and high fidelity frameworks together to build approximate models of important high fidelity metrics as functions of certain design variables. To test the process, a 4-bladed hingeless rotor model is used as a baseline. The design variables investigated include tip geometry and spanwise twist distribution. Approximation models are built for metrics related to rotor efficiency and vibration using the results from 60+ high fidelity (CFD/CSD) experiments and 400+ low fidelity experiments. Optimization using the approximation models found the Pareto Frontier anchor points, or the design having maximum rotor efficiency and the design having minimum vibration. Various Pareto generation methods are used to find designs on the frontier between these two anchor designs. When tested in the high fidelity framework, the Pareto anchor designs are shown to be very good designs when compared with other designs from the high fidelity database. This provides evidence that the process proposed has merit. Ultimately, this process can be utilized by industry rotor designers with their existing tools to bring high fidelity analysis into the preliminary design stage of rotors. In conclusion, the methods developed and documented in this thesis have made several novel contributions. First, an automated high fidelity CFD based forward flight simulation framework has been built for use in preliminary design optimization. The framework was built around an integrated, parallel processor capable CFD/CSD/AA process. Second, a novel method of building approximate models of high fidelity parameters has been developed. The method uses a combination of low and high fidelity results and combines Design of Experiments, statistical effects analysis, and aspects of approximation model management. And third, the determination of rotor blade shape variables through optimization using CFD based analysis in forward flight has been performed. This was done using the high fidelity CFD/CSD/AA framework and method mentioned above. While the low and high fidelity predictions methods used in the work still have inaccuracies that can affect the absolute levels of the results, a framework has been successfully developed and demonstrated that allows for an efficient process to improve rotor blade designs in terms of a selected choice of objective function(s). Using engineering judgment, this methodology could be applied today to investigate opportunities to improve existing designs. With improvements in the low and high fidelity prediction components that will certainly occur, this framework could become a powerful tool for future rotorcraft design work. (Abstract shortened by UMI.)

Assessment of Intralaminar Progressive Damage and Failure Analysis Using an Efficient Evaluation Framework

NASA Technical Reports Server (NTRS)

Hyder, Imran; Schaefer, Joseph; Justusson, Brian; Wanthal, Steve; Leone, Frank; Rose, Cheryl

2017-01-01

Reducing the timeline for development and certification for composite structures has been a long standing objective of the aerospace industry. This timeline can be further exacerbated when attempting to integrate new fiber-reinforced composite materials due to the large number of testing required at every level of design. computational progressive damage and failure analysis (PDFA) attempts to mitigate this effect; however, new PDFA methods have been slow to be adopted in industry since material model evaluation techniques have not been fully defined. This study presents an efficient evaluation framework which uses a piecewise verification and validation (V&V) approach for PDFA methods. Specifically, the framework is applied to evaluate PDFA research codes within the context of intralaminar damage. Methods are incrementally taken through various V&V exercises specifically tailored to study PDFA intralaminar damage modeling capability. Finally, methods are evaluated against a defined set of success criteria to highlight successes and limitations.

Generalized Full-Information Item Bifactor Analysis

ERIC Educational Resources Information Center

Cai, Li; Yang, Ji Seung; Hansen, Mark

2011-01-01

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of…

A high-level 3D visualization API for Java and ImageJ.

PubMed

Schmid, Benjamin; Schindelin, Johannes; Cardona, Albert; Longair, Mark; Heisenberg, Martin

2010-05-21

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.

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

Yuan, Chris, E-mail: cyuan@uwm.edu; Wang, Endong; Zhai, Qiang

Temporal homogeneity of inventory data is one of the major problems in life cycle assessment (LCA). Addressing temporal homogeneity of life cycle inventory data is important in reducing the uncertainties and improving the reliability of LCA results. This paper attempts to present a critical review and discussion on the fundamental issues of temporal homogeneity in conventional LCA and propose a theoretical framework for temporal discounting in LCA. Theoretical perspectives for temporal discounting in life cycle inventory analysis are discussed first based on the key elements of a scientific mechanism for temporal discounting. Then generic procedures for performing temporal discounting inmore » LCA is derived and proposed based on the nature of the LCA method and the identified key elements of a scientific temporal discounting method. A five-step framework is proposed and reported in details based on the technical methods and procedures needed to perform a temporal discounting in life cycle inventory analysis. Challenges and possible solutions are also identified and discussed for the technical procedure and scientific accomplishment of each step within the framework. - Highlights: • A critical review for temporal homogeneity problem of life cycle inventory data • A theoretical framework for performing temporal discounting on inventory data • Methods provided to accomplish each step of the temporal discounting framework.« less

Applying the ICF framework to study changes in quality-of-life for youth with chronic conditions

PubMed Central

McDougall, Janette; Wright, Virginia; Schmidt, Jonathan; Miller, Linda; Lowry, Karen

2011-01-01

Objective The objective of this paper is to describe how the ICF framework was applied as the foundation for a longitudinal study of changes in quality-of-life (QoL) for youth with chronic conditions. Method This article will describe the study’s aims, methods, measures and data analysis techniques. It will point out how the ICF framework was used—and expanded upon—to provide a model for studying the impact of factors on changes in QoL for youth with chronic conditions. Further, it will describe the instruments that were chosen to measure the components of the ICF framework and the data analysis techniques that will be used to examine the impact of factors on changes in youths’ QoL. Conclusions Qualitative and longitudinal designs for studying QoL based on the ICF framework can be useful for unraveling the complex ongoing inter-relationships among functioning, contextual factors and individuals’ perceptions of their QoL. PMID:21034288

Big Data Analysis Framework for Healthcare and Social Sectors in Korea

PubMed Central

Song, Tae-Min

2015-01-01

Objectives We reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea. Methods We reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0). We formulated a conceptual framework of big data in the healthcare and social service sectors at the national level. As a specific case, we designed a process and method of social big data analysis on suicide buzz. Results Developed countries (e.g., the United States, the UK, Singapore, Australia, and even OECD and EU) are emphasizing the potential of big data, and using it as a tool to solve their long-standing problems. Big data strategies for the healthcare and social service sectors were formulated based on an ICT-based policy of current government and the strategic goals of the Ministry of Health and Welfare. We suggest a framework of big data analysis in the healthcare and welfare service sectors separately and assigned them tentative names: 'health risk analysis center' and 'integrated social welfare service network'. A framework of social big data analysis is presented by applying it to the prevention and proactive detection of suicide in Korea. Conclusions There are some concerns with the utilization of big data in the healthcare and social welfare sectors. Thus, research on these issues must be conducted so that sophisticated and practical solutions can be reached. PMID:25705552

Fit Analysis of Different Framework Fabrication Techniques for Implant-Supported Partial Prostheses.

PubMed

Spazzin, Aloísio Oro; Bacchi, Atais; Trevisani, Alexandre; Farina, Ana Paula; Dos Santos, Mateus Bertolini

2016-01-01

This study evaluated the vertical misfit of implant-supported frameworks made using different techniques to obtain passive fit. Thirty three-unit fixed partial dentures were fabricated in cobalt-chromium alloy (n = 10) using three fabrication methods: one-piece casting, framework cemented on prepared abutments, and laser welding. The vertical misfit between the frameworks and the abutments was evaluated with an optical microscope using the single-screw test. Data were analyzed using one-way analysis of variance and Tukey test (α = .05). The one-piece casted frameworks presented significantly higher vertical misfit values than those found for framework cemented on prepared abutments and laser welding techniques (P < .001 and P < .003, respectively). Laser welding and framework cemented on prepared abutments are effective techniques to improve the adaptation of three-unit implant-supported prostheses. These techniques presented similar fit.

Development of an Analysis and Design Optimization Framework for Marine Propellers

NASA Astrophysics Data System (ADS)

Tamhane, Ashish C.

In this thesis, a framework for the analysis and design optimization of ship propellers is developed. This framework can be utilized as an efficient synthesis tool in order to determine the main geometric characteristics of the propeller but also to provide the designer with the capability to optimize the shape of the blade sections based on their specific criteria. A hybrid lifting-line method with lifting-surface corrections to account for the three-dimensional flow effects has been developed. The prediction of the correction factors is achieved using Artificial Neural Networks and Support Vector Regression. This approach results in increased approximation accuracy compared to existing methods and allows for extrapolation of the correction factor values. The effect of viscosity is implemented in the framework via the coupling of the lifting line method with the open-source RANSE solver OpenFOAM for the calculation of lift, drag and pressure distribution on the blade sections using a transition kappa-o SST turbulence model. Case studies of benchmark high-speed propulsors are utilized in order to validate the proposed framework for propeller operation in open-water conditions but also in a ship's wake.

A Mathematical Framework for Image Analysis

DTIC Science & Technology

1991-08-01

The results reported here were derived from the research project ’A Mathematical Framework for Image Analysis ’ supported by the Office of Naval...Research, contract N00014-88-K-0289 to Brown University. A common theme for the work reported is the use of probabilistic methods for problems in image ... analysis and image reconstruction. Five areas of research are described: rigid body recognition using a decision tree/combinatorial approach; nonrigid

Computer-aided pulmonary image analysis in small animal models

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

Xu, Ziyue; Mansoor, Awais; Mollura, Daniel J.

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors’ system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next.more » The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. Results: 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT’09 data set. Conclusions: The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.« less

The Effect of Framework Design on Stress Distribution in Implant-Supported FPDs: A 3-D FEM Study

PubMed Central

Eraslan, Oguz; Inan, Ozgur; Secilmis, Asli

2010-01-01

Objectives: The biomechanical behavior of the superstructure plays an important role in the functional longevity of dental implants. However, information about the influence of framework design on stresses transmitted to the implants and supporting tissues is limited. The purpose of this study was to evaluate the effects of framework designs on stress distribution at the supporting bone and supporting implants. Methods: In this study, the three-dimensional (3D) finite element stress analysis method was used. Three types of 3D mathematical models simulating three different framework designs for implant-supported 3-unit posterior fixed partial dentures were prepared with supporting structures. Convex (1), concave (2), and conventional (3) pontic framework designs were simulated. A 300-N static vertical occlusal load was applied on the node at the center of occlusal surface of the pontic to calculate the stress distributions. As a second condition, frameworks were directly loaded to evaluate the effect of the framework design clearly. The Solidworks/Cosmosworks structural analysis programs were used for finite element modeling/analysis. Results: The analysis of the von Mises stress values revealed that maximum stress concentrations were located at the loading areas for all models. The pontic side marginal edges of restorations and the necks of implants were other stress concentration regions. There was no clear difference among models when the restorations were loaded at occlusal surfaces. When the veneering porcelain was removed, and load was applied directly to the framework, there was a clear increase in stress concentration with a concave design on supporting implants and bone structure. Conclusions: The present study showed that the use of a concave design in the pontic frameworks of fixed partial dentures increases the von Mises stress levels on implant abutments and supporting bone structure. However, the veneering porcelain element reduces the effect of the framework and compensates for design weaknesses. PMID:20922156

Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

NASA Astrophysics Data System (ADS)

Jiao, Xiangqing; Liao, Yuan; Nguyen, Thai

2017-11-01

Accurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model's effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model's accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.

A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2012-01-01

A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.

A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2011-01-01

A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.

Uncertainty Analysis of Consequence Management (CM) Data Products.

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

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

Investigating the Potential of Computer Environments for the Teaching and Learning of Functions: A Double Analysis from Two Research Traditions

ERIC Educational Resources Information Center

Lagrange, Jean-Baptiste; Psycharis, Giorgos

2014-01-01

The general goal of this paper is to explore the potential of computer environments for the teaching and learning of functions. To address this, different theoretical frameworks and corresponding research traditions are available. In this study, we aim to network different frameworks by following a "double analysis" method to analyse two…

Second Harmonic Generation of Unpolarized Light

NASA Astrophysics Data System (ADS)

Ding, Changqin; Ulcickas, James R. W.; Deng, Fengyuan; Simpson, Garth J.

2017-11-01

A Mueller tensor mathematical framework was applied for predicting and interpreting the second harmonic generation (SHG) produced with an unpolarized fundamental beam. In deep tissue imaging through SHG and multiphoton fluorescence, partial or complete depolarization of the incident light complicates polarization analysis. The proposed framework has the distinct advantage of seamlessly merging the purely polarized theory based on the Jones or Cartesian susceptibility tensors with a more general Mueller tensor framework capable of handling partial depolarized fundamental and/or SHG produced. The predictions of the model are in excellent agreement with experimental measurements of z -cut quartz and mouse tail tendon obtained with polarized and depolarized incident light. The polarization-dependent SHG produced with unpolarized fundamental allowed determination of collagen fiber orientation in agreement with orthogonal methods based on image analysis. This method has the distinct advantage of being immune to birefringence or depolarization of the fundamental beam for structural analysis of tissues.

NoSQL Based 3D City Model Management System

NASA Astrophysics Data System (ADS)

Mao, B.; Harrie, L.; Cao, J.; Wu, Z.; Shen, J.

2014-04-01

To manage increasingly complicated 3D city models, a framework based on NoSQL database is proposed in this paper. The framework supports import and export of 3D city model according to international standards such as CityGML, KML/COLLADA and X3D. We also suggest and implement 3D model analysis and visualization in the framework. For city model analysis, 3D geometry data and semantic information (such as name, height, area, price and so on) are stored and processed separately. We use a Map-Reduce method to deal with the 3D geometry data since it is more complex, while the semantic analysis is mainly based on database query operation. For visualization, a multiple 3D city representation structure CityTree is implemented within the framework to support dynamic LODs based on user viewpoint. Also, the proposed framework is easily extensible and supports geoindexes to speed up the querying. Our experimental results show that the proposed 3D city management system can efficiently fulfil the analysis and visualization requirements.

Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis.

PubMed

Hoch, Jeffrey S; Briggs, Andrew H; Willan, Andrew R

2002-07-01

Economic evaluation is often seen as a branch of health economics divorced from mainstream econometric techniques. Instead, it is perceived as relying on statistical methods for clinical trials. Furthermore, the statistic of interest in cost-effectiveness analysis, the incremental cost-effectiveness ratio is not amenable to regression-based methods, hence the traditional reliance on comparing aggregate measures across the arms of a clinical trial. In this paper, we explore the potential for health economists undertaking cost-effectiveness analysis to exploit the plethora of established econometric techniques through the use of the net-benefit framework - a recently suggested reformulation of the cost-effectiveness problem that avoids the reliance on cost-effectiveness ratios and their associated statistical problems. This allows the formulation of the cost-effectiveness problem within a standard regression type framework. We provide an example with empirical data to illustrate how a regression type framework can enhance the net-benefit method. We go on to suggest that practical advantages of the net-benefit regression approach include being able to use established econometric techniques, adjust for imperfect randomisation, and identify important subgroups in order to estimate the marginal cost-effectiveness of an intervention. Copyright 2002 John Wiley & Sons, Ltd.

Boom Minimization Framework for Supersonic Aircraft Using CFD Analysis

NASA Technical Reports Server (NTRS)

Ordaz, Irian; Rallabhandi, Sriram K.

2010-01-01

A new framework is presented for shape optimization using analytical shape functions and high-fidelity computational fluid dynamics (CFD) via Cart3D. The focus of the paper is the system-level integration of several key enabling analysis tools and automation methods to perform shape optimization and reduce sonic boom footprint. A boom mitigation case study subject to performance, stability and geometrical requirements is presented to demonstrate a subset of the capabilities of the framework. Lastly, a design space exploration is carried out to assess the key parameters and constraints driving the design.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

PubMed

Regalia, Giulia; Coelli, Stefania; Biffi, Emilia; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2016-01-01

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

PubMed Central

Pedrocchi, Alessandra

2016-01-01

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis. PMID:27239191

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

Specification and Preliminary Validation of IAT (Integrated Analysis Techniques) Methods: Executive Summary.

DTIC Science & Technology

1985-03-01

conceptual framwork , and preliminary validation of IAT concepts. Planned work for FY85, including more extensive validation, is also described. 20...Developments: Required Capabilities .... ......... 10 2-1 IAT Conceptual Framework - FY85 (FEO) ..... ........... 11 2-2 Recursive Nature of Decomposition...approach: 1) Identify needs & requirements for IAT. 2) Develop IAT conceptual framework. 3) Validate IAT methods. 4) Develop applications materials. To

Linking stressors and ecological responses

USGS Publications Warehouse

Gentile, J.H.; Solomon, K.R.; Butcher, J.B.; Harrass, M.; Landis, W.G.; Power, M.; Rattner, B.A.; Warren-Hicks, W.J.; Wenger, R.; Foran, Jeffery A.; Ferenc, Susan A.

1999-01-01

To characterize risk, it is necessary to quantify the linkages and interactions between chemical, physical and biological stressors and endpoints in the conceptual framework for ecological risk assessment (ERA). This can present challenges in a multiple stressor analysis, and it will not always be possible to develop a quantitative stressor-response profile. This review commences with a conceptual representation of the problem of developing a linkage analysis for multiple stressors and responses. The remainder of the review surveys a variety of mathematical and statistical methods (e.g., ranking methods, matrix models, multivariate dose-response for mixtures, indices, visualization, simulation modeling and decision-oriented methods) for accomplishing the linkage analysis for multiple stressors. Describing the relationships between multiple stressors and ecological effects are critical components of 'effects assessment' in the ecological risk assessment framework.

Determination Of Slope Instability Using Spatially Integrated Mapping Framework

NASA Astrophysics Data System (ADS)

Baharuddin, I. N. Z.; Omar, R. C.; Roslan, R.; Khalid, N. H. N.; Hanifah, M. I. M.

2016-11-01

The determination and identification of slope instability are often rely on data obtained from in-situ soil investigation work where it involves the logistic of machineries and manpower, thus these aspects may increase the cost especially for remote locations. Therefore a method, which is able to identify possible slope instability without frequent ground walkabout survey, is needed. This paper presents the method used in prediction of slope instability using spatial integrated mapping framework which applicable for remote areas such as tropical forest and natural hilly terrain. Spatial data such as geology, topography, land use map, slope angle and elevation were used in regional analysis during desktop study. Through this framework, the occurrence of slope instability was able to be identified and was validate using a confirmatory site- specific analysis.

Beyond Effectiveness: A Pragmatic Evaluation Framework for Learning and Continuous Quality Improvement of e-Learning Interventions in Healthcare.

PubMed

Dafalla, Tarig Dafalla Mohamed; Kushniruk, Andre W; Borycki, Elizabeth M

2015-01-01

A pragmatic evaluation framework for evaluating the usability and usefulness of an e-learning intervention for a patient clinical information scheduling system is presented in this paper. The framework was conceptualized based on two different but related concepts (usability and usefulness) and selection of appropriate and valid methods of data collection and analysis that included: (1) Low-Cost Rapid Usability Engineering (LCRUE), (2) Cognitive Task Analysis (CTA), (3) Heuristic Evaluation (HE) criteria for web-based learning, and (4) Software Usability Measurement Inventory (SUMI). The results of the analysis showed some areas where usability that were related to General Interface Usability (GIU), instructional design and content was problematic; some of which might account for the poorly rated aspects of usability when subjectively measured. This paper shows that using a pragmatic framework can be a useful way, not only for measuring the usability and usefulness, but also for providing a practical objective evidences for learning and continuous quality improvement of e-learning systems. The findings should be of interest to educators, developers, designers, researchers, and usability practitioners involved in the development of e-learning systems in healthcare. This framework could be an appropriate method for assessing the usability, usefulness and safety of health information systems both in the laboratory and in the clinical context.

A method to assess the use of new and recycled materials in pavements.

DOT National Transportation Integrated Search

2015-04-01

This report includes the results of a research project aimed at developing a comprehensive analysis : framework for evaluating new and recycled materials to be used in pavements in Michigan. Two basic : components of the framework are: (i) Engineerin...

The Constant Comparative Analysis Method Outside of Grounded Theory

ERIC Educational Resources Information Center

Fram, Sheila M.

2013-01-01

This commentary addresses the gap in the literature regarding discussion of the legitimate use of Constant Comparative Analysis Method (CCA) outside of Grounded Theory. The purpose is to show the strength of using CCA to maintain the emic perspective and how theoretical frameworks can maintain the etic perspective throughout the analysis. My…

Robustness Analysis of Integrated LPV-FDI Filters and LTI-FTC System for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Khong, Thuan H.; Shin, Jong-Yeob

2007-01-01

This paper proposes an analysis framework for robustness analysis of a nonlinear dynamics system that can be represented by a polynomial linear parameter varying (PLPV) system with constant bounded uncertainty. The proposed analysis framework contains three key tools: 1) a function substitution method which can convert a nonlinear system in polynomial form into a PLPV system, 2) a matrix-based linear fractional transformation (LFT) modeling approach, which can convert a PLPV system into an LFT system with the delta block that includes key uncertainty and scheduling parameters, 3) micro-analysis, which is a well known robust analysis tool for linear systems. The proposed analysis framework is applied to evaluating the performance of the LPV-fault detection and isolation (FDI) filters of the closed-loop system of a transport aircraft in the presence of unmodeled actuator dynamics and sensor gain uncertainty. The robustness analysis results are compared with nonlinear time simulations.

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

NASA Technical Reports Server (NTRS)

Heath, Christopher M.; Gray, Justin S.

2012-01-01

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

NASA Astrophysics Data System (ADS)

Kristoufek, Ladislav

2015-02-01

We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential nonstationarity and power-law correlations. The former feature allows for distinguishing between effects for a pair of variables from different temporal perspectives. The latter ones make the method a significant improvement over the standard least squares estimation. Theoretical claims are supported by Monte Carlo simulations. The method is then applied on selected examples from physics, finance, environmental science, and epidemiology. For most of the studied cases, the relationship between variables of interest varies strongly across scales.

Climate Action Benefits: Methods of Analysis

EPA Pesticide Factsheets

This page provides detailed information on the methods used in the CIRA analyses, including the overall framework, temperature projections, precipitation projections, sea level rise projections, uncertainty, and limitations.

A weak Galerkin generalized multiscale finite element method

DOE PAGES

Mu, Lin; Wang, Junping; Ye, Xiu

2016-03-31

In this study, we propose a general framework for weak Galerkin generalized multiscale (WG-GMS) finite element method for the elliptic problems with rapidly oscillating or high contrast coefficients. This general WG-GMS method features in high order accuracy on general meshes and can work with multiscale basis derived by different numerical schemes. A special case is studied under this WG-GMS framework in which the multiscale basis functions are obtained by solving local problem with the weak Galerkin finite element method. Convergence analysis and numerical experiments are obtained for the special case.

A weak Galerkin generalized multiscale finite element method

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

Mu, Lin; Wang, Junping; Ye, Xiu

In this study, we propose a general framework for weak Galerkin generalized multiscale (WG-GMS) finite element method for the elliptic problems with rapidly oscillating or high contrast coefficients. This general WG-GMS method features in high order accuracy on general meshes and can work with multiscale basis derived by different numerical schemes. A special case is studied under this WG-GMS framework in which the multiscale basis functions are obtained by solving local problem with the weak Galerkin finite element method. Convergence analysis and numerical experiments are obtained for the special case.

Microgravity isolation system design: A modern control analysis framework

NASA Technical Reports Server (NTRS)

Hampton, R. D.; Knospe, C. R.; Allaire, P. E.; Grodsinsky, C. M.

1994-01-01

Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from the manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. These methods, although more powerful than their classical counterparts, can nonetheless go only so far in meeting the design requirements for practical systems. Once a tentative controller design is available, it must still be evaluated to determine whether or not it is fully acceptable, and to compare it with other possible design candidates. Realistically, such evaluation will be an inherent part of a necessary iterative design process. In this paper, an approach is presented for applying complex mu-analysis methods to a closed-loop vibration isolation system (experiment plus controller). An analysis framework is presented for evaluating nominal stability, nominal performance, robust stability, and robust performance of active microgravity isolation systems, with emphasis on the effective use of mu-analysis methods.

Mayday - integrative analytics for expression data

PubMed Central

2010-01-01

Background DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files. Results We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved. Conclusions We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org. PMID:20214778

Systematic text condensation: a strategy for qualitative analysis.

PubMed

Malterud, Kirsti

2012-12-01

To present background, principles, and procedures for a strategy for qualitative analysis called systematic text condensation and discuss this approach compared with related strategies. Giorgi's psychological phenomenological analysis is the point of departure and inspiration for systematic text condensation. The basic elements of Giorgi's method and the elaboration of these in systematic text condensation are presented, followed by a detailed description of procedures for analysis according to systematic text condensation. Finally, similarities and differences compared with other frequently applied methods for qualitative analysis are identified, as the foundation of a discussion of strengths and limitations of systematic text condensation. Systematic text condensation is a descriptive and explorative method for thematic cross-case analysis of different types of qualitative data, such as interview studies, observational studies, and analysis of written texts. The method represents a pragmatic approach, although inspired by phenomenological ideas, and various theoretical frameworks can be applied. The procedure consists of the following steps: 1) total impression - from chaos to themes; 2) identifying and sorting meaning units - from themes to codes; 3) condensation - from code to meaning; 4) synthesizing - from condensation to descriptions and concepts. Similarities and differences comparing systematic text condensation with other frequently applied qualitative methods regarding thematic analysis, theoretical methodological framework, analysis procedures, and taxonomy are discussed. Systematic text condensation is a strategy for analysis developed from traditions shared by most of the methods for analysis of qualitative data. The method offers the novice researcher a process of intersubjectivity, reflexivity, and feasibility, while maintaining a responsible level of methodological rigour.

Development and Preliminary Testing of a Framework to Evaluate Patients' Experiences of the Fundamentals of Care: A Secondary Analysis of Three Stroke Survivor Narratives

PubMed Central

Kitson, Alison L.; Muntlin Athlin, Åsa

2013-01-01

Aim. To develop and test a framework describing the interrelationship of three key dimensions (physical, psychosocial, and relational) in the provision of the fundamentals of care to patients. Background. There are few conceptual frameworks to help healthcare staff, particularly nurses, know how to provide direct care around fundamental needs such as eating, drinking, and going to the toilet. Design. Deductive development of a conceptual framework and qualitative analysis of secondary interview data. Method. Framework development followed by a secondary in-depth analysis of primary narrative interview data from three stroke survivors. Results. Using the physical, psychosocial and relational dimensions to develop a conceptual framework, it was possible to identify a number of “archetypes” or scenarios that could explain stroke survivors' positive experiences of their care. Factors contributing to suboptimal care were also identified. Conclusions. This way of thinking about how the fundamentals of care are experienced by patients may help to elucidate the complex processes involved around providing high quality fundamentals of care. This analysis illustrates the multiple dimensions at play. However, more systematic investigation is required with further refining and testing with wider healthcare user groups. The framework has potential to be used as a predictive, evaluative, and explanatory tool. PMID:23864946

Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

People don't talk in institutional statements: A methodological case study of the Institutional Analysis and Development Framework

Treesearch

Cristy Watkins; Lynne M. Westphal

2015-01-01

In this paper, we describe our application of Ostrom et al.'s ADICO syntax, a grammatical tool based in the Institutional Analysis and Development framework, to a study of ecological restoration decision making in the Chicago Wilderness region. As this method has only been used to look at written policy and/or extractive natural resource management systems, our...

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Test Generation Framework for Distributed Fault-Tolerant Algorithms

NASA Technical Reports Server (NTRS)

Goodloe, Alwyn; Bushnell, David; Miner, Paul; Pasareanu, Corina S.

2009-01-01

Heavyweight formal methods such as theorem proving have been successfully applied to the analysis of safety critical fault-tolerant systems. Typically, the models and proofs performed during such analysis do not inform the testing process of actual implementations. We propose a framework for generating test vectors from specifications written in the Prototype Verification System (PVS). The methodology uses a translator to produce a Java prototype from a PVS specification. Symbolic (Java) PathFinder is then employed to generate a collection of test cases. A small example is employed to illustrate how the framework can be used in practice.

A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction

PubMed Central

Jesunathadas, Mark; Poston, Brach; Santello, Marco; Ye, Jieping; Panchanathan, Sethuraman

2014-01-01

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases. PMID:22498666

LISA Framework for Enhancing Gravitational Wave Signal Extraction Techniques

NASA Technical Reports Server (NTRS)

Thompson, David E.; Thirumalainambi, Rajkumar

2006-01-01

This paper describes the development of a Framework for benchmarking and comparing signal-extraction and noise-interference-removal methods that are applicable to interferometric Gravitational Wave detector systems. The primary use is towards comparing signal and noise extraction techniques at LISA frequencies from multiple (possibly confused) ,gravitational wave sources. The Framework includes extensive hybrid learning/classification algorithms, as well as post-processing regularization methods, and is based on a unique plug-and-play (component) architecture. Published methods for signal extraction and interference removal at LISA Frequencies are being encoded, as well as multiple source noise models, so that the stiffness of GW Sensitivity Space can be explored under each combination of methods. Furthermore, synthetic datasets and source models can be created and imported into the Framework, and specific degraded numerical experiments can be run to test the flexibility of the analysis methods. The Framework also supports use of full current LISA Testbeds, Synthetic data systems, and Simulators already in existence through plug-ins and wrappers, thus preserving those legacy codes and systems in tact. Because of the component-based architecture, all selected procedures can be registered or de-registered at run-time, and are completely reusable, reconfigurable, and modular.

Integration of Human Reliability Analysis Models into the Simulation-Based Framework for the Risk-Informed Safety Margin Characterization Toolkit

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

Boring, Ronald; Mandelli, Diego; Rasmussen, Martin

2016-06-01

This report presents an application of a computation-based human reliability analysis (HRA) framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER). HUNTER has been developed not as a standalone HRA method but rather as framework that ties together different HRA methods to model dynamic risk of human activities as part of an overall probabilistic risk assessment (PRA). While we have adopted particular methods to build an initial model, the HUNTER framework is meant to be intrinsically flexible to new pieces that achieve particular modeling goals. In the present report, the HUNTER implementation has the following goals: •more » Integration with a high fidelity thermal-hydraulic model capable of modeling nuclear power plant behaviors and transients • Consideration of a PRA context • Incorporation of a solid psychological basis for operator performance • Demonstration of a functional dynamic model of a plant upset condition and appropriate operator response This report outlines these efforts and presents the case study of a station blackout scenario to demonstrate the various modules developed to date under the HUNTER research umbrella.« less

Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis.

PubMed

Edwards, Jeffrey R; Lambert, Lisa Schurer

2007-03-01

Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables. (c) 2007 APA, all rights reserved.

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

PubMed Central

Bu, Qirong; Liu, Feihong; Zhang, Min; Ren, Yu; Lv, Yi

2018-01-01

Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms. PMID:29854359

The MHOST finite element program: 3-D inelastic analysis methods for hot section components. Volume 1: Theoretical manual

NASA Technical Reports Server (NTRS)

Nakazawa, Shohei

1991-01-01

Formulations and algorithms implemented in the MHOST finite element program are discussed. The code uses a novel concept of the mixed iterative solution technique for the efficient 3-D computations of turbine engine hot section components. The general framework of variational formulation and solution algorithms are discussed which were derived from the mixed three field Hu-Washizu principle. This formulation enables the use of nodal interpolation for coordinates, displacements, strains, and stresses. Algorithmic description of the mixed iterative method includes variations for the quasi static, transient dynamic and buckling analyses. The global-local analysis procedure referred to as the subelement refinement is developed in the framework of the mixed iterative solution, of which the detail is presented. The numerically integrated isoparametric elements implemented in the framework is discussed. Methods to filter certain parts of strain and project the element discontinuous quantities to the nodes are developed for a family of linear elements. Integration algorithms are described for linear and nonlinear equations included in MHOST program.

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

NASA Astrophysics Data System (ADS)

Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

2017-06-01

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

A Method for a Retrospective Analysis of Course Objectives: Have Pursued Objectives in Fact Been Attained? Twente Educational Report Number 7.

ERIC Educational Resources Information Center

Plomp, Tjeerd; van der Meer, Adri

A method pertaining to the identification and analysis of course objectives is discussed. A framework is developed by which post facto objectives can be determined and students' attainment of the objectives can be assessed. The method can also be used for examining the quality of instruction. Using this method, it is possible to determine…

A streamlined Python framework for AT-TPC data analysis

NASA Astrophysics Data System (ADS)

Taylor, J. Z.; Bradt, J.; Bazin, D.; Kuchera, M. P.

2017-09-01

User-friendly data analysis software has been developed for the Active-Target Time Projection Chamber (AT-TPC) experiment at the National Superconducting Cyclotron Laboratory at Michigan State University. The AT-TPC, commissioned in 2014, is a gas-filled detector that acts as both the detector and target for high-efficiency detection of low-intensity, exotic nuclear reactions. The pytpc framework is a Python package for analyzing AT-TPC data. The package was developed for the analysis of 46Ar(p, p) data. The existing software was used to analyze data produced by the 40Ar(p, p) experiment that ran in August, 2015. Usage of the package was documented in an analysis manual both to improve analysis steps and aid in the work of future AT-TPC users. Software features and analysis methods in the pytpc framework will be presented along with the 40Ar results.

Rhetorical Structure of Education Research Article Methods Sections

ERIC Educational Resources Information Center

Zhang, Baoya; Wannaruk, Anchalee

2016-01-01

This study investigated the rhetorical move structure of the education research article genre within the framework of Swales' (1981, 1990, 2004) move analysis. A corpus of 120 systematically sampled empirical education research articles served as data input for the analysis. The results indicate that the education research article methods section…

Bridging informatics and implementation science: evaluating a framework to assess electronic health record implementations in community settings.

PubMed

Richardson, Joshua E; Abramson, Erika L; Pfoh, Elizabeth R; Kaushal, Rainu

2012-01-01

Effective electronic health record (EHR) implementations in community settings are critical to promoting safe and reliable EHR use as well as mitigating provider dissatisfaction that often results. The implementation challenge is compounded given the scale and scope of EHR installations that are occurring and will continue to occur over the next five years. However, when compared to EHR evaluations relatively few biomedical informatics researchers have published on evaluating EHR implementations. Fewer still have evaluated EHR implementations in community settings. We report on the methods we used to achieve a novel application of an implementation science framework in informatics to qualitatively evaluate community-based EHR implementations. We briefly provide an overview of the implementation science framework, our methods for adapting it to informatics, the effects the framework had on our qualitative methods of inquiry and analysis, and discuss its potential value for informatics research.

Collaborative Rhetorical Structure: A Discourse Analysis Method for Analyzing Student Collaborative Inquiry via Computer Conferencing

ERIC Educational Resources Information Center

Kou, Xiaojing

2011-01-01

Various formats of online discussion have proven valuable for enhancing learning and collaboration in distance and blended learning contexts. However, despite their capacity to reveal essential processes in collaborative inquiry, current mainstream analytical frameworks, such as the cognitive presence framework (Garrison, Anderson, & Archer,…

New developments in the evolution and application of the WHO/IPCS framework on mode of action/species concordance analysis.

PubMed

Meek, M E; Boobis, A; Cote, I; Dellarco, V; Fotakis, G; Munn, S; Seed, J; Vickers, C

2014-01-01

The World Health Organization/International Programme on Chemical Safety mode of action/human relevance framework has been updated to reflect the experience acquired in its application and extend its utility to emerging areas in toxicity testing and non-testing methods. The underlying principles have not changed, but the framework's scope has been extended to enable integration of information at different levels of biological organization and reflect evolving experience in a much broader range of potential applications. Mode of action/species concordance analysis can also inform hypothesis-based data generation and research priorities in support of risk assessment. The modified framework is incorporated within a roadmap, with feedback loops encouraging continuous refinement of fit-for-purpose testing strategies and risk assessment. Important in this construct is consideration of dose-response relationships and species concordance analysis in weight of evidence. The modified Bradford Hill considerations have been updated and additionally articulated to reflect increasing experience in application for cases where the toxicological outcome of chemical exposure is known. The modified framework can be used as originally intended, where the toxicological effects of chemical exposure are known, or in hypothesizing effects resulting from chemical exposure, using information on putative key events in established modes of action from appropriate in vitro or in silico systems and other lines of evidence. This modified mode of action framework and accompanying roadmap and case examples are expected to contribute to improving transparency in explicitly addressing weight of evidence considerations in mode of action/species concordance analysis based on both conventional data sources and evolving methods. Copyright © 2013 John Wiley & Sons, Ltd. The World Health Organization retains copyright and all other rights in the manuscript of this article as submitted for publication.

Transport induced by mean-eddy interaction: II. Analysis of transport processes

NASA Astrophysics Data System (ADS)

Ide, Kayo; Wiggins, Stephen

2015-03-01

We present a framework for the analysis of transport processes resulting from the mean-eddy interaction in a flow. The framework is based on the Transport Induced by the Mean-Eddy Interaction (TIME) method presented in a companion paper (Ide and Wiggins, 2014) [1]. The TIME method estimates the (Lagrangian) transport across stationary (Eulerian) boundaries defined by chosen streamlines of the mean flow. Our framework proceeds after first carrying out a sequence of preparatory steps that link the flow dynamics to the transport processes. This includes the construction of the so-called "instantaneous flux" as the Hovmöller diagram. Transport processes are studied by linking the signals of the instantaneous flux field to the dynamical variability of the flow. This linkage also reveals how the variability of the flow contributes to the transport. The spatio-temporal analysis of the flux diagram can be used to assess the efficiency of the variability in transport processes. We apply the method to the double-gyre ocean circulation model in the situation where the Rossby-wave mode dominates the dynamic variability. The spatio-temporal analysis shows that the inter-gyre transport is controlled by the circulating eddy vortices in the fast eastward jet region, whereas the basin-scale Rossby waves have very little impact.

OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework

PubMed Central

Garcia, Samuel; Fourcaud-Trocmé, Nicolas

2008-01-01

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy. PMID:19521545

Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

USGS Publications Warehouse

Xu, Wenzhao; Collingsworth, Paris D.; Bailey, Barbara; Carlson Mazur, Martha L.; Schaeffer, Jeff; Minsker, Barbara

2017-01-01

This paper proposes a geospatial analysis framework and software to interpret water-quality sampling data from towed undulating vehicles in near-real time. The framework includes data quality assurance and quality control processes, automated kriging interpolation along undulating paths, and local hotspot and cluster analyses. These methods are implemented in an interactive Web application developed using the Shiny package in the R programming environment to support near-real time analysis along with 2- and 3-D visualizations. The approach is demonstrated using historical sampling data from an undulating vehicle deployed at three rivermouth sites in Lake Michigan during 2011. The normalized root-mean-square error (NRMSE) of the interpolation averages approximately 10% in 3-fold cross validation. The results show that the framework can be used to track river plume dynamics and provide insights on mixing, which could be related to wind and seiche events.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards.

PubMed

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards

PubMed Central

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument. PMID:26601032

Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing.

PubMed

Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C J; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy

2016-02-01

Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Qualitative Data Analysis: A Compendium of Techniques and a Framework for Selection for School Psychology Research and Beyond

ERIC Educational Resources Information Center

Leech, Nancy L.; Onwuegbuzie, Anthony J.

2008-01-01

Qualitative researchers in school psychology have a multitude of analyses available for data. The purpose of this article is to present several of the most common methods for analyzing qualitative data. Specifically, the authors describe the following 18 qualitative analysis techniques: method of constant comparison analysis, keywords-in-context,…

Reliability Validation and Improvement Framework

DTIC Science & Technology

2012-11-01

systems . Steps in that direction include the use of the Architec- ture Tradeoff Analysis Method ® (ATAM®) developed at the Carnegie Mellon...embedded software • cyber - physical systems (CPSs) to indicate that the embedded software interacts with, manag - es, and controls a physical system [Lee...the use of formal static analysis methods to increase our confidence in system operation beyond testing. However, analysis results

A framework for analysis of sentinel events in medical student education.

PubMed

Cohen, Daniel M; Clinchot, Daniel M; Werman, Howard A

2013-11-01

Although previous studies have addressed student factors contributing to dismissal or withdrawal from medical school for academic reasons, little information is available regarding institutional factors that may hinder student progress. The authors describe the development and application of a framework for sentinel event (SE) root cause analysis to evaluate cases in which students are dismissed or withdraw because of failure to progress in the medical school curriculum. The SE in medical student education (MSE) framework was piloted at the Ohio State University College of Medicine (OSUCOM) during 2010-2012. Faculty presented cases using the framework during academic oversight committee discussions. Nine SEs in MSE were presented using the framework. Major institution-level findings included the need for improved communication, documentation of cognitive and noncognitive (e.g., mental health) issues, clarification of requirements for remediation and fitness for duty, and additional psychological services. Challenges related to alternative and combined programs were identified as well. The OSUCOM undertook system changes based on the action plans developed through the discussions of these SEs. An SE analysis process appears to be a useful method for making system changes in response to institutional issues identified in evaluation of cases in which students fail to progress in the medical school curriculum. The authors plan to continue to refine the SE in MSE framework and analysis process. Next steps include assessing whether analysis using this framework yields improved student outcomes with universal applications for other institutions.

Large-scale structural analysis: The structural analyst, the CSM Testbed and the NAS System

NASA Technical Reports Server (NTRS)

Knight, Norman F., Jr.; Mccleary, Susan L.; Macy, Steven C.; Aminpour, Mohammad A.

1989-01-01

The Computational Structural Mechanics (CSM) activity is developing advanced structural analysis and computational methods that exploit high-performance computers. Methods are developed in the framework of the CSM testbed software system and applied to representative complex structural analysis problems from the aerospace industry. An overview of the CSM testbed methods development environment is presented and some numerical methods developed on a CRAY-2 are described. Selected application studies performed on the NAS CRAY-2 are also summarized.

DRUG EVALUATION AND DECISION MAKING IN CATALONIA: DEVELOPMENT AND VALIDATION OF A METHODOLOGICAL FRAMEWORK BASED ON MULTI-CRITERIA DECISION ANALYSIS (MCDA) FOR ORPHAN DRUGS.

PubMed

Gilabert-Perramon, Antoni; Torrent-Farnell, Josep; Catalan, Arancha; Prat, Alba; Fontanet, Manel; Puig-Peiró, Ruth; Merino-Montero, Sandra; Khoury, Hanane; Goetghebeur, Mireille M; Badia, Xavier

2017-01-01

The aim of this study was to adapt and assess the value of a Multi-Criteria Decision Analysis (MCDA) framework (EVIDEM) for the evaluation of Orphan drugs in Catalonia (Catalan Health Service). The standard evaluation and decision-making procedures of CatSalut were compared with the EVIDEM methodology and contents. The EVIDEM framework was adapted to the Catalan context, focusing on the evaluation of Orphan drugs (PASFTAC program), during a Workshop with sixteen PASFTAC members. The criteria weighting was done using two different techniques (nonhierarchical and hierarchical). Reliability was assessed by re-test. The EVIDEM framework and methodology was found useful and feasible for Orphan drugs evaluation and decision making in Catalonia. All the criteria considered for the development of the CatSalut Technical Reports and decision making were considered in the framework. Nevertheless, the framework could improve the reporting of some of these criteria (i.e., "unmet needs" or "nonmedical costs"). Some Contextual criteria were removed (i.e., "Mandate and scope of healthcare system", "Environmental impact") or adapted ("population priorities and access") for CatSalut purposes. Independently of the weighting technique considered, the most important evaluation criteria identified for orphan drugs were: "disease severity", "unmet needs" and "comparative effectiveness", while the "size of the population" had the lowest relevance for decision making. Test-retest analysis showed weight consistency among techniques, supporting reliability overtime. MCDA (EVIDEM framework) could be a useful tool to complement the current evaluation methods of CatSalut, contributing to standardization and pragmatism, providing a method to tackle ethical dilemmas and facilitating discussions related to decision making.

CAD-Based Aerodynamic Design of Complex Configurations using a Cartesian Method

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Pulliam, Thomas H.

2003-01-01

A modular framework for aerodynamic optimization of complex geometries is developed. By working directly with a parametric CAD system, complex-geometry models are modified nnd tessellated in an automatic fashion. The use of a component-based Cartesian method significantly reduces the demands on the CAD system, and also provides for robust and efficient flowfield analysis. The optimization is controlled using either a genetic or quasi-Newton algorithm. Parallel efficiency of the framework is maintained even when subject to limited CAD resources by dynamically re-allocating the processors of the flow solver. Overall, the resulting framework can explore designs incorporating large shape modifications and changes in topology.

Modelling runway incursion severity.

PubMed

Wilke, Sabine; Majumdar, Arnab; Ochieng, Washington Y

2015-06-01

Analysis of the causes underlying runway incursions is fundamental for the development of effective mitigation measures. However, there are significant weaknesses in the current methods to model these factors. This paper proposes a structured framework for modelling causal factors and their relationship to severity, which includes a description of the airport surface system architecture, establishment of terminological definitions, the determination and collection of appropriate data, the analysis of occurrences for severity and causes, and the execution of a statistical analysis framework. It is implemented in the context of U.S. airports, enabling the identification of a number of priority interventions, including the need for better investigation and causal factor capture, recommendations for airfield design, operating scenarios and technologies, and better training for human operators in the system. The framework is recommended for the analysis of runway incursions to support safety improvements and the methodology is transferable to other areas of aviation safety risk analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

The study of using earth tide response of groundwater level and rainfall recharge to identify groundwater aquifer

NASA Astrophysics Data System (ADS)

Huang, W. J.; Hsu, C. H.; Chang, L. C.; Chiang, C. J.; Wang, Y. S.; Lu, W. C.

2017-12-01

Hydrogeological framework is the most important basis for groundwater analysis and simulation. Conventionally, the core drill is a most commonly adopted skill to acquire the core's data with the help of other research methods to artificially determine the result. Now, with the established groundwater station network, there are a lot of groundwater level information available. Groundwater level is an integrated presentation of the hydrogeological framework and the external pumping and recharge system. Therefore, how to identify the hydrogeological framework from a large number of groundwater level data is an important subject. In this study, the frequency analysis method and rainfall recharge mechanism were used to identify the aquifer where the groundwater level's response frequency and amplitude react to the earth tide. As the earth tide change originates from the gravity caused by the paths of sun and moon, it leads to soil stress and strain changes, which further affects the groundwater level. The scale of groundwater level's change varies with the influence of aquifer pressure systems such as confined or unconfined aquifers. This method has been applied to the identification of aquifers in the Cho-Shui River Alluvial Fan. The results of the identification are compared to the records of core drill and they both are quite consistent. It is shown that the identification methods developed in this study can considerably contribute to the identification of hydrogeological framework.

Study protocol Implementation of the Veder contact method (VCM) in daily nursing home care for people with dementia: an evaluation based on the RE-AIM framework.

PubMed

Boersma, Petra; Van Weert, Julia C M; van Meijel, Berno; van de Ven, Peter M; Dröes, Rose-Marie

2017-07-01

People with dementia in nursing homes benefit from person-centred care methods. Studies examining the effect of these methods often fail to report about the implementation of these methods. The present study aims to describe the implementation of the Veder contact method (VCM) in daily nursing home care. A process analysis will be conducted based on qualitative data from focus groups with caregivers and interviews with key figures. To investigate whether the implementation of VCM is reflected in the attitude and behaviour of caregivers and in the behaviour and quality of life of people with dementia, a controlled observational cohort study will be conducted. Six nursing home wards implementing VCM will be compared with six control wards providing Care As Usual. Quantitative data from caregivers and residents will be collected before (T0), and 9-12 months after the implementation (T1). Qualitative analysis and multilevel analyses will be carried out on the collected data and structured based on the constructs of the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance). By using the RE-AIM framework this study introduces a structured and comprehensive way of investigating the implementation process and implementation effectiveness of person-centred care methods in daily dementia care.

Comparative SWOT analysis of strategic environmental assessment systems in the Middle East and North Africa region.

PubMed

Rachid, G; El Fadel, M

2013-08-15

This paper presents a SWOT analysis of SEA systems in the Middle East North Africa region through a comparative examination of the status, application and structure of existing systems based on country-specific legal, institutional and procedural frameworks. The analysis is coupled with the multi-attribute decision making method (MADM) within an analytical framework that involves both performance analysis based on predefined evaluation criteria and countries' self-assessment of their SEA system through open-ended surveys. The results show heterogenous status with a general delayed progress characterized by varied levels of weaknesses embedded in the legal and administrative frameworks and poor integration with the decision making process. Capitalizing on available opportunities, the paper highlights measures to enhance the development and enactment of SEA in the region. Copyright © 2013 Elsevier Ltd. All rights reserved.

A model of "integrated scientific method" and its application for the analysis of instruction

NASA Astrophysics Data System (ADS)

Rusbult, Craig Francis

A model of 'integrated scientific method' (ISM) was constructed as a framework for describing the process of science in terms of activities (formulating a research problem, and inventing and evaluating actions--such as selecting and inventing theories, evaluating theories, designing experiments, and doing experiments--intended to solve the problem) and evaluation criteria (empirical, conceptual, and cultural-personal). Instead of trying to define the scientific method, ISM is intended to serve as a flexible framework that--by varying the characteristics of its components, their integrated relationships, and their relative importance can be used to describe a variety of scientific methods, and a variety of perspectives about what constitutes an accurate portrayal of scientific methods. This framework is outlined visually and verbally, followed by an elaboration of the framework and my own views about science, and an evaluation of whether ISM can serve as a relatively neutral framework for describing a wide range of science practices and science interpretations. ISM was used to analyze an innovative, guided inquiry classroom (taught by Susan Johnson, using Genetics Construction Kit software) in which students do simulated scientific research by solving classical genetics problems that require effect-to-cause reasoning and theory revision. The immediate goal of analysis was to examine the 'science experiences' of students, to determine how the 'structure of instruction' provides opportunities for these experiences. Another goal was to test and improve the descriptive and analytical utility of ISM. In developing ISM, a major objective was to make ISM educationally useful. A concluding discussion includes controversies about "the nature of science" and how to teach it, how instruction can expand opportunities for student experience, and how goal-oriented intentional learning (using ISM might improve the learning, retention, and transfer of thinking skills. Potential educational applications of ISM could involve its use for instructional analysis or design, or for teaching students in the classroom; or ISM and IDM (a closely related, generalized 'integrated design method') could play valuable roles in a 'wide spiral' curriculum designed for the coordinated teaching of thinking skills, including creativity and critical thinking, across a wide range of subjects.

Dealing with Complex Causality in Realist Synthesis: The Promise of Qualitative Comparative Analysis

ERIC Educational Resources Information Center

Sager, Fritz; Andereggen, Celine

2012-01-01

In this article, the authors state two arguments: first, that the four categories of context, politics, polity, and policy make an adequate framework for systematic review being both exhaustive and parsimonious; second, that the method of qualitative comparative analysis (QCA) is an appropriate methodical approach for gaining realistic results…

A Customizable Language Learning Support System Using Ontology-Driven Engine

ERIC Educational Resources Information Center

Wang, Jingyun; Mendori, Takahiko; Xiong, Juan

2013-01-01

This paper proposes a framework for web-based language learning support systems designed to provide customizable pedagogical procedures based on the analysis of characteristics of both learner and course. This framework employs a course-centered ontology and a teaching method ontology as the foundation for the student model, which includes learner…

Demographic Accounting and Model-Building. Education and Development Technical Reports.

ERIC Educational Resources Information Center

Stone, Richard

This report describes and develops a model for coordinating a variety of demographic and social statistics within a single framework. The framework proposed, together with its associated methods of analysis, serves both general and specific functions. The general aim of these functions is to give numerical definition to the pattern of society and…

Guidance for Organisational Strategy on Knowledge to Action from Conceptual Frameworks and Practice

ERIC Educational Resources Information Center

Willis, Cameron; Riley, Barbara; Lewis, Mary; Stockton, Lisa; Yessis, Jennifer

2017-01-01

This paper aims to provide public health organisations involved in chronic disease prevention with conceptual and practical guidance for developing contextually sensitive knowledge-to-action (KTA) strategies. Methods involve an analysis of 13 relevant conceptual KTA frameworks, and a review of three case examples of organisations with active KTA…

Use of a Numerical Strategy Framework in the Professional Development of Teachers

ERIC Educational Resources Information Center

Laxman, Kumar; Hughes, Peter

2015-01-01

Derived initially from a strategic analysis of children's methods of counting, the New Zealand Numeracy Projects used, as a starting point for the professional development of teachers, a strategy framework that traces children's development in number reasoning. A pilot study indicated the usefulness of professional development where teachers use…

Origins of School Nursing

ERIC Educational Resources Information Center

Houlahan, Bridget

2018-01-01

This study investigated the origin and implementation of school nursing in New York City, using traditional historical methods with a social history framework. The intent of this research was to produce a comprehensive historical analysis of school nursing at the turn of the 20th century in order to provide a historical framework to promote the…

Meta-analysis of pathway enrichment: combining independent and dependent omics data sets.

PubMed

Kaever, Alexander; Landesfeind, Manuel; Feussner, Kirstin; Morgenstern, Burkhard; Feussner, Ivo; Meinicke, Peter

2014-01-01

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

PubMed

Tucker, George; Loh, Po-Ru; Berger, Bonnie

2013-10-04

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts. We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance. Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

PubMed Central

Nøhr, C.; Sørensen, E. M.; Gudes, O.; Geraghty, E. M.; Shaw, N. T.; Bivona-Tellez, C.

2014-01-01

Summary Objectives The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. Method The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. Results A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Conclusion Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health. PMID:25123730

A competency framework for colonoscopy training derived from cognitive task analysis techniques and expert review.

PubMed

Zupanc, Christine M; Burgess-Limerick, Robin; Hill, Andrew; Riek, Stephan; Wallis, Guy M; Plooy, Annaliese M; Horswill, Mark S; Watson, Marcus O; Hewett, David G

2015-12-01

Colonoscopy is a difficult cognitive-perceptual-motor task. Designing an appropriate instructional program for such a task requires an understanding of the knowledge, skills and attitudes underpinning the competency required to perform the task. Cognitive task analysis techniques provide an empirical means of deriving this information. Video recording and a think-aloud protocol were conducted while 20 experienced endoscopists performed colonoscopy procedures. "Cued-recall" interviews were also carried out post-procedure with nine of the endoscopists. Analysis of the resulting transcripts employed the constant comparative coding method within a grounded theory framework. The resulting draft competency framework was modified after review during semi-structured interviews conducted with six expert endoscopists. The proposed colonoscopy competency framework consists of twenty-seven skill, knowledge and attitude components, grouped into six categories (clinical knowledge; colonoscope handling; situation awareness; heuristics and strategies; clinical reasoning; and intra- and inter-personal). The colonoscopy competency framework provides a principled basis for the design of a training program, and for the design of formative assessment to gauge progress towards attaining the knowledge, skills and attitudes underpinning the achievement of colonoscopy competence.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

PubMed

Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2015-12-01

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.

Integrating Cognitive Task Analysis into Instructional Systems Development.

ERIC Educational Resources Information Center

Ryder, Joan M.; Redding, Richard E.

1993-01-01

Discussion of instructional systems development (ISD) focuses on recent developments in cognitive task analysis and describes the Integrated Task Analysis Model, a framework for integrating cognitive and behavioral task analysis methods within the ISD model. Three components of expertise are analyzed: skills, knowledge, and mental models. (96…

Big data analysis framework for healthcare and social sectors in Korea.

PubMed

Song, Tae-Min; Ryu, Seewon

2015-01-01

We reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea. We reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0). We formulated a conceptual framework of big data in the healthcare and social service sectors at the national level. As a specific case, we designed a process and method of social big data analysis on suicide buzz. Developed countries (e.g., the United States, the UK, Singapore, Australia, and even OECD and EU) are emphasizing the potential of big data, and using it as a tool to solve their long-standing problems. Big data strategies for the healthcare and social service sectors were formulated based on an ICT-based policy of current government and the strategic goals of the Ministry of Health and Welfare. We suggest a framework of big data analysis in the healthcare and welfare service sectors separately and assigned them tentative names: 'health risk analysis center' and 'integrated social welfare service network'. A framework of social big data analysis is presented by applying it to the prevention and proactive detection of suicide in Korea. There are some concerns with the utilization of big data in the healthcare and social welfare sectors. Thus, research on these issues must be conducted so that sophisticated and practical solutions can be reached.

Highly sensitive analysis of polycyclic aromatic hydrocarbons in environmental water with porous cellulose/zeolitic imidazolate framework-8 composite microspheres as a novel adsorbent coupled with high-performance liquid chromatography.

PubMed

Liang, Xiaotong; Liu, Shengquan; Zhu, Rong; Xiao, Lixia; Yao, Shouzhuo

2016-07-01

In this work, novel cellulose/zeolitic imidazolate frameworks-8 composite microspheres have been successfully fabricated and utilized as sorbent for environmental polycyclic aromatic hydrocarbons efficient extraction and sensitive analysis. The composite microspheres were synthesized through the in situ hydrothermal growth of zeolitic imidazolate frameworks-8 on cellulose matrix, and exhibited favorable hierarchical structure with chemical composition as assumed through scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction patterns, and Brunauer-Emmett-Teller surface areas characterization. A robust and highly efficient method was then successfully developed with as-prepared composite microspheres as novel solid-phase extraction sorbent with optimum extraction conditions, such as sorbent amount, sample volume, extraction time, desorption conditions, volume of organic modifier, and ionic strength. The method exhibited high sensitivity with low limit of detection down to 0.1-1.0 ng/L and satisfactory linearity with correlation coefficients ranging from 0.9988 to 0.9999, as well as good recoveries of 66.7-121.2% with relative standard deviations less than 10% for environmental polycyclic aromatic hydrocarbons analysis. Thus, our method was convenient and efficient for polycyclic aromatic hydrocarbons extraction and detection, potential for future environmental water samples analysis. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Role of Multiphysics Simulation in Multidisciplinary Analysis

NASA Technical Reports Server (NTRS)

Rifai, Steven M.; Ferencz, Robert M.; Wang, Wen-Ping; Spyropoulos, Evangelos T.; Lawrence, Charles; Melis, Matthew E.

1998-01-01

This article describes the applications of the Spectrum(Tm) Solver in Multidisciplinary Analysis (MDA). Spectrum, a multiphysics simulation software based on the finite element method, addresses compressible and incompressible fluid flow, structural, and thermal modeling as well as the interaction between these disciplines. Multiphysics simulation is based on a single computational framework for the modeling of multiple interacting physical phenomena. Interaction constraints are enforced in a fully-coupled manner using the augmented-Lagrangian method. Within the multiphysics framework, the finite element treatment of fluids is based on Galerkin-Least-Squares (GLS) method with discontinuity capturing operators. The arbitrary-Lagrangian-Eulerian method is utilized to account for deformable fluid domains. The finite element treatment of solids and structures is based on the Hu-Washizu variational principle. The multiphysics architecture lends itself naturally to high-performance parallel computing. Aeroelastic, propulsion, thermal management and manufacturing applications are presented.

Solid-phase extraction with the metal-organic framework MIL-101(Cr) combined with direct analysis in real time mass spectrometry for the fast analysis of triazine herbicides.

PubMed

Li, Xianjiang; Xing, Jiawei; Chang, Cuilan; Wang, Xin; Bai, Yu; Yan, Xiuping; Liu, Huwei

2014-06-01

MIL-101(Cr) is an excellent metal-organic framework with high surface area and nanoscale cavities, making it promising in solid-phase extraction. Herein, we used MIL-101(Cr) as a solid-phase extraction packing material combined with fast detection of direct analysis in real time mass spectrometry (DART-MS) for the analysis of triazine herbicides. After systematic optimization of the operation parameters, including the gas temperature of DART, the moving speed of the 1D platform, solvent for desorption, amount of MIL-101(Cr) extraction time, eluent volume and salt concentration, this method can realize the simultaneous detection of five kinds of triazine herbicides. The limits of detection were 0.1∼0.2 ng/mL and the linear ranges covered more than two orders of magnitude with the quantitation limits of 0.5∼1 ng/mL. Moreover, the developed method has been applied for the analysis of lake water samples and the recoveries for spiked analytes were in the range of 85∼110%. These results showed that solid-phase extraction with metal-organic frameworks is an efficient sample preparation approach for DART-MS analysis and could find more applications in environmental analysis. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research

ERIC Educational Resources Information Center

Rohlfing, Ingo; Schneider, Carsten Q.

2018-01-01

The combination of Qualitative Comparative Analysis (QCA) with process tracing, which we call set-theoretic multimethod research (MMR), is steadily becoming more popular in empirical research. Despite the fact that both methods have an elected affinity based on set theory, it is not obvious how a within-case method operating in a single case and a…

A Guide to Analyzing Message-Response Sequences and Group Interaction Patterns in Computer-Mediated Communication

ERIC Educational Resources Information Center

Jeong, Allan

2005-01-01

This paper proposes a set of methods and a framework for evaluating, modeling, and predicting group interactions in computer-mediated communication. The method of sequential analysis is described along with specific software tools and techniques to facilitate the analysis of message-response sequences. In addition, the Dialogic Theory and its…

A Framework for Performing Multiscale Stochastic Progressive Failure Analysis of Composite Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2006-01-01

A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis - Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

A Framework for Performing Multiscale Stochastic Progressive Failure Analysis of Composite Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2007-01-01

A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis-Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.

Applications of algebraic topology to compatible spatial discretizations.

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

Bochev, Pavel Blagoveston; Hyman, James M.

We provide a common framework for compatible discretizations using algebraic topology to guide our analysis. The main concept is the natural inner product on cochains, which induces a combinatorial Hodge theory. The framework comprises of mutually consistent operations of differentiation and integration, has a discrete Stokes theorem, and preserves the invariants of the DeRham cohomology groups. The latter allows for an elementary calculation of the kernel of the discrete Laplacian. Our framework provides an abstraction that includes examples of compatible finite element, finite volume and finite difference methods. We describe how these methods result from the choice of a reconstructionmore » operator and when they are equivalent.« less

Age-structured mark-recapture analysis: A virtual-population-analysis-based model for analyzing age-structured capture-recapture data

USGS Publications Warehouse

Coggins, L.G.; Pine, William E.; Walters, C.J.; Martell, S.J.D.

2006-01-01

We present a new model to estimate capture probabilities, survival, abundance, and recruitment using traditional Jolly-Seber capture-recapture methods within a standard fisheries virtual population analysis framework. This approach compares the numbers of marked and unmarked fish at age captured in each year of sampling with predictions based on estimated vulnerabilities and abundance in a likelihood function. Recruitment to the earliest age at which fish can be tagged is estimated by using a virtual population analysis method to back-calculate the expected numbers of unmarked fish at risk of capture. By using information from both marked and unmarked animals in a standard fisheries age structure framework, this approach is well suited to the sparse data situations common in long-term capture-recapture programs with variable sampling effort. ?? Copyright by the American Fisheries Society 2006.

[Satisfaction and perceived quality of people insured by the Social Health Protection in Mexico. Methodological foundations].

PubMed

Saturno-Hernández, Pedro J; Gutiérrez-Reyes, Juan Pablo; Vieyra-Romero, Waldo Ivan; Romero-Martínez, Martín; O'Shea-Cuevas, Gabriel Jaime; Lozano-Herrera, Javier; Tavera-Martínez, Sonia; Hernández-Ávila, Mauricio

2016-01-01

To describe the conceptual framework and methods for implementation and analysis of the satisfaction survey of the Mexican System for Social Protection in Health. We analyze the methodological elements of the 2013, 2014 and 2015 surveys, including the instrument, sampling method and study design, conceptual framework, and characteristics and indicators of the analysis. The survey captures information on perceived quality and satisfaction. Sampling has national and State representation. Simple and composite indicators (index of satisfaction and rate of reported quality problems) are built and described. The analysis is completed using Pareto diagrams, correlation between indicators and association with satisfaction by means of multivariate models. The measurement of satisfaction and perceived quality is a complex but necessary process to comply with regulations and to identify strategies for improvement. The described survey presents a design and rigorous analysis focused on its utility for improving.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

Unmanned Tactical Autonomous Control and Collaboration Situation Awareness

DTIC Science & Technology

2017-06-01

methodology framework using interdependence analysis (IA) tables for informing design requirements based on SA requirements. Future research should seek...requirements of UTACC. The authors then apply SA principles to Coactive Design in order to inform robotic design. The result is a methodology framework using...28  2.  Non -intrusive Methods ................................................................29  3.  Post-Mission Reviews

A framework to support decision making in the selection of sustainable drainage system design alternatives.

PubMed

Wang, Mingming; Sweetapple, Chris; Fu, Guangtao; Farmani, Raziyeh; Butler, David

2017-10-01

This paper presents a new framework for decision making in sustainable drainage system (SuDS) scheme design. It integrates resilience, hydraulic performance, pollution control, rainwater usage, energy analysis, greenhouse gas (GHG) emissions and costs, and has 12 indicators. The multi-criteria analysis methods of entropy weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were selected to support SuDS scheme selection. The effectiveness of the framework is demonstrated with a SuDS case in China. Indicators used include flood volume, flood duration, a hydraulic performance indicator, cost and resilience. Resilience is an important design consideration, and it supports scheme selection in the case study. The proposed framework will help a decision maker to choose an appropriate design scheme for implementation without subjectivity. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Decision Analysis Framework for Evaluation of Helmet Mounted Display Alternatives for Fighter Aircraft

DTIC Science & Technology

2014-12-26

additive value function, which assumes mutual preferential independence (Gregory S. Parnell, 2013). In other words, this method can be used if the... additive value function method to calculate the aggregate value of multiple objectives. Step 9 : Sensitivity Analysis Once the global values are...gravity metric, the additive method will be applied using equal weights for each axis value function. Pilot Satisfaction (Usability) As expressed

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

From expert-derived user needs to user-perceived ease of use and usefulness: a two-phase mixed-methods evaluation framework.

PubMed

Boland, Mary Regina; Rusanov, Alexander; So, Yat; Lopez-Jimenez, Carlos; Busacca, Linda; Steinman, Richard C; Bakken, Suzanne; Bigger, J Thomas; Weng, Chunhua

2014-12-01

Underspecified user needs and frequent lack of a gold standard reference are typical barriers to technology evaluation. To address this problem, this paper presents a two-phase evaluation framework involving usability experts (phase 1) and end-users (phase 2). In phase 1, a cross-system functionality alignment between expert-derived user needs and system functions was performed to inform the choice of "the best available" comparison system to enable a cognitive walkthrough in phase 1 and a comparative effectiveness evaluation in phase 2. During phase 2, five quantitative and qualitative evaluation methods are mixed to assess usability: time-motion analysis, software log, questionnaires - System Usability Scale and the Unified Theory of Acceptance of Use of Technology, think-aloud protocols, and unstructured interviews. Each method contributes data for a unique measure (e.g., time motion analysis contributes task-completion-time; software log contributes action transition frequency). The measures are triangulated to yield complementary insights regarding user-perceived ease-of-use, functionality integration, anxiety during use, and workflow impact. To illustrate its use, we applied this framework in a formative evaluation of a software called Integrated Model for Patient Care and Clinical Trials (IMPACT). We conclude that this mixed-methods evaluation framework enables an integrated assessment of user needs satisfaction and user-perceived usefulness and usability of a novel design. This evaluation framework effectively bridges the gap between co-evolving user needs and technology designs during iterative prototyping and is particularly useful when it is difficult for users to articulate their needs for technology support due to the lack of a baseline. Copyright © 2013 Elsevier Inc. All rights reserved.

Risk assessment of vector-borne diseases for public health governance.

PubMed

Sedda, L; Morley, D W; Braks, M A H; De Simone, L; Benz, D; Rogers, D J

2014-12-01

In the context of public health, risk governance (or risk analysis) is a framework for the assessment and subsequent management and/or control of the danger posed by an identified disease threat. Generic frameworks in which to carry out risk assessment have been developed by various agencies. These include monitoring, data collection, statistical analysis and dissemination. Due to the inherent complexity of disease systems, however, the generic approach must be modified for individual, disease-specific risk assessment frameworks. The analysis was based on the review of the current risk assessments of vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, ECDC, FAO, CDC etc…). Literature, legislation and statistical assessment of the risk analysis frameworks. This review outlines the need for the development of a general public health risk assessment method for vector-borne diseases, in order to guarantee that sufficient information is gathered to apply robust models of risk assessment. Stochastic (especially spatial) methods, often in Bayesian frameworks are now gaining prominence in standard risk assessment procedures because of their ability to assess accurately model uncertainties. Risk assessment needs to be addressed quantitatively wherever possible, and submitted with its quality assessment in order to enable successful public health measures to be adopted. In terms of current practice, often a series of different models and analyses are applied to the same problem, with results and outcomes that are difficult to compare because of the unknown model and data uncertainties. Therefore, the risk assessment areas in need of further research are identified in this article. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

UNC-Utah NA-MIC framework for DTI fiber tract analysis.

PubMed

Verde, Audrey R; Budin, Francois; Berger, Jean-Baptiste; Gupta, Aditya; Farzinfar, Mahshid; Kaiser, Adrien; Ahn, Mihye; Johnson, Hans; Matsui, Joy; Hazlett, Heather C; Sharma, Anuja; Goodlett, Casey; Shi, Yundi; Gouttard, Sylvain; Vachet, Clement; Piven, Joseph; Zhu, Hongtu; Gerig, Guido; Styner, Martin

2014-01-01

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

UNC-Utah NA-MIC framework for DTI fiber tract analysis

PubMed Central

Verde, Audrey R.; Budin, Francois; Berger, Jean-Baptiste; Gupta, Aditya; Farzinfar, Mahshid; Kaiser, Adrien; Ahn, Mihye; Johnson, Hans; Matsui, Joy; Hazlett, Heather C.; Sharma, Anuja; Goodlett, Casey; Shi, Yundi; Gouttard, Sylvain; Vachet, Clement; Piven, Joseph; Zhu, Hongtu; Gerig, Guido; Styner, Martin

2014-01-01

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts. PMID:24409141

From Molecules to Cells to Organisms: Understanding Health and Disease with Multidimensional Single-Cell Methods

NASA Astrophysics Data System (ADS)

Candia, Julián

2013-03-01

The multidimensional nature of many single-cell measurements (e.g. multiple markers measured simultaneously using Fluorescence-Activated Cell Sorting (FACS) technologies) offers unprecedented opportunities to unravel emergent phenomena that are governed by the cooperative action of multiple elements across different scales, from molecules and proteins to cells and organisms. We will discuss an integrated analysis framework to investigate multicolor FACS data from different perspectives: Singular Value Decomposition to achieve an effective dimensional reduction in the data representation, machine learning techniques to separate different patient classes and improve diagnosis, as well as a novel cell-similarity network analysis method to identify cell subpopulations in an unbiased manner. Besides FACS data, this framework is versatile: in this vein, we will demonstrate an application to the multidimensional single-cell shape analysis of healthy and prematurely aged cells.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Framework for shape analysis of white matter fiber bundles.

PubMed

Glozman, Tanya; Bruckert, Lisa; Pestilli, Franco; Yecies, Derek W; Guibas, Leonidas J; Yeom, Kristen W

2018-02-15

Diffusion imaging coupled with tractography algorithms allows researchers to image human white matter fiber bundles in-vivo. These bundles are three-dimensional structures with shapes that change over time during the course of development as well as in pathologic states. While most studies on white matter variability focus on analysis of tissue properties estimated from the diffusion data, e.g. fractional anisotropy, the shape variability of white matter fiber bundle is much less explored. In this paper, we present a set of tools for shape analysis of white matter fiber bundles, namely: (1) a concise geometric model of bundle shapes; (2) a method for bundle registration between subjects; (3) a method for deformation estimation. Our framework is useful for analysis of shape variability in white matter fiber bundles. We demonstrate our framework by applying our methods on two datasets: one consisting of data for 6 normal adults and another consisting of data for 38 normal children of age 11 days to 8.5 years. We suggest a robust and reproducible method to measure changes in the shape of white matter fiber bundles. We demonstrate how this method can be used to create a model to assess age-dependent changes in the shape of specific fiber bundles. We derive such models for an ensemble of white matter fiber bundles on our pediatric dataset and show that our results agree with normative human head and brain growth data. Creating these models for a large pediatric longitudinal dataset may improve understanding of both normal development and pathologic states and propose novel parameters for the examination of the pediatric brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain.

PubMed

Saramago, Pedro; Woods, Beth; Weatherly, Helen; Manca, Andrea; Sculpher, Mark; Khan, Kamran; Vickers, Andrew J; MacPherson, Hugh

2016-10-06

Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes. This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises. Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance. The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

Alternatives Assessment Frameworks: Research Needs for the Informed Substitution of Hazardous Chemicals

PubMed Central

Jacobs, Molly M.; Malloy, Timothy F.; Tickner, Joel A.; Edwards, Sally

2015-01-01

Background Given increasing pressures for hazardous chemical replacement, there is growing interest in alternatives assessment to avoid substituting a toxic chemical with another of equal or greater concern. Alternatives assessment is a process for identifying, comparing, and selecting safer alternatives to chemicals of concern (including those used in materials, processes, or technologies) on the basis of their hazards, performance, and economic viability. Objectives The purposes of this substantive review of alternatives assessment frameworks are to identify consistencies and differences in methods and to outline needs for research and collaboration to advance science policy practice. Methods This review compares methods used in six core components of these frameworks: hazard assessment, exposure characterization, life-cycle impacts, technical feasibility evaluation, economic feasibility assessment, and decision making. Alternatives assessment frameworks published from 1990 to 2014 were included. Results Twenty frameworks were reviewed. The frameworks were consistent in terms of general process steps, but some differences were identified in the end points addressed. Methodological gaps were identified in the exposure characterization, life-cycle assessment, and decision–analysis components. Methods for addressing data gaps remain an issue. Discussion Greater consistency in methods and evaluation metrics is needed but with sufficient flexibility to allow the process to be adapted to different decision contexts. Conclusion Although alternatives assessment is becoming an important science policy field, there is a need for increased cross-disciplinary collaboration to refine methodologies in support of the informed substitution and design of safer chemicals, materials, and products. Case studies can provide concrete lessons to improve alternatives assessment. Citation Jacobs MM, Malloy TF, Tickner JA, Edwards S. 2016. Alternatives assessment frameworks: research needs for the informed substitution of hazardous chemicals. Environ Health Perspect 124:265–280; http://dx.doi.org/10.1289/ehp.1409581 PMID:26339778

A Qualitative Analysis Framework Using Natural Language Processing and Graph Theory

ERIC Educational Resources Information Center

Tierney, Patrick J.

2012-01-01

This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool--graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a "graphical solution". Rather, it is a method to help qualitative…

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

Effects of Anchor Item Methods on the Detection of Differential Item Functioning within the Family of Rasch Models

ERIC Educational Resources Information Center

Wang, Wen-Chung

2004-01-01

Scale indeterminacy in analysis of differential item functioning (DIF) within the framework of item response theory can be resolved by imposing 3 anchor item methods: the equal-mean-difficulty method, the all-other anchor item method, and the constant anchor item method. In this article, applicability and limitations of these 3 methods are…

Improving Quality and Reducing Waste in Allied Health Workplace Education Programs: A Pragmatic Operational Education Framework Approach.

PubMed

Golder, Janet; Farlie, Melanie K; Sevenhuysen, Samantha

2016-01-01

Efficient utilisation of education resources is required for the delivery of effective learning opportunities for allied health professionals. This study aimed to develop an education framework to support delivery of high-quality education within existing education resources. This study was conducted in a large metropolitan health service. Homogenous and purposive sampling methods were utilised in Phase 1 (n=43) and 2 (n=14) consultation stages. Participants included 25 allied health professionals, 22 managers, 1 educator, and 3 executives. Field notes taken during 43 semi-structured interviews and 4 focus groups were member-checked, and semantic thematic analysis methods were utilised. Framework design was informed by existing published framework development guides. The framework model contains governance, planning, delivery, and evaluation and research elements and identifies performance indicators, practice examples, and support tools for a range of stakeholders. Themes integrated into framework content include improving quality of education and training provided and delivery efficiency, greater understanding of education role requirements, and workforce support for education-specific knowledge and skill development. This framework supports efficient delivery of allied health workforce education and training to the highest standard, whilst pragmatically considering current allied health education workforce demands.

Teaching Games for Understanding in American High-School Soccer: A Quantitative Data Analysis Using the Game Performance Assessment Instrument

ERIC Educational Resources Information Center

Harvey, Stephen; Cushion, Christopher J.; Wegis, Heidi M.; Massa-Gonzalez, Ada N.

2010-01-01

Background: Previous research examining the effectiveness of the Teaching Games for Understanding (TGfU) approach has been equivocal. This has been hampered by a dependence on a comparative (i.e., "which method is best?") theoretical framework. An alternative "practice-referenced" framework has the potential to examine the effectiveness of TGfU…

Establishing a Framework of Influential Factors on Empowering Primary School Students in Peer Mediation

PubMed Central

Jorbozeh, Hamideh; Dehdari, Tahereh; Ashoorkhani, Mahnaz; Taghdisi, Mohammad Hossein

2014-01-01

Background: Empowerment of children and adolescents in terms of social skills is critical for promoting their social health. Objectives: This study attempts to explore a framework of influential factors on empowering primary school students by means of peer mediation from the stakeholders' point of view, as a qualitative content analysis design. Patients and Methods: This study was a qualitative content analysis (conventional method). Seven focused group discussions and six in-depth interviews were conducted with schoolchildren, parents and education authorities. Following each interview, recordings were entered to an open code software and analyzed. Data collection was continued up to data saturation. Results: Within the provided framework, the participants' views and comments were classified into two major categories “educational empowerment” and “social empowerment”, and into two themes; “program” and “advocacy”. The “program” theme included factors such as design and implementation, development, maintenance and improvement, and individual and social impact. The “advocacy” theme included factors such as social, emotional and physical support. Conclusions: The explained framework components regarding peer mediation are useful to design peace education programs and to empower school-age children in peer mediation. PMID:25763191

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents

ERIC Educational Resources Information Center

Hwang, Heungsun; Montreal, Hec; Dillon, William R.; Takane, Yoshio

2006-01-01

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Dillon, William R.

2010-01-01

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

Enrollment Projection within a Decision-Making Framework.

ERIC Educational Resources Information Center

Armstrong, David F.; Nunley, Charlene Wenckowski

1981-01-01

Two methods used to predict enrollment at Montgomery College in Maryland are compared and evaluated, and the administrative context in which they are used is considered. The two methods involve time series analysis (curve fitting) and indicator techniques (yield from components). (MSE)

Developing an evidence-based methodological framework to systematically compare HTA coverage decisions: A mixed methods study.

PubMed

Nicod, Elena; Kanavos, Panos

2016-01-01

Health Technology Assessment (HTA) often results in different coverage recommendations across countries for a same medicine despite similar methodological approaches. This paper develops and pilots a methodological framework that systematically identifies the reasons for these differences using an exploratory sequential mixed methods research design. The study countries were England, Scotland, Sweden and France. The methodological framework was built around three stages of the HTA process: (a) evidence, (b) its interpretation, and (c) its influence on the final recommendation; and was applied to two orphan medicinal products. The criteria accounted for at each stage were qualitatively analyzed through thematic analysis. Piloting the framework for two medicines, eight trials, 43 clinical endpoints and seven economic models were coded 155 times. Eighteen different uncertainties about this evidence were coded 28 times, 56% of which pertained to evidence commonly appraised and 44% to evidence considered by only some agencies. The poor agreement in interpreting this evidence (κ=0.183) was partly explained by stakeholder input (ns=48 times), or by agency-specific risk (nu=28 uncertainties) and value preferences (noc=62 "other considerations"), derived through correspondence analysis. Accounting for variability at each stage of the process can be achieved by codifying its existence and quantifying its impact through the application of this framework. The transferability of this framework to other disease areas, medicines and countries is ensured by its iterative and flexible nature, and detailed description. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Weberian Legacy of Thom Greenfield.

ERIC Educational Resources Information Center

Samier, Eugenie

1996-01-01

Traces through Thomas Greenfield's work his use of Max Weber's interpretive social analysis, including Weber's view of the individual unit of analysis, value topologies, comparative history methods, and analytical ideal topologies. Compares Greenfield's and Weber's metaphysical assumptions, ontological perspectives, and epistemological frameworks.…

Advanced Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Technical Exchange Meeting

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

Smith, Curtis

2013-09-01

During FY13, the INL developed an advanced SMR PRA framework which has been described in the report Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Detailed Technical Framework Specification, INL/EXT-13-28974 (April 2013). In this framework, the various areas are considered: Probabilistic models to provide information specific to advanced SMRs Representation of specific SMR design issues such as having co-located modules and passive safety features Use of modern open-source and readily available analysis methods Internal and external events resulting in impacts to safety All-hazards considerations Methods to support the identification of design vulnerabilities Mechanistic and probabilistic data needs to support modelingmore » and tools In order to describe this framework more fully and obtain feedback on the proposed approaches, the INL hosted a technical exchange meeting during August 2013. This report describes the outcomes of that meeting.« less

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Models for evaluating the performability of degradable computing systems

NASA Technical Reports Server (NTRS)

Wu, L. T.

1982-01-01

Recent advances in multiprocessor technology established the need for unified methods to evaluate computing systems performance and reliability. In response to this modeling need, a general modeling framework that permits the modeling, analysis and evaluation of degradable computing systems is considered. Within this framework, several user oriented performance variables are identified and shown to be proper generalizations of the traditional notions of system performance and reliability. Furthermore, a time varying version of the model is developed to generalize the traditional fault tree reliability evaluation methods of phased missions.

A framework of knowledge creation processes in participatory simulation of hospital work systems.

PubMed

Andersen, Simone Nyholm; Broberg, Ole

2017-04-01

Participatory simulation (PS) is a method to involve workers in simulating and designing their own future work system. Existing PS studies have focused on analysing the outcome, and minimal attention has been devoted to the process of creating this outcome. In order to study this process, we suggest applying a knowledge creation perspective. The aim of this study was to develop a framework describing the process of how ergonomics knowledge is created in PS. Video recordings from three projects applying PS of hospital work systems constituted the foundation of process mining analysis. The analysis resulted in a framework revealing the sources of ergonomics knowledge creation as sequential relationships between the activities of simulation participants sharing work experiences; experimenting with scenarios; and reflecting on ergonomics consequences. We argue that this framework reveals the hidden steps of PS that are essential when planning and facilitating PS that aims at designing work systems. Practitioner Summary: When facilitating participatory simulation (PS) in work system design, achieving an understanding of the PS process is essential. By applying a knowledge creation perspective and process mining, we investigated the knowledge-creating activities constituting the PS process. The analysis resulted in a framework of the knowledge-creating process in PS.

A Framework for Analysis of Research Risks and Benefits to Participants in Standard of Care Pragmatic Clinical Trials

PubMed Central

Chen, Stephanie C; Kim, Scott Y H

2016-01-01

Background/Aims Standard of care pragmatic clinical trials (SCPCTs) that compare treatments already in use could improve care and reduce cost but there is considerable debate about the research risks of SCPCTs and how to apply informed consent regulations to such trials. We sought to develop a framework integrating the insights from opposing sides of the debate. Methods We developed a formal risk-benefit analysis framework for SCPCTs and then applied it to key provisions of the U.S. federal regulations. Results Our formal framework for SCPCT risk-benefit analysis takes into account three key considerations: the ex ante estimates of risks and benefits of the treatments to be compared in a SCPCT, the allocation ratios of treatments inside and outside a SCPCT, and the significance of some participants receiving a different treatment inside a SCPCT than outside the trial. The framework provides practical guidance on how the research ethics regulations on informed consent should be applied to SCPCTs. Conclusions Our proposed formal model makes explicit the relationship between the concepts used by opposing sides of the debate about the research risks of SCPCTs and can be used to clarify the implications for informed consent. PMID:27365010

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

NASA Astrophysics Data System (ADS)

Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The

2015-11-01

While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.

A critical analysis of the South African Disaster Management Act and Policy Framework.

PubMed

van Niekerk, Dewald

2014-10-01

The promulgation of the South African Disaster Management Act No. 57 of 2002 and the National Disaster Management Policy Framework of 2005 placed South Africa at the international forefront by integrating disaster risk reduction into all spheres of government through a decentralised approach. Yet, good policy and legislation do not necessarily translate into good practice. This paper provides a critical analysis of the Act and Policy Framework. Using qualitative research methods, it analyses the attitudes and perceptions of senior public officials on all levels of government, the private sector and academia. The study finds that one of the weakest aspects of the Act and Framework is the absence of clear guidance to local municipalities. The placement of the disaster risk management function on all tiers of government remains problematic, funding is inadequate and overall knowledge and capacities for disaster risk reduction are insufficient. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014.

Resource allocation and purchasing arrangements to improve accessibility of medicines: Evidence from Iran

PubMed Central

Bastani, Peivand; Mehralian, Gholamhossein; Dinarvand, Rasoul

2015-01-01

Objective: The aim of this study was to review the current methods of pharmaceutical purchasing by Iranian insurance organizations within the World Bank conceptual framework model so as to provide applicable pharmaceutical resource allocation and purchasing (RAP) arrangements in Iran. Methods: This qualitative study was conducted through a qualitative document analysis (QDA), applying the four-step Scott method in document selection, and conducting 20 semi-structured interviews using a triangulation method. Furthermore, the data were analyzed applying five steps framework analysis using Atlas-ti software. Findings: The QDA showed that the purchasers face many structural, financing, payment, delivery and service procurement and purchasing challenges. Moreover, the findings of interviews are provided in three sections including demand-side, supply-side and price and incentive regime. Conclusion: Localizing RAP arrangements as a World Bank Framework in a developing country like Iran considers the following as the prerequisite for implementing strategic purchasing in pharmaceutical sector: The improvement of accessibility, subsidiary mechanisms, reimbursement of new drugs, rational use, uniform pharmacopeia, best supplier selection, reduction of induced demand and moral hazard, payment reform. It is obvious that for Iran, these customized aspects are more various and detailed than those proposed in a World Bank model for developing countries. PMID:25710045

Developing a Value Framework: The Need to Reflect the Opportunity Costs of Funding Decisions.

PubMed

Sculpher, Mark; Claxton, Karl; Pearson, Steven D

2017-02-01

A growing number of health care systems internationally use formal economic evaluation methods to support health care funding decisions. Recently, a range of organizations have been advocating forms of analysis that have been termed "value frameworks." There has also been a push for analytical methods to reflect a fuller range of benefits of interventions through multicriteria decision analysis. A key principle that is invariably neglected in current and proposed frameworks is the need to reflect evidence on the opportunity costs that health systems face when making funding decisions. The mechanisms by which opportunity costs are realized vary depending on the system's financial arrangements, but they always mean that a decision to fund a specific intervention for a particular patient group has the potential to impose costs on others in terms of forgone benefits. These opportunity costs are rarely explicitly reflected in analysis to support decisions, but recent developments to quantify benefits forgone make more appropriate analyses feasible. Opportunity costs also need to be reflected in decisions if a broader range of attributes of benefit is considered, and opportunity costs are a key consideration in determining the appropriate level of total expenditure in a system. The principles by which opportunity costs can be reflected in analysis are illustrated in this article by using the example of the proposed methods for value-based pricing in the United Kingdom. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A Digital Mixed Methods Research Design: Integrating Multimodal Analysis with Data Mining and Information Visualization for Big Data Analytics

ERIC Educational Resources Information Center

O'Halloran, Kay L.; Tan, Sabine; Pham, Duc-Son; Bateman, John; Vande Moere, Andrew

2018-01-01

This article demonstrates how a digital environment offers new opportunities for transforming qualitative data into quantitative data in order to use data mining and information visualization for mixed methods research. The digital approach to mixed methods research is illustrated by a framework which combines qualitative methods of multimodal…

Watershed Planning within a Quantitative Scenario Analysis Framework.

PubMed

Merriam, Eric R; Petty, J Todd; Strager, Michael P

2016-07-24

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall short as a result of an inability to quantify and predict complex cumulative effects of current and future land use scenarios at relevant spatial scales. The goal of this manuscript is to provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework. Sites are first selected for inclusion within the watershed assessment by identifying sites that fall along independent gradients and combinations of known stressors. Field and laboratory techniques are then used to obtain data on the physical, chemical, and biological effects of multiple land use activities. Multiple linear regression analysis is then used to produce landscape-based cumulative effects models for predicting aquatic conditions. Lastly, methods for incorporating cumulative effects models within a scenario analysis framework for guiding management and regulatory decisions (e.g., permitting and mitigation) within actively developing watersheds are discussed and demonstrated for 2 sub-watersheds within the mountaintop mining region of central Appalachia. The watershed assessment and management approach provided herein enables resource managers to facilitate economic and development activity while protecting aquatic resources and producing opportunity for net ecological benefits through targeted remediation.

Spatial eigensolution analysis of energy-stable flux reconstruction schemes and influence of the numerical flux on accuracy and robustness

NASA Astrophysics Data System (ADS)

Mengaldo, Gianmarco; De Grazia, Daniele; Moura, Rodrigo C.; Sherwin, Spencer J.

2018-04-01

This study focuses on the dispersion and diffusion characteristics of high-order energy-stable flux reconstruction (ESFR) schemes via the spatial eigensolution analysis framework proposed in [1]. The analysis is performed for five ESFR schemes, where the parameter 'c' dictating the properties of the specific scheme recovered is chosen such that it spans the entire class of ESFR methods, also referred to as VCJH schemes, proposed in [2]. In particular, we used five values of 'c', two that correspond to its lower and upper bounds and the others that identify three schemes that are linked to common high-order methods, namely the ESFR recovering two versions of discontinuous Galerkin methods and one recovering the spectral difference scheme. The performance of each scheme is assessed when using different numerical intercell fluxes (e.g. different levels of upwinding), ranging from "under-" to "over-upwinding". In contrast to the more common temporal analysis, the spatial eigensolution analysis framework adopted here allows one to grasp crucial insights into the diffusion and dispersion properties of FR schemes for problems involving non-periodic boundary conditions, typically found in open-flow problems, including turbulence, unsteady aerodynamics and aeroacoustics.

Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves

PubMed Central

Joshi, Shantanu H.; Klassen, Eric; Srivastava, Anuj; Jermyn, Ian

2011-01-01

This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas - elastic shape metric and path-straightening methods - for finding geodesics in shape spaces of curves. The elastic metric allows for optimal matching of features between curves while path-straightening ensures that the algorithm results in geodesic paths. This paper extends this framework by removing two important shape preserving transformations: rotations and re-parameterizations, by forming quotient spaces and constructing geodesics on these quotient spaces. These ideas are demonstrated using experiments involving 2D and 3D curves. PMID:21738385

Comparison of safety effect estimates obtained from empirical Bayes before-after study, propensity scores-potential outcomes framework, and regression model with cross-sectional data.

PubMed

Wood, Jonathan S; Donnell, Eric T; Porter, Richard J

2015-02-01

A variety of different study designs and analysis methods have been used to evaluate the performance of traffic safety countermeasures. The most common study designs and methods include observational before-after studies using the empirical Bayes method and cross-sectional studies using regression models. The propensity scores-potential outcomes framework has recently been proposed as an alternative traffic safety countermeasure evaluation method to address the challenges associated with selection biases that can be part of cross-sectional studies. Crash modification factors derived from the application of all three methods have not yet been compared. This paper compares the results of retrospective, observational evaluations of a traffic safety countermeasure using both before-after and cross-sectional study designs. The paper describes the strengths and limitations of each method, focusing primarily on how each addresses site selection bias, which is a common issue in observational safety studies. The Safety Edge paving technique, which seeks to mitigate crashes related to roadway departure events, is the countermeasure used in the present study to compare the alternative evaluation methods. The results indicated that all three methods yielded results that were consistent with each other and with previous research. The empirical Bayes results had the smallest standard errors. It is concluded that the propensity scores with potential outcomes framework is a viable alternative analysis method to the empirical Bayes before-after study. It should be considered whenever a before-after study is not possible or practical. Copyright © 2014 Elsevier Ltd. All rights reserved.

Probabilistic framework for product design optimization and risk management

NASA Astrophysics Data System (ADS)

Keski-Rahkonen, J. K.

2018-05-01

Probabilistic methods have gradually gained ground within engineering practices but currently it is still the industry standard to use deterministic safety margin approaches to dimensioning components and qualitative methods to manage product risks. These methods are suitable for baseline design work but quantitative risk management and product reliability optimization require more advanced predictive approaches. Ample research has been published on how to predict failure probabilities for mechanical components and furthermore to optimize reliability through life cycle cost analysis. This paper reviews the literature for existing methods and tries to harness their best features and simplify the process to be applicable in practical engineering work. Recommended process applies Monte Carlo method on top of load-resistance models to estimate failure probabilities. Furthermore, it adds on existing literature by introducing a practical framework to use probabilistic models in quantitative risk management and product life cycle costs optimization. The main focus is on mechanical failure modes due to the well-developed methods used to predict these types of failures. However, the same framework can be applied on any type of failure mode as long as predictive models can be developed.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Complementing Gender Analysis Methods.

PubMed

Kumar, Anant

2016-01-01

The existing gender analysis frameworks start with a premise that men and women are equal and should be treated equally. These frameworks give emphasis on equal distribution of resources between men and women and believe that this will bring equality which is not always true. Despite equal distribution of resources, women tend to suffer and experience discrimination in many areas of their lives such as the power to control resources within social relationships, and the need for emotional security and reproductive rights within interpersonal relationships. These frameworks believe that patriarchy as an institution plays an important role in women's oppression, exploitation, and it is a barrier in their empowerment and rights. Thus, some think that by ensuring equal distribution of resources and empowering women economically, institutions like patriarchy can be challenged. These frameworks are based on proposed equality principle which puts men and women in competing roles. Thus, the real equality will never be achieved. Contrary to the existing gender analysis frameworks, the Complementing Gender Analysis framework proposed by the author provides a new approach toward gender analysis which not only recognizes the role of economic empowerment and equal distribution of resources but suggests to incorporate the concept and role of social capital, equity, and doing gender in gender analysis which is based on perceived equity principle, putting men and women in complementing roles that may lead to equality. In this article the author reviews the mainstream gender theories in development from the viewpoint of the complementary roles of gender. This alternative view is argued based on existing literature and an anecdote of observations made by the author. While criticizing the equality theory, the author offers equity theory in resolving the gender conflict by using the concept of social and psychological capital.

Resource allocation and purchasing arrangements to improve accessibility of medicines: Evidence from Iran.

PubMed

Bastani, Peivand; Mehralian, Gholamhossein; Dinarvand, Rasoul

2015-01-01

The aim of this study was to review the current methods of pharmaceutical purchasing by Iranian insurance organizations within the World Bank conceptual framework model so as to provide applicable pharmaceutical resource allocation and purchasing (RAP) arrangements in Iran. This qualitative study was conducted through a qualitative document analysis (QDA), applying the four-step Scott method in document selection, and conducting 20 semi-structured interviews using a triangulation method. Furthermore, the data were analyzed applying five steps framework analysis using Atlas-ti software. The QDA showed that the purchasers face many structural, financing, payment, delivery and service procurement and purchasing challenges. Moreover, the findings of interviews are provided in three sections including demand-side, supply-side and price and incentive regime. Localizing RAP arrangements as a World Bank Framework in a developing country like Iran considers the following as the prerequisite for implementing strategic purchasing in pharmaceutical sector: The improvement of accessibility, subsidiary mechanisms, reimbursement of new drugs, rational use, uniform pharmacopeia, best supplier selection, reduction of induced demand and moral hazard, payment reform. It is obvious that for Iran, these customized aspects are more various and detailed than those proposed in a World Bank model for developing countries.

A Statistical Framework for the Functional Analysis of Metagenomes

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

Sharon, Itai; Pati, Amrita; Markowitz, Victor

2008-10-01

Metagenomic studies consider the genetic makeup of microbial communities as a whole, rather than their individual member organisms. The functional and metabolic potential of microbial communities can be analyzed by comparing the relative abundance of gene families in their collective genomic sequences (metagenome) under different conditions. Such comparisons require accurate estimation of gene family frequencies. They present a statistical framework for assessing these frequencies based on the Lander-Waterman theory developed originally for Whole Genome Shotgun (WGS) sequencing projects. They also provide a novel method for assessing the reliability of the estimations which can be used for removing seemingly unreliable measurements.more » They tested their method on a wide range of datasets, including simulated genomes and real WGS data from sequencing projects of whole genomes. Results suggest that their framework corrects inherent biases in accepted methods and provides a good approximation to the true statistics of gene families in WGS projects.« less

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

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

Hartmann, Anja, E-mail: hartmann@ipk-gatersleben.de; Schreiber, Falk; Martin-Luther-University Halle-Wittenberg, Halle

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the contextmore » of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM{sup 2} – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.« less

Web-based Visual Analytics for Extreme Scale Climate Science

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

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course

ERIC Educational Resources Information Center

Klebba, Joanne M.; Hamilton, Janet G.

2007-01-01

Structured case analysis is a hybrid pedagogy that flexibly combines diverse instructional methods with comprehensive case analysis as a mechanism to develop critical thinking skills. An incremental learning framework is proposed that allows instructors to develop and monitor content-specific theory and the corresponding critical thinking skills.…

Reflections on Practical Approaches to Involving Children and Young People in the Data Analysis Process

ERIC Educational Resources Information Center

Coad, Jane; Evans, Ruth

2008-01-01

This article reflects on key methodological issues emerging from children and young people's involvement in data analysis processes. We outline a pragmatic framework illustrating different approaches to engaging children, using two case studies of children's experiences of participating in data analysis. The article highlights methods of…

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to "variogram analysis," that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

Partial volume correction and image analysis methods for intersubject comparison of FDG-PET studies

NASA Astrophysics Data System (ADS)

Yang, Jun

2000-12-01

Partial volume effect is an artifact mainly due to the limited imaging sensor resolution. It creates bias in the measured activity in small structures and around tissue boundaries. In brain FDG-PET studies, especially for Alzheimer's disease study where there is serious gray matter atrophy, accurate estimate of cerebral metabolic rate of glucose is even more problematic due to large amount of partial volume effect. In this dissertation, we developed a framework enabling inter-subject comparison of partial volume corrected brain FDG-PET studies. The framework is composed of the following image processing steps: (1)MRI segmentation, (2)MR-PET registration, (3)MR based PVE correction, (4)MR 3D inter-subject elastic mapping. Through simulation studies, we showed that the newly developed partial volume correction methods, either pixel based or ROI based, performed better than previous methods. By applying this framework to a real Alzheimer's disease study, we demonstrated that the partial volume corrected glucose rates vary significantly among the control, at risk and disease patient groups and this framework is a promising tool useful for assisting early identification of Alzheimer's patients.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

An information transfer based novel framework for fault root cause tracing of complex electromechanical systems in the processing industry

NASA Astrophysics Data System (ADS)

Wang, Rongxi; Gao, Xu; Gao, Jianmin; Gao, Zhiyong; Kang, Jiani

2018-02-01

As one of the most important approaches for analyzing the mechanism of fault pervasion, fault root cause tracing is a powerful and useful tool for detecting the fundamental causes of faults so as to prevent any further propagation and amplification. Focused on the problems arising from the lack of systematic and comprehensive integration, an information transfer-based novel data-driven framework for fault root cause tracing of complex electromechanical systems in the processing industry was proposed, taking into consideration the experience and qualitative analysis of conventional fault root cause tracing methods. Firstly, an improved symbolic transfer entropy method was presented to construct a directed-weighted information model for a specific complex electromechanical system based on the information flow. Secondly, considering the feedback mechanisms in the complex electromechanical systems, a method for determining the threshold values of weights was developed to explore the disciplines of fault propagation. Lastly, an iterative method was introduced to identify the fault development process. The fault root cause was traced by analyzing the changes in information transfer between the nodes along with the fault propagation pathway. An actual fault root cause tracing application of a complex electromechanical system is used to verify the effectiveness of the proposed framework. A unique fault root cause is obtained regardless of the choice of the initial variable. Thus, the proposed framework can be flexibly and effectively used in fault root cause tracing for complex electromechanical systems in the processing industry, and formulate the foundation of system vulnerability analysis and condition prediction, as well as other engineering applications.

Understanding Design Tradeoffs for Health Technologies: A Mixed-Methods Approach

PubMed Central

O’Leary, Katie; Eschler, Jordan; Kendall, Logan; Vizer, Lisa M.; Ralston, James D.; Pratt, Wanda

2017-01-01

We introduce a mixed-methods approach for determining how people weigh tradeoffs in values related to health and technologies for health self-management. Our approach combines interviews with Q-methodology, a method from psychology uniquely suited to quantifying opinions. We derive the framework for structured data collection and analysis for the Q-methodology from theories of self-management of chronic illness and technology adoption. To illustrate the power of this new approach, we used it in a field study of nine older adults with type 2 diabetes, and nine mothers of children with asthma. Our mixed-methods approach provides three key advantages for health design science in HCI: (1) it provides a structured health sciences theoretical framework to guide data collection and analysis; (2) it enhances the coding of unstructured data with statistical patterns of polarizing and consensus views; and (3) it empowers participants to actively weigh competing values that are most personally significant to them. PMID:28804794

Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Pan, Y.; Wu, J.; Huang, H.; Liu, J.

2012-08-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

A Framework of Working Across Disciplines in Early Design and R&D of Large Complex Engineered Systems

NASA Technical Reports Server (NTRS)

McGowan, Anna-Maria Rivas; Papalambros, Panos Y.; Baker, Wayne E.

2015-01-01

This paper examines four primary methods of working across disciplines during R&D and early design of large-scale complex engineered systems such as aerospace systems. A conceptualized framework, called the Combining System Elements framework, is presented to delineate several aspects of cross-discipline and system integration practice. The framework is derived from a theoretical and empirical analysis of current work practices in actual operational settings and is informed by theories from organization science and engineering. The explanatory framework may be used by teams to clarify assumptions and associated work practices, which may reduce ambiguity in understanding diverse approaches to early systems research, development and design. The framework also highlights that very different engineering results may be obtained depending on work practices, even when the goals for the engineered system are the same.

Using Decision Analysis to Improve Malaria Control Policy Making

PubMed Central

Kramer, Randall; Dickinson, Katherine L.; Anderson, Richard M.; Fowler, Vance G.; Miranda, Marie Lynn; Mutero, Clifford M.; Saterson, Kathryn A.; Wiener, Jonathan B.

2013-01-01

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets and artemesinin combination therapies for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases. PMID:19356821

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Data Analysis with Graphical Models: Software Tools

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1994-01-01

Probabilistic graphical models (directed and undirected Markov fields, and combined in chain graphs) are used widely in expert systems, image processing and other areas as a framework for representing and reasoning with probabilities. They come with corresponding algorithms for performing probabilistic inference. This paper discusses an extension to these models by Spiegelhalter and Gilks, plates, used to graphically model the notion of a sample. This offers a graphical specification language for representing data analysis problems. When combined with general methods for statistical inference, this also offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper outlines the framework and then presents some basic tools for the task: a graphical version of the Pitman-Koopman Theorem for the exponential family, problem decomposition, and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Quantified choice of root-mean-square errors of approximation for evaluation and power analysis of small differences between structural equation models.

PubMed

Li, Libo; Bentler, Peter M

2011-06-01

MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of approximation (RMSEA) pairs. In this article, we develop a new method that quantifies those chosen RMSEA pairs and allows a quantitative comparison of them. Our method proposes the use of single RMSEA values to replace the choice of RMSEA pairs for model comparison and power analysis, thus avoiding the differential meaning of the chosen RMSEA pairs inherent in the approach of MacCallum et al. (2006). With this choice, the conventional cutoff values in model overall evaluation can directly be transferred and applied to the evaluation and power analysis of model differences. © 2011 American Psychological Association

A deliberative framework to identify the need for real-life evidence building of new cancer drugs after interim funding decision.

PubMed

Leung, Leanne; de Lemos, Mário L; Kovacic, Laurel

2017-01-01

Background With the rising cost of new oncology treatments, it is no longer sustainable to base initial drug funding decisions primarily on prospective clinical trials as their performance in real-life populations are often difficult to determine. In British Columbia, an approach in evidence building is to retrospectively analyse patient outcomes using observational research on an ad hoc basis. Methods The deliberative framework was constructed in three stages: framework design, framework validation and treatment programme characterization, and key informant interview. Framework design was informed through a literature review and analyses of provincial and national decision-making processes. Treatment programmes funded between 2010 and 2013 were used for framework validation. A selection concordance rate of 80% amongst three reviewers was considered to be a validation of the framework. Key informant interviews were conducted to determine the utility of this deliberative framework. Results A multi-domain deliberative framework with 15 assessment parameters was developed. A selection concordance rate of 84.2% was achieved for content validation of the framework. Nine treatment programmes from five different tumour groups were selected for retrospective outcomes analysis. Five contributory factors to funding uncertainties were identified. Key informants agreed that the framework is a comprehensive tool that targets the key areas involved in the funding decision-making process. Conclusions The oncology-based deliberative framework can be routinely used to assess treatment programmes from the major tumour sites for retrospective outcomes analysis. Key informants indicate this is a value-added tool and will provide insight to the current prospective funding model.

Digital versus conventional techniques for pattern fabrication of implant-supported frameworks

PubMed Central

Alikhasi, Marzieh; Rohanian, Ahmad; Ghodsi, Safoura; Kolde, Amin Mohammadpour

2018-01-01

Objective: The aim of this experimental study was to compare retention of frameworks cast from wax patterns fabricated by three different methods. Materials and Methods: Thirty-six implant analogs connected to one-piece abutments were divided randomly into three groups according to the wax pattern fabrication method (n = 12). Computer-aided design/computer-aided manufacturing (CAD/CAM) milling machine, three-dimensional printer, and conventional technique were used for fabrication of waxing patterns. All laboratory procedures were performed by an expert-reliable technician to eliminate intra-operator bias. The wax patterns were cast, finished, and seated on related abutment analogs. The number of adjustment times was recorded and analyzed by Kruskal–Wallis test. Frameworks were cemented on the corresponding analogs with zinc phosphate cement and tensile resistance test was used to measure retention value. Statistical Analysis Used: One-way analysis of variance (ANOVA) and post hoc Tukey tests were used for statistical analysis. Level of significance was set at P < 0.05. Results: The mean retentive values of 680.36 ± 21.93 N, 440.48 ± 85.98 N, and 407.23 ± 67.48 N were recorded for CAD/CAM, rapid prototyping, and conventional group, respectively. One-way ANOVA test revealed significant differences among the three groups (P < 0.001). The post hoc Tukey test showed significantly higher retention for CAD/CAM group (P < 0.001), while there was no significant difference between the two other groups (P = 0.54). CAD/CAM group required significantly more adjustments (P < 0.001). Conclusions: CAD/CAM-fabricated wax patterns showed significantly higher retention for implant-supported cement-retained frameworks; this could be a valuable help when there are limitations in the retention of single-unit implant restorations. PMID:29657528

Python package for model STructure ANalysis (pySTAN)

NASA Astrophysics Data System (ADS)

Van Hoey, Stijn; van der Kwast, Johannes; Nopens, Ingmar; Seuntjens, Piet

2013-04-01

The selection and identification of a suitable hydrological model structure is more than fitting parameters of a model structure to reproduce a measured hydrograph. The procedure is highly dependent on various criteria, i.e. the modelling objective, the characteristics and the scale of the system under investigation as well as the available data. Rigorous analysis of the candidate model structures is needed to support and objectify the selection of the most appropriate structure for a specific case (or eventually justify the use of a proposed ensemble of structures). This holds both in the situation of choosing between a limited set of different structures as well as in the framework of flexible model structures with interchangeable components. Many different methods to evaluate and analyse model structures exist. This leads to a sprawl of available methods, all characterized by different assumptions, changing conditions of application and various code implementations. Methods typically focus on optimization, sensitivity analysis or uncertainty analysis, with backgrounds from optimization, machine-learning or statistics amongst others. These methods also need an evaluation metric (objective function) to compare the model outcome with some observed data. However, for current methods described in literature, implementations are not always transparent and reproducible (if available at all). No standard procedures exist to share code and the popularity (and amount of applications) of the methods is sometimes more dependent on the availability than the merits of the method. Moreover, new implementations of existing methods are difficult to verify and the different theoretical backgrounds make it difficult for environmental scientists to decide about the usefulness of a specific method. A common and open framework with a large set of methods can support users in deciding about the most appropriate method. Hence, it enables to simultaneously apply and compare different methods on a fair basis. We developed and present pySTAN (python framework for STructure Analysis), a python package containing a set of functions for model structure evaluation to provide the analysis of (hydrological) model structures. A selected set of algorithms for optimization, uncertainty and sensitivity analysis is currently available, together with a set of evaluation (objective) functions and input distributions to sample from. The methods are implemented model-independent and the python language provides the wrapper functions to apply administer external model codes. Different objective functions can be considered simultaneously with both statistical metrics and more hydrology specific metrics. By using so-called reStructuredText (sphinx documentation generator) and Python documentation strings (docstrings), the generation of manual pages is semi-automated and a specific environment is available to enhance both the readability and transparency of the code. It thereby enables a larger group of users to apply and compare these methods and to extend the functionalities.

Hybrid Intrusion Forecasting Framework for Early Warning System

NASA Astrophysics Data System (ADS)

Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

Preservice elementary teachers' use of a discursive model of meaning making in the co-construction of science understanding

NASA Astrophysics Data System (ADS)

Boyer, Elisebeth C.

This research investigates how three preservice elementary teachers were prepared to teach science using a Discursive Model of Meaning Making. The research is divided into two parts. The first consists of the nature of the participants’ learning experiences in a science methods course within a school-university Professional Development School partnership. This part of the investigation used Constant Comparative Analysis of field notes gathered through participant observation of the methods course. The analysis investigated how the methods instructors employed productive questioning, talk moves, and a coherent research based Teaching Science as Argument Framework. The second part of the study consisted of an investigation into how the participants applied what they experienced during the methods course in their initial science teaching experiences, as well as how the participants made sense of their initial science teaching. Data consisted of teaching videos of the participants during their initial science teaching experiences and self-analysis videos created by the participants. This part of the research used Discourse Analysis of the teaching and self-analysis videos. These inquiries provide insight into what aspects of the methods course were taken up by the participants and how they made sense of their practices. Findings are: 1) Throughout the methods course, instructors modeled how the Teaching Science as Argument Framework can be used to negotiate scientific understanding by employing a Discursive Model of Meaning Making. 2) During lesson plan conferences the Discursive Model was emphasized as participants planned classroom discussion and explored possible student responses enabling them to anticipate how they could attempt to increase student understanding. 3) Participants displayed three distinct patterns of adoption of the Teaching Science as Argument Framework (TSAF), involving different discursive practices. They were, • Detached Discursive Approach: Use of some discursive strategies without an apparent connection to the TSAF. • Connected Approach with a Focus on Student Thinking: Intentional use of the Discursive Model informed by aspects of the TSAF. • TSAF Approach: Priority is given to the TSAF supported by substantial application of the Discursive Model. 4) The evidence participants chose to highlight in their self-analysis videos is reflective of their patterns of adoption of the Teaching Science as Argument Framework and their differing discursive practices. Analysis led to the formation of the middle theory that when learning to teach science in the elementary school, teacher commitment to the discourse and practices of science is constructed through participation in a learning community where a discursive model of meaning making is the norm. Curricular and methodological implications, as well as implications for future research are presented.

A case study using the PrOACT-URL and BRAT frameworks for structured benefit risk assessment.

PubMed

Nixon, Richard; Dierig, Christoph; Mt-Isa, Shahrul; Stöckert, Isabelle; Tong, Thaison; Kuhls, Silvia; Hodgson, Gemma; Pears, John; Waddingham, Ed; Hockley, Kimberley; Thomson, Andrew

2016-01-01

While benefit-risk assessment is a key component of the drug development and maintenance process, it is often described in a narrative. In contrast, structured benefit-risk assessment builds on established ideas from decision analysis and comprises a qualitative framework and quantitative methodology. We compare two such frameworks, applying multi-criteria decision-analysis (MCDA) within the PrOACT-URL framework and weighted net clinical benefit (wNCB), within the BRAT framework. These are applied to a case study of natalizumab for the treatment of relapsing remitting multiple sclerosis. We focus on the practical considerations of applying these methods and give recommendations for visual presentation of results. In the case study, we found structured benefit-risk analysis to be a useful tool for structuring, quantifying, and communicating the relative benefit and safety profiles of drugs in a transparent, rational and consistent way. The two frameworks were similar. MCDA is a generic and flexible methodology that can be used to perform a structured benefit-risk in any common context. wNCB is a special case of MCDA and is shown to be equivalent to an extension of the number needed to treat (NNT) principle. It is simpler to apply and understand than MCDA and can be applied when all outcomes are measured on a binary scale. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Frequency analysis of uncertain structures using imprecise probability

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

Modares, Mehdi; Bergerson, Joshua

2015-01-01

Two new methods for finite element based frequency analysis of a structure with uncertainty are developed. An imprecise probability formulation based on enveloping p-boxes is used to quantify the uncertainty present in the mechanical characteristics of the structure. For each element, independent variations are considered. Using the two developed methods, P-box Frequency Analysis (PFA) and Interval Monte-Carlo Frequency Analysis (IMFA), sharp bounds on natural circular frequencies at different probability levels are obtained. These methods establish a framework for handling incomplete information in structural dynamics. Numerical example problems are presented that illustrate the capabilities of the new methods along with discussionsmore » on their computational efficiency.« less

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

A Framework for Assessment of Aviation Safety Technology Portfolios

NASA Technical Reports Server (NTRS)

Jones, Sharon M.; Reveley, Mary S.

2014-01-01

The programs within NASA's Aeronautics Research Mission Directorate (ARMD) conduct research and development to improve the national air transportation system so that Americans can travel as safely as possible. NASA aviation safety systems analysis personnel support various levels of ARMD management in their fulfillment of system analysis and technology prioritization as defined in the agency's program and project requirements. This paper provides a framework for the assessment of aviation safety research and technology portfolios that includes metrics such as projected impact on current and future safety, technical development risk and implementation risk. The paper also contains methods for presenting portfolio analysis and aviation safety Bayesian Belief Network (BBN) output results to management using bubble charts and quantitative decision analysis techniques.

Imperial College near infrared spectroscopy neuroimaging analysis framework.

PubMed

Orihuela-Espina, Felipe; Leff, Daniel R; James, David R C; Darzi, Ara W; Yang, Guang-Zhong

2018-01-01

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.

A methodological framework to support the initiation, design and institutionalization of participatory modeling processes in water resources management

NASA Astrophysics Data System (ADS)

Halbe, Johannes; Pahl-Wostl, Claudia; Adamowski, Jan

2018-01-01

Multiple barriers constrain the widespread application of participatory methods in water management, including the more technical focus of most water agencies, additional cost and time requirements for stakeholder involvement, as well as institutional structures that impede collaborative management. This paper presents a stepwise methodological framework that addresses the challenges of context-sensitive initiation, design and institutionalization of participatory modeling processes. The methodological framework consists of five successive stages: (1) problem framing and stakeholder analysis, (2) process design, (3) individual modeling, (4) group model building, and (5) institutionalized participatory modeling. The Management and Transition Framework is used for problem diagnosis (Stage One), context-sensitive process design (Stage Two) and analysis of requirements for the institutionalization of participatory water management (Stage Five). Conceptual modeling is used to initiate participatory modeling processes (Stage Three) and ensure a high compatibility with quantitative modeling approaches (Stage Four). This paper describes the proposed participatory model building (PMB) framework and provides a case study of its application in Québec, Canada. The results of the Québec study demonstrate the applicability of the PMB framework for initiating and designing participatory model building processes and analyzing barriers towards institutionalization.

A Bayesian framework to estimate diversification rates and their variation through time and space

PubMed Central

2011-01-01

Background Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification. Results We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinidae) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification. Conclusions Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling. PMID:22013891

College-Based Personal Finance Education: Student Interest in Three Delivery Methods

ERIC Educational Resources Information Center

Goetz, Joseph; Cude, Brenda J.; Nielsen, Robert B.; Chatterjee, Swarn; Mimura, Yoko

2011-01-01

Using online survey responses from 509 undergraduate students, three financial education methods (on-campus financial counseling center, online financial management resources, and in-person educational workshops) were examined. Using a social constructionist framework, the analysis controlled for various demographic and financial factors. The…

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

PubMed

Sun, Xinglong; Xu, Tingfa; Zhang, Jizhou; Zhao, Zishu; Li, Yuankun

2017-07-26

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets.

Validation of the theoretical domains framework for use in behaviour change and implementation research

PubMed Central

2012-01-01

Background An integrative theoretical framework, developed for cross-disciplinary implementation and other behaviour change research, has been applied across a wide range of clinical situations. This study tests the validity of this framework. Methods Validity was investigated by behavioural experts sorting 112 unique theoretical constructs using closed and open sort tasks. The extent of replication was tested by Discriminant Content Validation and Fuzzy Cluster Analysis. Results There was good support for a refinement of the framework comprising 14 domains of theoretical constructs (average silhouette value 0.29): ‘Knowledge’, ‘Skills’, ‘Social/Professional Role and Identity’, ‘Beliefs about Capabilities’, ‘Optimism’, ‘Beliefs about Consequences’, ‘Reinforcement’, ‘Intentions’, ‘Goals’, ‘Memory, Attention and Decision Processes’, ‘Environmental Context and Resources’, ‘Social Influences’, ‘Emotions’, and ‘Behavioural Regulation’. Conclusions The refined Theoretical Domains Framework has a strengthened empirical base and provides a method for theoretically assessing implementation problems, as well as professional and other health-related behaviours as a basis for intervention development. PMID:22530986

CSM Testbed Development and Large-Scale Structural Applications

NASA Technical Reports Server (NTRS)

Knight, Norman F., Jr.; Gillian, R. E.; Mccleary, Susan L.; Lotts, C. G.; Poole, E. L.; Overman, A. L.; Macy, S. C.

1989-01-01

A research activity called Computational Structural Mechanics (CSM) conducted at the NASA Langley Research Center is described. This activity is developing advanced structural analysis and computational methods that exploit high-performance computers. Methods are developed in the framework of the CSM Testbed software system and applied to representative complex structural analysis problems from the aerospace industry. An overview of the CSM Testbed methods development environment is presented and some new numerical methods developed on a CRAY-2 are described. Selected application studies performed on the NAS CRAY-2 are also summarized.

Precision Fit of Screw-Retained Implant-Supported Fixed Dental Prostheses Fabricated by CAD/CAM, Copy-Milling, and Conventional Methods.

PubMed

de França, Danilo Gonzaga; Morais, Maria Helena; das Neves, Flávio D; Carreiro, Adriana Fonte; Barbosa, Gustavo As

The aim of this study was to evaluate the effectiveness of fabrication methods (computer-aided design/computer-aided manufacture [CAD/CAM], copy-milling, and conventional casting) in the fit accuracy of three-unit, screw-retained fixed dental prostheses. Sixteen three-unit implant-supported screw-retained frameworks were fabricated to fit an in vitro model. Eight frameworks were fabricated using the CAD/CAM system, four in zirconia and four in cobalt-chromium. Four zirconia frameworks were fabricated using the copy-milled system, and four were cast in cobalt-chromium using conventional casting with premachined abutments. The vertical and horizontal misfit at the implant-framework interface was measured using scanning electron microscopy at ×250. The results for vertical misfit were analyzed using Kruskal-Wallis and Mann-Whitney tests. The horizontal misfits were categorized as underextended, equally extended, or overextended. Statistical analysis established differences between groups according to the chi-square test (α = .05). The mean vertical misfit was 5.9 ± 3.6 μm for CAD/CAM-fabricated zirconia, 1.2 ± 2.2 μm for CAD/CAM-fabricated cobalt-chromium frameworks, 7.6 ± 9.2 μm for copy-milling-fabricated zirconia frameworks, and 11.8 (9.8) μm for conventionally fabricated frameworks. The Mann-Whitney test revealed significant differences between all but the zirconia-fabricated frameworks. A significant association was observed between the horizontal misfits and the fabrication method. The percentage of horizontal misfits that were underextended and overextended was higher in milled zirconia (83.3%), CAD/CAM cobaltchromium (66.7%), cast cobalt-chromium (58.3%), and CAD/CAM zirconia (33.3%) frameworks. CAD/CAM-fabricated frameworks exhibit better vertical misfit and low variability compared with copy-milled and conventionally fabricated frameworks. The percentage of interfaces equally extended was higher when CAD/CAM and zirconia were used.

Mapping forest inventory and analysis data attributes within the framework of double sampling for stratification design

Treesearch

David C. Chojnacky; Randolph H. Wynne; Christine E. Blinn

2009-01-01

Methodology is lacking to easily map Forest Inventory and Analysis (FIA) inventory statistics for all attribute variables without having to develop separate models and methods for each variable. We developed a mapping method that can directly transfer tabular data to a map on which pixels can be added any way desired to estimate carbon (or any other variable) for a...

Auditory Scene Analysis: An Attention Perspective

ERIC Educational Resources Information Center

Sussman, Elyse S.

2017-01-01

Purpose: This review article provides a new perspective on the role of attention in auditory scene analysis. Method: A framework for understanding how attention interacts with stimulus-driven processes to facilitate task goals is presented. Previously reported data obtained through behavioral and electrophysiological measures in adults with normal…

Mean Comparison: Manifest Variable versus Latent Variable

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2006-01-01

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

The NIH analytical methods and reference materials program for dietary supplements.

PubMed

Betz, Joseph M; Fisher, Kenneth D; Saldanha, Leila G; Coates, Paul M

2007-09-01

Quality of botanical products is a great uncertainty that consumers, clinicians, regulators, and researchers face. Definitions of quality abound, and include specifications for sanitation, adventitious agents (pesticides, metals, weeds), and content of natural chemicals. Because dietary supplements (DS) are often complex mixtures, they pose analytical challenges and method validation may be difficult. In response to product quality concerns and the need for validated and publicly available methods for DS analysis, the US Congress directed the Office of Dietary Supplements (ODS) at the National Institutes of Health (NIH) to accelerate an ongoing methods validation process, and the Dietary Supplements Methods and Reference Materials Program was created. The program was constructed from stakeholder input and incorporates several federal procurement and granting mechanisms in a coordinated and interlocking framework. The framework facilitates validation of analytical methods, analytical standards, and reference materials.

Multisensor satellite data for water quality analysis and water pollution risk assessment: decision making under deep uncertainty with fuzzy algorithm in framework of multimodel approach

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Sztoyka, Yulia; Kopachevsky, Ivan; Artemenko, Igor; Yuschenko, Maxim

2017-10-01

Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS, MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and discussed.

A computational framework for prime implicants identification in noncoherent dynamic systems.

PubMed

Di Maio, Francesco; Baronchelli, Samuele; Zio, Enrico

2015-01-01

Dynamic reliability methods aim at complementing the capability of traditional static approaches (e.g., event trees [ETs] and fault trees [FTs]) by accounting for the system dynamic behavior and its interactions with the system state transition process. For this, the system dynamics is here described by a time-dependent model that includes the dependencies with the stochastic transition events. In this article, we present a novel computational framework for dynamic reliability analysis whose objectives are i) accounting for discrete stochastic transition events and ii) identifying the prime implicants (PIs) of the dynamic system. The framework entails adopting a multiple-valued logic (MVL) to consider stochastic transitions at discretized times. Then, PIs are originally identified by a differential evolution (DE) algorithm that looks for the optimal MVL solution of a covering problem formulated for MVL accident scenarios. For testing the feasibility of the framework, a dynamic noncoherent system composed of five components that can fail at discretized times has been analyzed, showing the applicability of the framework to practical cases. © 2014 Society for Risk Analysis.

African Primary Care Research: Qualitative data analysis and writing results

PubMed Central

Govender, Indiran; Ogunbanjo, Gboyega A.; Mash, Bob

2014-01-01

Abstract This article is part of a series on African primary care research and gives practical guidance on qualitative data analysis and the presentation of qualitative findings. After an overview of qualitative methods and analytical approaches, the article focuses particularly on content analysis, using the framework method as an example. The steps of familiarisation, creating a thematic index, indexing, charting, interpretation and confirmation are described. Key concepts with regard to establishing the quality and trustworthiness of data analysis are described. Finally, an approach to the presentation of qualitative findings is given. PMID:26245437

African Primary Care Research: qualitative data analysis and writing results.

PubMed

Mabuza, Langalibalele H; Govender, Indiran; Ogunbanjo, Gboyega A; Mash, Bob

2014-06-05

This article is part of a series on African primary care research and gives practical guidance on qualitative data analysis and the presentation of qualitative findings. After an overview of qualitative methods and analytical approaches, the article focuses particularly on content analysis, using the framework method as an example. The steps of familiarisation, creating a thematic index, indexing, charting, interpretation and confirmation are described. Key concepts with regard to establishing the quality and trustworthiness of data analysis are described. Finally, an approach to the presentation of qualitative findings is given.

Bridging Human Reliability Analysis and Psychology, Part 2: A Cognitive Framework to Support HRA

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

April M. Whaley; Stacey M. L. Hendrickson; Ronald L. Boring

This is the second of two papers that discuss the literature review conducted as part of the U.S. Nuclear Regulatory Commission (NRC) effort to develop a hybrid human reliability analysis (HRA) method in response to Staff Requirements Memorandum (SRM) SRM-M061020. This review was conducted with the goal of strengthening the technical basis within psychology, cognitive science and human factors for the hybrid HRA method being proposed. An overview of the literature review approach and high-level structure is provided in the first paper, whereas this paper presents the results of the review. The psychological literature review encompassed research spanning the entiretymore » of human cognition and performance, and consequently produced an extensive list of psychological processes, mechanisms, and factors that contribute to human performance. To make sense of this large amount of information, the results of the literature review were organized into a cognitive framework that identifies causes of failure of macrocognition in humans, and connects those proximate causes to psychological mechanisms and performance influencing factors (PIFs) that can lead to the failure. This cognitive framework can serve as a tool to inform HRA. Beyond this, however, the cognitive framework has the potential to also support addressing human performance issues identified in Human Factors applications.« less

A proposed analytic framework for determining the impact of an antimicrobial resistance intervention.

PubMed

Grohn, Yrjo T; Carson, Carolee; Lanzas, Cristina; Pullum, Laura; Stanhope, Michael; Volkova, Victoriya

2017-06-01

Antimicrobial use (AMU) is increasingly threatened by antimicrobial resistance (AMR). The FDA is implementing risk mitigation measures promoting prudent AMU in food animals. Their evaluation is crucial: the AMU/AMR relationship is complex; a suitable framework to analyze interventions is unavailable. Systems science analysis, depicting variables and their associations, would help integrate mathematics/epidemiology to evaluate the relationship. This would identify informative data and models to evaluate interventions. This National Institute for Mathematical and Biological Synthesis AMR Working Group's report proposes a system framework to address the methodological gap linking livestock AMU and AMR in foodborne bacteria. It could evaluate how AMU (and interventions) impact AMR. We will evaluate pharmacokinetic/dynamic modeling techniques for projecting AMR selection pressure on enteric bacteria. We study two methods to model phenotypic AMR changes in bacteria in the food supply and evolutionary genotypic analyses determining molecular changes in phenotypic AMR. Systems science analysis integrates the methods, showing how resistance in the food supply is explained by AMU and concurrent factors influencing the whole system. This process is updated with data and techniques to improve prediction and inform improvements for AMU/AMR surveillance. Our proposed framework reflects both the AMR system's complexity, and desire for simple, reliable conclusions.

Nursing management of sensory overload in psychiatry – Theoretical densification and modification of the framework model

PubMed

Scheydt, Stefan; Needham, Ian; Behrens, Johann

2017-01-01

Background: Within the scope of the research project on the subjects of sensory overload and stimulus regulation, a theoretical framework model of the nursing care of patients with sensory overload in psychiatry was developed. In a second step, this theoretical model should now be theoretically compressed and, if necessary, modified. Aim: Empirical verification as well as modification, enhancement and theoretical densification of the framework model of nursing care of patients with sensory overload in psychiatry. Method: Analysis of 8 expert interviews by summarizing and structuring content analysis methods based on Meuser and Nagel (2009) as well as Mayring (2010). Results: The developed framework model (Scheydt et al., 2016b) could be empirically verified, theoretically densificated and extended by one category (perception modulation). Thus, four categories of nursing care of patients with sensory overload can be described in inpatient psychiatry: removal from stimuli, modulation of environmental factors, perceptual modulation as well as help somebody to help him- or herself / coping support. Conclusions: Based on the methodological approach, a relatively well-saturated, credible conceptualization of a theoretical model for the description of the nursing care of patients with sensory overload in stationary psychiatry could be worked out. In further steps, these measures have to be further developed, implemented and evaluated regarding to their efficacy.

Development of a theoretical framework for analyzing cerebrospinal fluid dynamics

PubMed Central

Cohen, Benjamin; Voorhees, Abram; Vedel, Søren; Wei, Timothy

2009-01-01

Background To date hydrocephalus researchers acknowledge the need for rigorous but utilitarian fluid mechanics understanding and methodologies in studying normal and hydrocephalic intracranial dynamics. Pressure volume models and electric circuit analogs introduced pressure into volume conservation; but control volume analysis enforces independent conditions on pressure and volume. Previously, utilization of clinical measurements has been limited to understanding of the relative amplitude and timing of flow, volume and pressure waveforms; qualitative approaches without a clear framework for meaningful quantitative comparison. Methods Control volume analysis is presented to introduce the reader to the theoretical background of this foundational fluid mechanics technique for application to general control volumes. This approach is able to directly incorporate the diverse measurements obtained by clinicians to better elucidate intracranial dynamics and progression to disorder. Results Several examples of meaningful intracranial control volumes and the particular measurement sets needed for the analysis are discussed. Conclusion Control volume analysis provides a framework to guide the type and location of measurements and also a way to interpret the resulting data within a fundamental fluid physics analysis. PMID:19772652

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

A method for studying decision-making by guideline development groups.

PubMed

Gardner, Benjamin; Davidson, Rosemary; McAteer, John; Michie, Susan

2009-08-05

Multidisciplinary guideline development groups (GDGs) have considerable influence on UK healthcare policy and practice, but previous research suggests that research evidence is a variable influence on GDG recommendations. The Evidence into Recommendations (EiR) study has been set up to document social-psychological influences on GDG decision-making. In this paper we aim to evaluate the relevance of existing qualitative methodologies to the EiR study, and to develop a method best-suited to capturing influences on GDG decision-making. A research team comprised of three postdoctoral research fellows and a multidisciplinary steering group assessed the utility of extant qualitative methodologies for coding verbatim GDG meeting transcripts and semi-structured interviews with GDG members. A unique configuration of techniques was developed to permit data reduction and analysis. Our method incorporates techniques from thematic analysis, grounded theory analysis, content analysis, and framework analysis. Thematic analysis of individual interviews conducted with group members at the start and end of the GDG process defines discrete problem areas to guide data extraction from GDG meeting transcripts. Data excerpts are coded both inductively and deductively, using concepts taken from theories of decision-making, social influence and group processes. These codes inform a framework analysis to describe and explain incidents within GDG meetings. We illustrate the application of the method by discussing some preliminary findings of a study of a National Institute for Health and Clinical Excellence (NICE) acute physical health GDG. This method is currently being applied to study the meetings of three of NICE GDGs. These cover topics in acute physical health, mental health and public health, and comprise a total of 45 full-day meetings. The method offers potential for application to other health care and decision-making groups.

Modelling and analysis of the sugar cataract development process using stochastic hybrid systems.

PubMed

Riley, D; Koutsoukos, X; Riley, K

2009-05-01

Modelling and analysis of biochemical systems such as sugar cataract development (SCD) are critical because they can provide new insights into systems, which cannot be easily tested with experiments; however, they are challenging problems due to the highly coupled chemical reactions that are involved. The authors present a stochastic hybrid system (SHS) framework for modelling biochemical systems and demonstrate the approach for the SCD process. A novel feature of the framework is that it allows modelling the effect of drug treatment on the system dynamics. The authors validate the three sugar cataract models by comparing trajectories computed by two simulation algorithms. Further, the authors present a probabilistic verification method for computing the probability of sugar cataract formation for different chemical concentrations using safety and reachability analysis methods for SHSs. The verification method employs dynamic programming based on a discretisation of the state space and therefore suffers from the curse of dimensionality. To analyse the SCD process, a parallel dynamic programming implementation that can handle large, realistic systems was developed. Although scalability is a limiting factor, this work demonstrates that the proposed method is feasible for realistic biochemical systems.

Mimvec: a deep learning approach for analyzing the human phenome.

PubMed

Gan, Mingxin; Li, Wenran; Zeng, Wanwen; Wang, Xiaojian; Jiang, Rui

2017-09-21

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing. We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Exploring Advertising in Higher Education: An Empirical Analysis in North America, Europe, and Japan

ERIC Educational Resources Information Center

Papadimitriou, Antigoni; Blanco Ramírez, Gerardo

2015-01-01

This empirical study explores higher education advertising campaigns displayed in five world cities: Boston, New York, Oslo, Tokyo, and Toronto. The study follows a mixed-methods research design relying on content analysis and multimodal semiotic analysis and employs a conceptual framework based on the knowledge triangle of education, research,…

The design of an m-Health monitoring system based on a cloud computing platform

NASA Astrophysics Data System (ADS)

Xu, Boyi; Xu, Lida; Cai, Hongming; Jiang, Lihong; Luo, Yang; Gu, Yizhi

2017-01-01

Compared to traditional medical services provided within hospitals, m-Health monitoring systems (MHMSs) face more challenges in personalised health data processing. To achieve personalised and high-quality health monitoring by means of new technologies, such as mobile network and cloud computing, in this paper, a framework of an m-Health monitoring system based on a cloud computing platform (Cloud-MHMS) is designed to implement pervasive health monitoring. Furthermore, the modules of the framework, which are Cloud Storage and Multiple Tenants Access Control Layer, Healthcare Data Annotation Layer, and Healthcare Data Analysis Layer, are discussed. In the data storage layer, a multiple tenant access method is designed to protect patient privacy. In the data annotation layer, linked open data are adopted to augment health data interoperability semantically. In the data analysis layer, the process mining algorithm and similarity calculating method are implemented to support personalised treatment plan selection. These three modules cooperate to implement the core functions in the process of health monitoring, which are data storage, data processing, and data analysis. Finally, we study the application of our architecture in the monitoring of antimicrobial drug usage to demonstrate the usability of our method in personal healthcare analysis.

Structural Equation Models in a Redundancy Analysis Framework With Covariates.

PubMed

Lovaglio, Pietro Giorgio; Vittadini, Giorgio

2014-01-01

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

PubMed

Choi, Hyungwon; Kim, Sinae; Fermin, Damian; Tsou, Chih-Chiang; Nesvizhskii, Alexey I

2015-11-03

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Multiscale Failure Analysis of Laminated Composite Panels Subjected to Blast Loading Using FEAMAC/Explicit

NASA Technical Reports Server (NTRS)

Pineda, Evan J.; Waas, Anthony M.; Berdnarcyk, Brett A.; Arnold, Steven M.; Collier, Craig S.

2009-01-01

This preliminary report demonstrates the capabilities of the recently developed software implementation that links the Generalized Method of Cells to explicit finite element analysis by extending a previous development which tied the generalized method of cells to implicit finite elements. The multiscale framework, which uses explicit finite elements at the global-scale and the generalized method of cells at the microscale is detailed. This implementation is suitable for both dynamic mechanics problems and static problems exhibiting drastic and sudden changes in material properties, which often encounter convergence issues with commercial implicit solvers. Progressive failure analysis of stiffened and un-stiffened fiber-reinforced laminates subjected to normal blast pressure loads was performed and is used to demonstrate the capabilities of this framework. The focus of this report is to document the development of the software implementation; thus, no comparison between the results of the models and experimental data is drawn. However, the validity of the results are assessed qualitatively through the observation of failure paths, stress contours, and the distribution of system energies.

Realist theory construction for a mixed method multilevel study of neighbourhood context and postnatal depression.

PubMed

Eastwood, John G; Kemp, Lynn A; Jalaludin, Bin B

2016-01-01

We have recently described a protocol for a study that aims to build a theory of neighbourhood context and postnatal depression. That protocol proposed a critical realist Explanatory Theory Building Method comprising of an: (1) emergent phase, (2) construction phase, and (3) confirmatory phase. A concurrent triangulated mixed method multilevel cross-sectional study design was described. The protocol also described in detail the Theory Construction Phase which will be presented here. The Theory Construction Phase will include: (1) defining stratified levels; (2) analytic resolution; (3) abductive reasoning; (4) comparative analysis (triangulation); (5) retroduction; (6) postulate and proposition development; (7) comparison and assessment of theories; and (8) conceptual frameworks and model development. The stratified levels of analysis in this study were predominantly social and psychological. The abductive analysis used the theoretical frames of: Stress Process; Social Isolation; Social Exclusion; Social Services; Social Capital, Acculturation Theory and Global-economic level mechanisms. Realist propositions are presented for each analysis of triangulated data. Inference to best explanation is used to assess and compare theories. A conceptual framework of maternal depression, stress and context is presented that includes examples of mechanisms at psychological, social, cultural and global-economic levels. Stress was identified as a necessary mechanism that has the tendency to cause several outcomes including depression, anxiety, and health harming behaviours. The conceptual framework subsequently included conditional mechanisms identified through the retroduction including the stressors of isolation and expectations and buffers of social support and trust. The meta-theory of critical realism is used here to generate and construct social epidemiological theory using stratified ontology and both abductive and retroductive analysis. The findings will be applied to the development of a middle range theory and subsequent programme theory for local perinatal child and family interventions.

Computation of elementary modes: a unifying framework and the new binary approach

PubMed Central

Gagneur, Julien; Klamt, Steffen

2004-01-01

Background Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods. Results We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date. Conclusions The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks. PMID:15527509

Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds

PubMed Central

Hamad, Eradah O; Savundranayagam, Marie Y; Holmes, Jeffrey D; Kinsella, Elizabeth Anne

2016-01-01

Background Twitter’s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. Objective The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. Methods We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. Conclusions We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts. PMID:26957477

Examination of the regulatory frameworks applicable to biologic drugs (including stem cells and their progeny) in Europe, the U.S., and Australia: part I--a method of manual documentary analysis.

PubMed

Ilic, Nina; Savic, Snezana; Siegel, Evan; Atkinson, Kerry; Tasic, Ljiljana

2012-12-01

Recent development of a wide range of regulatory standards applicable to production and use of tissues, cells, and other biologics (or biologicals), as advanced therapies, indicates considerable interest in the regulation of these products. The objective of this study was to analyze and compare high-tier documents within the Australian, European, and U.S. biologic drug regulatory environments using qualitative methodology. Cohort 1 of the selected 18 high-tier regulatory documents from the European Medicines Agency (EMA), the U.S. Food and Drug Administration (FDA), and the Therapeutic Goods Administration (TGA) regulatory frameworks were subject to a manual documentary analysis. These documents were consistent with the legal requirements for manufacturing and use of biologic drugs in humans and fall into six different categories. Manual analysis included a terminology search. The occurrence, frequency, and interchangeable use of different terms and phrases were recorded in the manual documentary analysis. Despite obvious differences, manual documentary analysis revealed certain consistency in use of terminology across analyzed frameworks. Phrase search frequencies have shown less uniformity than the search of terms. Overall, the EMA framework's documents referred to "medicinal products" and "marketing authorization(s)," the FDA documents discussed "drug(s)" or "biologic(s)," and the TGA documents referred to "biological(s)." Although high-tier documents often use different terminology they share concepts and themes. Documents originating from the same source have more conjunction in their terminology although they belong to different frameworks (i.e., Good Clinical Practice requirements based on the Declaration of Helsinki, 1964). Automated (software-based) documentary analysis should be obtained for the conceptual and relational analysis.

Examination of the Regulatory Frameworks Applicable to Biologic Drugs (Including Stem Cells and Their Progeny) in Europe, the U.S., and Australia: Part I—A Method of Manual Documentary Analysis

PubMed Central

Savic, Snezana; Siegel, Evan; Atkinson, Kerry; Tasic, Ljiljana

2012-01-01

Recent development of a wide range of regulatory standards applicable to production and use of tissues, cells, and other biologics (or biologicals), as advanced therapies, indicates considerable interest in the regulation of these products. The objective of this study was to analyze and compare high-tier documents within the Australian, European, and U.S. biologic drug regulatory environments using qualitative methodology. Cohort 1 of the selected 18 high-tier regulatory documents from the European Medicines Agency (EMA), the U.S. Food and Drug Administration (FDA), and the Therapeutic Goods Administration (TGA) regulatory frameworks were subject to a manual documentary analysis. These documents were consistent with the legal requirements for manufacturing and use of biologic drugs in humans and fall into six different categories. Manual analysis included a terminology search. The occurrence, frequency, and interchangeable use of different terms and phrases were recorded in the manual documentary analysis. Despite obvious differences, manual documentary analysis revealed certain consistency in use of terminology across analyzed frameworks. Phrase search frequencies have shown less uniformity than the search of terms. Overall, the EMA framework's documents referred to “medicinal products” and “marketing authorization(s),” the FDA documents discussed “drug(s)” or “biologic(s),” and the TGA documents referred to “biological(s).” Although high-tier documents often use different terminology they share concepts and themes. Documents originating from the same source have more conjunction in their terminology although they belong to different frameworks (i.e., Good Clinical Practice requirements based on the Declaration of Helsinki, 1964). Automated (software-based) documentary analysis should be obtained for the conceptual and relational analysis. PMID:23283551

A Comparison of Approaches for Setting Proficiency Standards.

ERIC Educational Resources Information Center

Koffler, Stephen L.

This research compared the cut-off scores estimated from an empirical procedure (Contrasting group method) to those determined from a more theoretical process (Nedelsky method). A methodological and statistical framework was also provided for analysis of the data to obtain the most appropriate standard using the empirical procedure. Data were…

Reliability and Validity of the Research Methods Skills Assessment

ERIC Educational Resources Information Center

Smith, Tamarah; Smith, Samantha

2018-01-01

The Research Methods Skills Assessment (RMSA) was created to measure psychology majors' statistics knowledge and skills. The American Psychological Association's Guidelines for the Undergraduate Major in Psychology (APA, 2007, 2013) served as a framework for development. Results from a Rasch analysis with data from n = 330 undergraduates showed…

A Typology of FL Education with Particular Emphasis on Compensatory and Individualized Instruction.

ERIC Educational Resources Information Center

Jakobovits, Leon A.

A comparative analysis of foreign language teaching methods considered within the framework of the author's EBTA Cube (Eight Basic Approaches to Teaching) contrasts methods of instruction in three categories: (1) non-programed versus programed instruction, (2) mass versus individualized instruction, and (3) traditional versus compensatory…

Razalas' Grouping Method and Mathematics Achievement

ERIC Educational Resources Information Center

Salazar, Douglas A.

2015-01-01

This study aimed to raise the achievement level of students in Integral Calculus using Direct Instruction with Razalas' Method of Grouping. The study employed qualitative and quantitative analysis relative to data generated by the Achievement Test and Math journal with follow-up interview. Within the framework of the limitations of the study, the…

Identifying the domains of context important to implementation science: a study protocol.

PubMed

Squires, Janet E; Graham, Ian D; Hutchinson, Alison M; Michie, Susan; Francis, Jill J; Sales, Anne; Brehaut, Jamie; Curran, Janet; Ivers, Noah; Lavis, John; Linklater, Stefanie; Fenton, Shannon; Noseworthy, Thomas; Vine, Jocelyn; Grimshaw, Jeremy M

2015-09-28

There is growing recognition that "context" can and does modify the effects of implementation interventions aimed at increasing healthcare professionals' use of research evidence in clinical practice. However, conceptual clarity about what exactly comprises "context" is lacking. The purpose of this research program is to develop, refine, and validate a framework that identifies the key domains of context (and their features) that can facilitate or hinder (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. A multi-phased investigation of context using mixed methods will be conducted. The first phase is a concept analysis of context using the Walker and Avant method to distinguish between the defining and irrelevant attributes of context. This phase will result in a preliminary framework for context that identifies its important domains and their features according to the published literature. The second phase is a secondary analysis of qualitative data from 13 studies of interviews with 312 healthcare professionals on the perceived barriers and enablers to their application of research evidence in clinical practice. These data will be analyzed inductively using constant comparative analysis. For the third phase, we will conduct semi-structured interviews with key health system stakeholders and change agents to elicit their knowledge and beliefs about the contextual features that influence the effectiveness of implementation interventions and healthcare professionals' use of evidence in clinical practice. Results from all three phases will be synthesized using a triangulation protocol to refine the context framework drawn from the concept analysis. The framework will then be assessed for content validity using an iterative Delphi approach with international experts (researchers and health system stakeholders/change agents). This research program will result in a framework that identifies the domains of context and their features that can facilitate or hinder: (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. The framework will increase the conceptual clarity of the term "context" for advancing implementation science, improving healthcare professionals' use of evidence in clinical practice, and providing greater understanding of what interventions are likely to be effective in which contexts.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

Developing a framework to review near-miss maternal morbidity in India: a structured review and key stakeholder analysis.

PubMed

Bhattacharyya, Sanghita; Srivastava, Aradhana; Knight, Marian

2014-11-13

In India there is a thrust towards promoting institutional delivery, resulting in problems of overcrowding and compromise to quality of care. Review of near-miss obstetric events has been suggested to be useful to investigate health system functioning, complementing maternal death reviews. The aim of this project was to identify the key elements required for a near-miss review programme for India. A structured review was conducted to identify methods used in assessing near-miss cases. The findings of the structured review were used to develop a suggested framework for conducting near-miss reviews in India. A pool of experts in near-miss review methods in low and middle income countries (LMICs) was identified for vetting the framework developed. Opinions were sought about the feasibility of implementing near-miss reviews in India, the processes to be followed, factors that made implementation successful and the associated challenges. A draft of the framework was revised based on the experts' opinions. Five broad methods of near-miss case review/audit were identified: Facility-based near-miss case review, confidential enquiries, criterion-based clinical audit, structured case review (South African Model) and home-based interviews. The opinion of the 11 stakeholders highlighted that the methods that a facility adopts should depend on the type and number of cases the facility handles, availability and maintenance of a good documentation system, and local leadership and commitment of staff. A proposed framework for conducting near-miss reviews was developed that included a combination of criterion-based clinical audit and near-miss review methods. The approach allowed for development of a framework for researchers and planners seeking to improve quality of maternal care not only at the facility level but also beyond, encompassing community health workers and referral. Further work is needed to evaluate the implementation of this framework to determine its efficacy in improving the quality of care and hence maternal and perinatal morbidity and mortality.

Spatiotemporal Data Mining, Analysis, and Visualization of Human Activity Data

ERIC Educational Resources Information Center

Li, Xun

2012-01-01

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data…

Defining the Technology Transition Manager within the Acquisition Framework of the Department of Defense

DTIC Science & Technology

2010-03-01

80 Discussion ...methods used to construct the survey questionnaire and discuss the data analysis methodology. Chapter IV will present the analysis of the data...2 provides a snapshot of the similarities and differences across various studies on new product development. The subsequent paragraphs will discuss

The Educational Governance of German School Social Science: The Example of Globalization

ERIC Educational Resources Information Center

Szukala, Andrea

2016-01-01

Purpose: This article challenges the outsiders' views on European school social science adopting genuine cosmopolitan views, when globalisation is treated in social science classrooms. Method: The article is based on the theoretical framework of educational governance analysis and on qualitative corpus analysis of representative German Laenders'…

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

A framework for evaluating national space activity

NASA Astrophysics Data System (ADS)

Wood, Danielle; Weigel, Annalisa

2012-04-01

Space technology and resources are used around the world to address societal challenges. Space provides valuable satellite services, unique scientific discoveries, surprising technology applications and new economic opportunities. Many developing countries formally recognize the advantages of space resources and pursue national level activity to harness them. There is limited data or documentation on the space activities of developing countries. Meanwhile, traditional approaches to summarize national space activity do not necessarily capture the types of activity that developing countries pursue in space. This is especially true if they do not have a formal national space program or office. Developing countries pursue national space activity through activities of many types—from national satellite programs to commercial use of satellite services to involvement with international space institutions. This research aims to understand and analyze these trends. This paper introduces two analytical frameworks for evaluating space activity at the national level. The frameworks are specifically designed to capture the activity of countries that have traditionally been less involved in space. They take a broad view of space related activity across multiple societal sectors and disciplines. The discussion explains the approach for using the frameworks as well as illustrative examples of how they can be applied as part of a research process. The first framework is called the Mission and Management Ladders. This framework considers specific space projects within countries and ranks them on "Ladders" that measure technical challenge and managerial autonomy. This first method is at a micro level of analysis. The second framework is called the Space Participation Metric (SPM). The SPM can be used to assign a Space Participation score to countries based on their involvement in various space related activities. This second method uses a macro level of analysis. The authors developed both frameworks as part of a long term research program about the space activities of developing countries. This aspect of the research focuses on harnessing multiple techniques to summarize complex, multi-disciplinary information about global space activity.

Methods used to address fidelity of receipt in health intervention research: a citation analysis and systematic review.

PubMed

Rixon, Lorna; Baron, Justine; McGale, Nadine; Lorencatto, Fabiana; Francis, Jill; Davies, Anna

2016-11-18

The American Behaviour Change Consortium (BCC) framework acknowledges patients as active participants and supports the need to investigate the fidelity with which they receive interventions, i.e. receipt. According to this framework, addressing receipt consists in using strategies to assess or enhance participants' understanding and/or performance of intervention skills. This systematic review aims to establish the frequency with which receipt is addressed as defined in the BCC framework in health research, and to describe the methods used in papers informed by the BCC framework and in the wider literature. A forward citation search on papers presenting the BCC framework was performed to determine the frequency with which receipt as defined in this framework was addressed. A second electronic database search, including search terms pertaining to fidelity, receipt, health and process evaluations was performed to identify papers reporting on receipt in the wider literature and irrespective of the framework used. These results were combined with forward citation search results to review methods to assess receipt. Eligibility criteria and data extraction forms were developed and applied to papers. Results are described in a narrative synthesis. 19.6% of 33 studies identified from the forward citation search to report on fidelity were found to address receipt. In 60.6% of these, receipt was assessed in relation to understanding and in 42.4% in relation to performance of skill. Strategies to enhance these were present in 12.1% and 21.1% of studies, respectively. Fifty-five studies were included in the review of the wider literature. Several frameworks and operationalisations of receipt were reported, but the latter were not always consistent with the guiding framework. Receipt was most frequently operationalised in relation to intervention content (16.4%), satisfaction (14.5%), engagement (14.5%), and attendance (14.5%). The majority of studies (90.0%) included subjective assessments of receipt. These relied on quantitative (76.0%) rather than qualitative (42.0%) methods and studies collected data on intervention recipients (50.0%), intervention deliverers (28.0%), or both (22.0%). Few studies (26.0%) reported on the reliability or validity of methods used. Receipt is infrequently addressed in health research and improvements to methods of assessment and reporting are required.

Toward a bioethical framework for antibiotic use, antimicrobial resistance and for empirically designing ethically robust strategies to protect human health: a research protocol

PubMed Central

Martins Pereira, Sandra; de Sá Brandão, Patrícia Joana; Araújo, Joana; Carvalho, Ana Sofia

2017-01-01

Introduction Antimicrobial resistance (AMR) is a challenging global and public health issue, raising bioethical challenges, considerations and strategies. Objectives This research protocol presents a conceptual model leading to formulating an empirically based bioethics framework for antibiotic use, AMR and designing ethically robust strategies to protect human health. Methods Mixed methods research will be used and operationalized into five substudies. The bioethical framework will encompass and integrate two theoretical models: global bioethics and ethical decision-making. Results Being a study protocol, this article reports on planned and ongoing research. Conclusions Based on data collection, future findings and using a comprehensive, integrative, evidence-based approach, a step-by-step bioethical framework will be developed for (i) responsible use of antibiotics in healthcare and (ii) design of strategies to decrease AMR. This will entail the analysis and interpretation of approaches from several bioethical theories, including deontological and consequentialist approaches, and the implications of uncertainty to these approaches. PMID:28459355

Health level 7 development framework for medication administration.

PubMed

Kim, Hwa Sun; Cho, Hune

2009-01-01

We propose the creation of a standard data model for medication administration activities through the development of a clinical document architecture using the Health Level 7 Development Framework process based on an object-oriented analysis and the development method of Health Level 7 Version 3. Medication administration is the most common activity performed by clinical professionals in healthcare settings. A standardized information model and structured hospital information system are necessary to achieve evidence-based clinical activities. A virtual scenario is used to demonstrate the proposed method of administering medication. We used the Health Level 7 Development Framework and other tools to create the clinical document architecture, which allowed us to illustrate each step of the Health Level 7 Development Framework in the administration of medication. We generated an information model of the medication administration process as one clinical activity. It should become a fundamental conceptual model for understanding international-standard methodology by healthcare professionals and nursing practitioners with the objective of modeling healthcare information systems.

A Visit to Eynsham County Primary School (Part II)

ERIC Educational Resources Information Center

Madera, Kathryn

1974-01-01

An evaluation of the Eynsham County Primary School, uses the seven criteria suggested by the Spodek early childhood education analysis framework: assumptions, goals, curriculum, method, style, organization, and effectiveness. (CS)

A planning-oriented sustainability assessment framework for peri-urban water management in developing countries.

PubMed

Starkl, Markus; Brunner, Norbert; López, Eduardo; Martínez-Ruiz, José Luis

2013-12-15

DPSIR and the three-pillar model are well-established frameworks for sustainability assessment. This paper proposes a planning-oriented sustainability assessment framework (POSAF). It is informed by those frameworks but differs insofar as it puts more emphasis on a constructivist conception which recognises that sustainability needs to be defined anew for each planning problem. In finding such a consensus definition, POSAF uses participatory scenario analysis and participatory planning, technical feasibility study, participatory assessment, analysis of trade-offs and social networks in an unusual combination and for goals that differ from the original conceptions of these methods. POSAF was applied in a peri-urban area of Mexico City for the design of improved water service provision, integrating solid waste management. It supported consensus amongst users about the importance of environmental issues, informed planners about the values of stakeholders and users, detected local differences, and identified possible conflicts at an early stage of decision-making. Copyright © 2013 Elsevier Ltd. All rights reserved.

Absolute order-of-magnitude reasoning applied to a social multi-criteria evaluation framework

NASA Astrophysics Data System (ADS)

Afsordegan, A.; Sánchez, M.; Agell, N.; Aguado, J. C.; Gamboa, G.

2016-03-01

A social multi-criteria evaluation framework for solving a real-case problem of selecting a wind farm location in the regions of Urgell and Conca de Barberá in Catalonia (northeast of Spain) is studied. This paper applies a qualitative multi-criteria decision analysis approach based on linguistic labels assessment able to address uncertainty and deal with different levels of precision. This method is based on qualitative reasoning as an artificial intelligence technique for assessing and ranking multi-attribute alternatives with linguistic labels in order to handle uncertainty. This method is suitable for problems in the social framework such as energy planning which require the construction of a dialogue process among many social actors with high level of complexity and uncertainty. The method is compared with an existing approach, which has been applied previously in the wind farm location problem. This approach, consisting of an outranking method, is based on Condorcet's original method. The results obtained by both approaches are analysed and their performance in the selection of the wind farm location is compared in aggregation procedures. Although results show that both methods conduct to similar alternatives rankings, the study highlights both their advantages and drawbacks.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

A Quality Function Deployment Framework for the Service Quality of Health Information Websites

PubMed Central

Kim, Dohoon

2010-01-01

Objectives This research was conducted to identify both the users' service requirements on health information websites (HIWs) and the key functional elements for running HIWs. With the quality function deployment framework, the derived service attributes (SAs) are mapped into the suppliers' functional characteristics (FCs) to derive the most critical FCs for the users' satisfaction. Methods Using the survey data from 228 respondents, the SAs, FCs and their relationships were analyzed using various multivariate statistical methods such as principal component factor analysis, discriminant analysis, correlation analysis, etc. Simple and compound FC priorities were derived by matrix calculation. Results Nine factors of SAs and five key features of FCs were identified, and these served as the basis for the house of quality model. Based on the compound FC priorities, the functional elements pertaining to security and privacy, and usage support should receive top priority in the course of enhancing HIWs. Conclusions The quality function deployment framework can improve the FCs of the HIWs in an effective, structured manner, and it can also be utilized for critical success factors together with their strategic implications for enhancing the service quality of HIWs. Therefore, website managers could efficiently improve website operations by considering this study's results. PMID:21818418

A conceptual framework for managing clinical processes.

PubMed

Buffone, G J; Moreau, D

1997-01-01

Reengineering of the health care delivery system is underway, as is the transformation of the processes and methods used for recording information describing patient care (i.e., the development of a computer-based record). This report describes the use of object-oriented analysis and design to develop and implement clinical process reengineering as well as the organization of clinical data. In addition, the facility of the proposed framework for implementing workflow computing is discussed.

Ethics On The Fly: Toward A Drone - Specific Code Of Conduct For Law Enforcement

DTIC Science & Technology

2016-03-01

Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202- 4302, and to the Office of Management and Budget... documentary evidence of ethical frameworks for UAS currently in use by law enforcement. A comparative policy analysis is then performed to identify...Using the case study method, this thesis considered documentary evidence of ethical frameworks for UAS currently in use by law enforcement. A

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices.

PubMed

Choi, Yerim; Jeon, Yu-Mi; Wang, Lin; Kim, Kwanho

2017-08-23

The safety of children has always been an important issue, and several studies have been conducted to determine the stress state of a child to ensure the safety. Audio signals and biological signals including heart rate are known to be effective for stress state detection. However, collecting those data requires specialized equipment, which is not appropriate for the constant monitoring of children, and advanced data analysis is required for accurate detection. In this regard, we propose a stress state detection framework which utilizes both audio signal and heart rate collected from wearable devices, and adopted machine learning methods for the detection. Experiments using real-world data were conducted to compare detection performances across various machine learning methods and noise levels of audio signal. Adopting the proposed framework in the real-world will contribute to the enhancement of child safety.

Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis

PubMed Central

Papadimitriou, Konstantinos I.; Liu, Shih-Chii; Indiveri, Giacomo; Drakakis, Emmanuel M.

2014-01-01

The field of neuromorphic silicon synapse circuits is revisited and a parsimonious mathematical framework able to describe the dynamics of this class of log-domain circuits in the aggregate and in a systematic manner is proposed. Starting from the Bernoulli Cell Formalism (BCF), originally formulated for the modular synthesis and analysis of externally linear, time-invariant logarithmic filters, and by means of the identification of new types of Bernoulli Cell (BC) operators presented here, a generalized formalism (GBCF) is established. The expanded formalism covers two new possible and practical combinations of a MOS transistor (MOST) and a linear capacitor. The corresponding mathematical relations codifying each case are presented and discussed through the tutorial treatment of three well-known transistor-level examples of log-domain neuromorphic silicon synapses. The proposed mathematical tool unifies past analysis approaches of the same circuits under a common theoretical framework. The speed advantage of the proposed mathematical framework as an analysis tool is also demonstrated by a compelling comparative circuit analysis example of high order, where the GBCF and another well-known log-domain circuit analysis method are used for the determination of the input-output transfer function of the high (4th) order topology. PMID:25653579

Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis.

PubMed

Papadimitriou, Konstantinos I; Liu, Shih-Chii; Indiveri, Giacomo; Drakakis, Emmanuel M

2014-01-01

The field of neuromorphic silicon synapse circuits is revisited and a parsimonious mathematical framework able to describe the dynamics of this class of log-domain circuits in the aggregate and in a systematic manner is proposed. Starting from the Bernoulli Cell Formalism (BCF), originally formulated for the modular synthesis and analysis of externally linear, time-invariant logarithmic filters, and by means of the identification of new types of Bernoulli Cell (BC) operators presented here, a generalized formalism (GBCF) is established. The expanded formalism covers two new possible and practical combinations of a MOS transistor (MOST) and a linear capacitor. The corresponding mathematical relations codifying each case are presented and discussed through the tutorial treatment of three well-known transistor-level examples of log-domain neuromorphic silicon synapses. The proposed mathematical tool unifies past analysis approaches of the same circuits under a common theoretical framework. The speed advantage of the proposed mathematical framework as an analysis tool is also demonstrated by a compelling comparative circuit analysis example of high order, where the GBCF and another well-known log-domain circuit analysis method are used for the determination of the input-output transfer function of the high (4(th)) order topology.

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

“Best fit” framework synthesis: refining the method

PubMed Central

2013-01-01

Background Following publication of the first worked example of the “best fit” method of evidence synthesis for the systematic review of qualitative evidence in this journal, the originators of the method identified a need to specify more fully some aspects of this particular derivative of framework synthesis. Methods and Results We therefore present a second such worked example in which all techniques are defined and explained, and their appropriateness is assessed. Specified features of the method include the development of new techniques to identify theories in a systematic manner; the creation of an a priori framework for the synthesis; and the “testing” of the synthesis. An innovative combination of existing methods of quality assessment, analysis and synthesis is used to complete the process. This second worked example was a qualitative evidence synthesis of employees’ views of workplace smoking cessation interventions, in which the “best fit” method was found to be practical and fit for purpose. Conclusions The method is suited to producing context-specific conceptual models for describing or explaining the decision-making and health behaviours of patients and other groups. It offers a pragmatic means of conducting rapid qualitative evidence synthesis and generating programme theories relating to intervention effectiveness, which might be of relevance both to researchers and policy-makers. PMID:23497061

Dynamic decision-making for reliability and maintenance analysis of manufacturing systems based on failure effects

NASA Astrophysics Data System (ADS)

Zhang, Ding; Zhang, Yingjie

2017-09-01

A framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Evidential Reasoning-Based CREAM to Human Reliability Analysis in Maritime Accident Process.

PubMed

Wu, Bing; Yan, Xinping; Wang, Yang; Soares, C Guedes

2017-10-01

This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario- and barrier-based framework is proposed to describe the maritime accident process. This evidential reasoning-based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice. © 2017 Society for Risk Analysis.

Joint source based analysis of multiple brain structures in studying major depressive disorder

NASA Astrophysics Data System (ADS)

Ramezani, Mahdi; Rasoulian, Abtin; Hollenstein, Tom; Harkness, Kate; Johnsrude, Ingrid; Abolmaesumi, Purang

2014-03-01

We propose a joint Source-Based Analysis (jSBA) framework to identify brain structural variations in patients with Major Depressive Disorder (MDD). In this framework, features representing position, orientation and size (i.e. pose), shape, and local tissue composition are extracted. Subsequently, simultaneous analysis of these features within a joint analysis method is performed to generate the basis sources that show signi cant di erences between subjects with MDD and those in healthy control. Moreover, in a cross-validation leave- one-out experiment, we use a Fisher Linear Discriminant (FLD) classi er to identify individuals within the MDD group. Results show that we can classify the MDD subjects with an accuracy of 76% solely based on the information gathered from the joint analysis of pose, shape, and tissue composition in multiple brain structures.

What Touched Your Heart? Collaborative Story Analysis Emerging From an Apsáalooke Cultural Context.

PubMed

Hallett, John; Held, Suzanne; McCormick, Alma Knows His Gun; Simonds, Vanessa; Real Bird, Sloane; Martin, Christine; Simpson, Colleen; Schure, Mark; Turnsplenty, Nicole; Trottier, Coleen

2017-07-01

Community-based participatory research and decolonizing research share some recommendations for best practices for conducting research. One commonality is partnering on all stages of research; co-developing methods of data analysis is one stage with a deficit of partnering examples. We present a novel community-based and developed method for analyzing qualitative data within an Indigenous health study and explain incompatibilities of existing methods for our purposes and community needs. We describe how we explored available literature, received counsel from community Elders and experts in the field, and collaboratively developed a data analysis method consonant with community values. The method of analysis, in which interview/story remained intact, team members received story, made meaning through discussion, and generated a conceptual framework to inform intervention development, is detailed. We offer the development process and method as an example for researchers working with communities who want to keep stories intact during qualitative data analysis.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

PubMed Central

Ju, Jin Hyun; Crystal, Ronald G.

2017-01-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL. PMID:28505156

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PubMed

Ju, Jin Hyun; Shenoy, Sushila A; Crystal, Ronald G; Mezey, Jason G

2017-05-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

A PREFERENCE-OPPORTUNITY-CHOICE FRAMEWORK WITH APPLICATIONS TO INTERGROUP FRIENDSHIP*

PubMed Central

Zeng, Zhen; Xie, Yu

2009-01-01

A longstanding objective of friendship research is to identify the effects of personal preference and structural opportunity on intergroup friendship choice. Although past studies have used various methods to separate preference from opportunity, researchers have not yet systematically compared the properties and implications of these methods. We put forward a general framework for discrete choice, where choice probability is specified as proportional to the product of preference and opportunity. To implement this framework, we propose a modification to the conditional logit model for estimating preference parameters free from the influence of opportunity structure. We then compare our approach to several alternative methods for separating preference and opportunity used in the friendship choice literature. As an empirical example, we test hypotheses of homophily and status asymmetry in friendship choice using data from the National Longitudinal Study of Adolescent Health. The example also demonstrates the approach of conducting a sensitivity analysis to examine how parameter estimates vary by specification of the opportunity structure. PMID:19569394

A decision support framework for characterizing and managing dermal exposures to chemicals during Emergency Management and Operations.

PubMed

Dotson, G Scott; Hudson, Naomi L; Maier, Andrew

2015-01-01

Emergency Management and Operations (EMO) personnel are in need of resources and tools to assist in understanding the health risks associated with dermal exposures during chemical incidents. This article reviews available resources and presents a conceptual framework for a decision support system (DSS) that assists in characterizing and managing risk during chemical emergencies involving dermal exposures. The framework merges principles of three decision-making techniques: 1) scenario planning, 2) risk analysis, and 3) multicriteria decision analysis (MCDA). This DSS facilitates dynamic decision making during each of the distinct life cycle phases of an emergency incident (ie, preparedness, response, or recovery) and identifies EMO needs. A checklist tool provides key questions intended to guide users through the complexities of conducting a dermal risk assessment. The questions define the scope of the framework for resource identification and application to support decision-making needs. The framework consists of three primary modules: 1) resource compilation, 2) prioritization, and 3) decision. The modules systematically identify, organize, and rank relevant information resources relating to the hazards of dermal exposures to chemicals and risk management strategies. Each module is subdivided into critical elements designed to further delineate the resources based on relevant incident phase and type of information. The DSS framework provides a much needed structure based on contemporary decision analysis principles for 1) documenting key questions for EMO problem formulation and 2) a method for systematically organizing, screening, and prioritizing information resources on dermal hazards, exposures, risk characterization, and management.

A decision support framework for characterizing and managing dermal exposures to chemicals during Emergency Management and Operations

PubMed Central

Dotson, G. Scott; Hudson, Naomi L.; Maier, Andrew

2016-01-01

Emergency Management and Operations (EMO) personnel are in need of resources and tools to assist in understanding the health risks associated with dermal exposures during chemical incidents. This article reviews available resources and presents a conceptual framework for a decision support system (DSS) that assists in characterizing and managing risk during chemical emergencies involving dermal exposures. The framework merges principles of three decision-making techniques: 1) scenario planning, 2) risk analysis, and 3) multicriteria decision analysis (MCDA). This DSS facilitates dynamic decision making during each of the distinct life cycle phases of an emergency incident (ie, preparedness, response, or recovery) and identifies EMO needs. A checklist tool provides key questions intended to guide users through the complexities of conducting a dermal risk assessment. The questions define the scope of the framework for resource identification and application to support decision-making needs. The framework consists of three primary modules: 1) resource compilation, 2) prioritization, and 3) decision. The modules systematically identify, organize, and rank relevant information resources relating to the hazards of dermal exposures to chemicals and risk management strategies. Each module is subdivided into critical elements designed to further delineate the resources based on relevant incident phase and type of information. The DSS framework provides a much needed structure based on contemporary decision analysis principles for 1) documenting key questions for EMO problem formulation and 2) a method for systematically organizing, screening, and prioritizing information resources on dermal hazards, exposures, risk characterization, and management. PMID:26312660

Object-Based Image Analysis Beyond Remote Sensing - the Human Perspective

NASA Astrophysics Data System (ADS)

Blaschke, T.; Lang, S.; Tiede, D.; Papadakis, M.; Györi, A.

2016-06-01

We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate place in terms of objects - is object-based image analysis (OBIA).

Time and frequency domain characteristics of detrending-operation-based scaling analysis: Exact DFA and DMA frequency responses

NASA Astrophysics Data System (ADS)

Kiyono, Ken; Tsujimoto, Yutaka

2016-07-01

We develop a general framework to study the time and frequency domain characteristics of detrending-operation-based scaling analysis methods, such as detrended fluctuation analysis (DFA) and detrending moving average (DMA) analysis. In this framework, using either the time or frequency domain approach, the frequency responses of detrending operations are calculated analytically. Although the frequency domain approach based on conventional linear analysis techniques is only applicable to linear detrending operations, the time domain approach presented here is applicable to both linear and nonlinear detrending operations. Furthermore, using the relationship between the time and frequency domain representations of the frequency responses, the frequency domain characteristics of nonlinear detrending operations can be obtained. Based on the calculated frequency responses, it is possible to establish a direct connection between the root-mean-square deviation of the detrending-operation-based scaling analysis and the power spectrum for linear stochastic processes. Here, by applying our methods to DFA and DMA, including higher-order cases, exact frequency responses are calculated. In addition, we analytically investigate the cutoff frequencies of DFA and DMA detrending operations and show that these frequencies are not optimally adjusted to coincide with the corresponding time scale.

Time and frequency domain characteristics of detrending-operation-based scaling analysis: Exact DFA and DMA frequency responses.

PubMed

Kiyono, Ken; Tsujimoto, Yutaka

2016-07-01

We develop a general framework to study the time and frequency domain characteristics of detrending-operation-based scaling analysis methods, such as detrended fluctuation analysis (DFA) and detrending moving average (DMA) analysis. In this framework, using either the time or frequency domain approach, the frequency responses of detrending operations are calculated analytically. Although the frequency domain approach based on conventional linear analysis techniques is only applicable to linear detrending operations, the time domain approach presented here is applicable to both linear and nonlinear detrending operations. Furthermore, using the relationship between the time and frequency domain representations of the frequency responses, the frequency domain characteristics of nonlinear detrending operations can be obtained. Based on the calculated frequency responses, it is possible to establish a direct connection between the root-mean-square deviation of the detrending-operation-based scaling analysis and the power spectrum for linear stochastic processes. Here, by applying our methods to DFA and DMA, including higher-order cases, exact frequency responses are calculated. In addition, we analytically investigate the cutoff frequencies of DFA and DMA detrending operations and show that these frequencies are not optimally adjusted to coincide with the corresponding time scale.

Using EIGER for Antenna Design and Analysis

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Khayat, Michael; Kennedy, Timothy F.; Fink, Patrick W.

2007-01-01

EIGER (Electromagnetic Interactions GenERalized) is a frequency-domain electromagnetics software package that is built upon a flexible framework, designed using object-oriented techniques. The analysis methods used include moment method solutions of integral equations, finite element solutions of partial differential equations, and combinations thereof. The framework design permits new analysis techniques (boundary conditions, Green#s functions, etc.) to be added to the software suite with a sensible effort. The code has been designed to execute (in serial or parallel) on a wide variety of platforms from Intel-based PCs and Unix-based workstations. Recently, new potential integration scheme s that avoid singularity extraction techniques have been added for integral equation analysis. These new integration schemes are required for facilitating the use of higher-order elements and basis functions. Higher-order elements are better able to model geometrical curvature using fewer elements than when using linear elements. Higher-order basis functions are beneficial for simulating structures with rapidly varying fields or currents. Results presented here will demonstrate curren t and future capabilities of EIGER with respect to analysis of installed antenna system performance in support of NASA#s mission of exploration. Examples include antenna coupling within an enclosed environment and antenna analysis on electrically large manned space vehicles.

Unified Computational Methods for Regression Analysis of Zero-Inflated and Bound-Inflated Data

PubMed Central

Yang, Yan; Simpson, Douglas

2010-01-01

Bounded data with excess observations at the boundary are common in many areas of application. Various individual cases of inflated mixture models have been studied in the literature for bound-inflated data, yet the computational methods have been developed separately for each type of model. In this article we use a common framework for computing these models, and expand the range of models for both discrete and semi-continuous data with point inflation at the lower boundary. The quasi-Newton and EM algorithms are adapted and compared for estimation of model parameters. The numerical Hessian and generalized Louis method are investigated as means for computing standard errors after optimization. Correlated data are included in this framework via generalized estimating equations. The estimation of parameters and effectiveness of standard errors are demonstrated through simulation and in the analysis of data from an ultrasound bioeffect study. The unified approach enables reliable computation for a wide class of inflated mixture models and comparison of competing models. PMID:20228950

ATLAS particle detector CSC ROD software design and implementation, and, Addition of K physics to chi-squared analysis of FDQM

NASA Astrophysics Data System (ADS)

Hawkins, Donovan Lee

In this thesis I present a software framework for use on the ATLAS muon CSC readout driver. This C++ framework uses plug-in Decoders incorporating hand-optimized assembly language routines to perform sparsification and data formatting. The software is designed with both flexibility and performance in mind, and runs on a custom 9U VME board using Texas Instruments TMS360C6203 digital signal processors. I describe the requirements of the software, the methods used in its design, and the results of testing the software with simulated data. I also present modifications to a chi-squared analysis of the Standard Model and Four Down Quark Model (FDQM) originally done by Dr. Dennis Silverman. The addition of four new experiments to the analysis has little effect on the Standard Model but provides important new restrictions on the FDQM. The method used to incorporate these new experiments is presented, and the consequences of their addition are reviewed.

Gap analysis: Concepts, methods, and recent results

USGS Publications Warehouse

Jennings, M.D.

2000-01-01

Rapid progress is being made in the conceptual, technical, and organizational requirements for generating synoptic multi-scale views of the earth's surface and its biological content. Using the spatially comprehensive data that are now available, researchers, land managers, and land-use planners can, for the first time, quantitatively place landscape units - from general categories such as 'Forests' or 'Cold-Deciduous Shrubland Formation' to more categories such as 'Picea glauca-Abies balsamea-Populus spp. Forest Alliance' - in their large-area contexts. The National Gap Analysis Program (GAP) has developed the technical and organizational capabilities necessary for the regular production and analysis of such information. This paper provides a brief overview of concepts and methods as well as some recent results from the GAP projects. Clearly, new frameworks for biogeographic information and organizational cooperation are needed if we are to have any hope of documenting the full range of species occurrences and ecological processes in ways meaningful to their management. The GAP experience provides one model for achieving these new frameworks.

Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies

PubMed Central

Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George

2016-01-01

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K-means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. PMID:27330233

Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies.

PubMed

Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K -means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

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

Data Analysis and Visualization; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii)more » evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.« less

Developing and Assessing Teachers' Knowledge of Game-Based Learning

ERIC Educational Resources Information Center

Shah, Mamta; Foster, Aroutis

2015-01-01

Research focusing on the development and assessment of teacher knowledge in game-based learning is in its infancy. A mixed-methods study was undertaken to educate pre-service teachers in game-based learning using the Game Network Analysis (GaNA) framework. Fourteen pre-service teachers completed a methods course, which prepared them in game…

Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

ERIC Educational Resources Information Center

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2010-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex…

Software development for teleroentgenogram analysis

NASA Astrophysics Data System (ADS)

Goshkoderov, A. A.; Khlebnikov, N. A.; Obabkov, I. N.; Serkov, K. V.; Gajniyarov, I. M.; Aliev, A. A.

2017-09-01

A framework for the analysis and calculation of teleroentgenograms was developed. Software development was carried out in the Department of Children's Dentistry and Orthodontics in Ural State Medical University. The software calculates the teleroentgenogram by the original method which was developed in this medical department. Program allows designing its own methods for calculating the teleroentgenograms by new methods. It is planned to use the technology of machine learning (Neural networks) in the software. This will help to make the process of calculating the teleroentgenograms easier because methodological points will be placed automatically.

Using Qualitative Hazard Analysis to Guide Quantitative Safety Analysis

NASA Technical Reports Server (NTRS)

Shortle, J. F.; Allocco, M.

2005-01-01

Quantitative methods can be beneficial in many types of safety investigations. However, there are many difficulties in using quantitative m ethods. Far example, there may be little relevant data available. This paper proposes a framework for using quantitative hazard analysis to prioritize hazard scenarios most suitable for quantitative mziysis. The framework first categorizes hazard scenarios by severity and likelihood. We then propose another metric "modeling difficulty" that desc ribes the complexity in modeling a given hazard scenario quantitatively. The combined metrics of severity, likelihood, and modeling difficu lty help to prioritize hazard scenarios for which quantitative analys is should be applied. We have applied this methodology to proposed concepts of operations for reduced wake separation for airplane operatio ns at closely spaced parallel runways.

Comparative analysis on the selection of number of clusters in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Kabashima, Yoshiyuki

2018-02-01

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

Sustainability analysis and life-cycle ecological impacts of rainwater harvesting systems using holistic analysis and a modified eco-efficiency framework

EPA Science Inventory

Background/Question/Methods A sustainability paradigm is being recognized globally as a path forward for human prosperity and ecological health in the face of climate change and meeting challenges of the water-energy-food nexus. Rainfall shortages for drinking water and crop pro...

Advancing public participation in scientific research: A framework for leveraging public participation in environmental health and emergency response research

EPA Science Inventory

This research paper uses case analysis methods to understand why participants engage in this innovative approach public participation in scientific research, and what they hope that will mean for their community. The research questions that guide this analysis are: 1) what factor...

Demonstrating the Financial Benefit of Human Resource Development: Status and Update on the Theory and Practice.

ERIC Educational Resources Information Center

Swanson, Richard A.

1998-01-01

A research review identified findings about the financial analysis method, forecasting of the financial benefits of human resource development (HRD), and recent financial analysis research: (1) HRD embedded in a performance improvement framework yielded high return on investment; and (2) HRD interventions focused on performance variables forecast…

A Biologically Informed Framework for the Analysis of the PPAR Signaling Pathway using a Bayesian Network

EPA Science Inventory

The US EPA’s ToxCastTM program seeks to combine advances in high-throughput screening technology with methodologies from statistics and computer science to develop high-throughput decision support tools for assessing chemical hazard and risk. To develop new methods of analysis of...

Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives

NASA Technical Reports Server (NTRS)

Hearn, Tristan; Hendricks, Eric; Chin, Jeffrey; Gray, Justin; Moore, Kenneth T.

2016-01-01

A new engine cycle analysis tool, called Pycycle, was recently built using the OpenMDAO framework. This tool uses equilibrium chemistry based thermodynamics, and provides analytic derivatives. This allows for stable and efficient use of gradient-based optimization and sensitivity analysis methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a multi-point turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.

Developing an evaluation framework for clinical redesign programs: lessons learnt.

PubMed

Samaranayake, Premaratne; Dadich, Ann; Fitzgerald, Anneke; Zeitz, Kathryn

2016-09-19

Purpose The purpose of this paper is to present lessons learnt through the development of an evaluation framework for a clinical redesign programme - the aim of which was to improve the patient journey through improved discharge practices within an Australian public hospital. Design/methodology/approach The development of the evaluation framework involved three stages - namely, the analysis of secondary data relating to the discharge planning pathway; the analysis of primary data including field-notes and interview transcripts on hospital processes; and the triangulation of these data sets to devise the framework. The evaluation framework ensured that resource use, process management, patient satisfaction, and staff well-being and productivity were each connected with measures, targets, and the aim of clinical redesign programme. Findings The application of business process management and a balanced scorecard enabled a different way of framing the evaluation, ensuring measurable outcomes were connected to inputs and outputs. Lessons learnt include: first, the importance of mixed-methods research to devise the framework and evaluate the redesigned processes; second, the need for appropriate tools and resources to adequately capture change across the different domains of the redesign programme; and third, the value of developing and applying an evaluative framework progressively. Research limitations/implications The evaluation framework is limited by its retrospective application to a clinical process redesign programme. Originality/value This research supports benchmarking with national and international practices in relation to best practice healthcare redesign processes. Additionally, it provides a theoretical contribution on evaluating health services improvement and redesign initiatives.

Critical analysis of e-health readiness assessment frameworks: suitability for application in developing countries.

PubMed

Mauco, Kabelo Leonard; Scott, Richard E; Mars, Maurice

2018-02-01

Introduction e-Health is an innovative way to make health services more effective and efficient and application is increasing worldwide. e-Health represents a substantial ICT investment and its failure usually results in substantial losses in time, money (including opportunity costs) and effort. Therefore it is important to assess e-health readiness prior to implementation. Several frameworks have been published on e-health readiness assessment, under various circumstances and geographical regions of the world. However, their utility for the developing world is unknown. Methods A literature review and analysis of published e-health readiness assessment frameworks or models was performed to determine if any are appropriate for broad assessment of e-health readiness in the developing world. A total of 13 papers described e-health readiness in different settings. Results and Discussion Eight types of e-health readiness were identified and no paper directly addressed all of these. The frameworks were based upon varying assumptions and perspectives. There was no underlying unifying theory underpinning the frameworks. Few assessed government and societal readiness, and none cultural readiness; all are important in the developing world. While the shortcomings of existing frameworks have been highlighted, most contain aspects that are relevant and can be drawn on when developing a framework and assessment tools for the developing world. What emerged is the need to develop different assessment tools for the various stakeholder sectors. This is an area that needs further research before attempting to develop a more generic framework for the developing world.

Segmentation of radiographic images under topological constraints: application to the femur.

PubMed

Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

2010-09-01

A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.

Cluster Correspondence Analysis.

PubMed

van de Velden, M; D'Enza, A Iodice; Palumbo, F

2017-03-01

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

Reference-free error estimation for multiple measurement methods.

PubMed

Madan, Hennadii; Pernuš, Franjo; Špiclin, Žiga

2018-01-01

We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works, the random errors are modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on six synthetic and one clinical dataset containing measurements of total lesion load, a biomarker of neurodegenerative diseases, which was obtained with four automatic methods by analyzing brain magnetic resonance images. The estimates of bias and random error were in a good agreement with the corresponding least squares regression estimates against a reference.

A partial differential equation-based general framework adapted to Rayleigh's, Rician's and Gaussian's distributed noise for restoration and enhancement of magnetic resonance image.

PubMed

Yadav, Ram Bharos; Srivastava, Subodh; Srivastava, Rajeev

2016-01-01

The proposed framework is obtained by casting the noise removal problem into a variational framework. This framework automatically identifies the various types of noise present in the magnetic resonance image and filters them by choosing an appropriate filter. This filter includes two terms: the first term is a data likelihood term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of the corresponding probability density functions: Gaussian or Rayleigh or Rician. Further, due to the ill-posedness of the likelihood term, a prior function is needed. This paper examines three partial differential equation based priors which include total variation based prior, anisotropic diffusion based prior, and a complex diffusion (CD) based prior. A regularization parameter is used to balance the trade-off between data fidelity term and prior. The finite difference scheme is used for discretization of the proposed method. The performance analysis and comparative study of the proposed method with other standard methods is presented for brain web dataset at varying noise levels in terms of peak signal-to-noise ratio, mean square error, structure similarity index map, and correlation parameter. From the simulation results, it is observed that the proposed framework with CD based prior is performing better in comparison to other priors in consideration.

Enterprise application architecture development based on DoDAF and TOGAF

NASA Astrophysics Data System (ADS)

Tao, Zhi-Gang; Luo, Yun-Feng; Chen, Chang-Xin; Wang, Ming-Zhe; Ni, Feng

2017-05-01

For the purpose of supporting the design and analysis of enterprise application architecture, here, we report a tailored enterprise application architecture description framework and its corresponding design method. The presented framework can effectively support service-oriented architecting and cloud computing by creating the metadata model based on architecture content framework (ACF), DoDAF metamodel (DM2) and Cloud Computing Modelling Notation (CCMN). The framework also makes an effort to extend and improve the mapping between The Open Group Architecture Framework (TOGAF) application architectural inputs/outputs, deliverables and Department of Defence Architecture Framework (DoDAF)-described models. The roadmap of 52 DoDAF-described models is constructed by creating the metamodels of these described models and analysing the constraint relationship among metamodels. By combining the tailored framework and the roadmap, this article proposes a service-oriented enterprise application architecture development process. Finally, a case study is presented to illustrate the results of implementing the tailored framework in the Southern Base Management Support and Information Platform construction project using the development process proposed by the paper.

Singularity in structural optimization

NASA Technical Reports Server (NTRS)

Patnaik, S. N.; Guptill, J. D.; Berke, L.

1993-01-01

The conditions under which global and local singularities may arise in structural optimization are examined. Examples of these singularities are presented, and a framework is given within which the singularities can be recognized. It is shown, in particular, that singularities can be identified through the analysis of stress-displacement relations together with compatibility conditions or the displacement-stress relations derived by the integrated force method of structural analysis. Methods of eliminating the effects of singularities are suggested and illustrated numerically.

Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives

NASA Technical Reports Server (NTRS)

Hearn, Tristan; Hendricks, Eric; Chin, Jeffrey; Gray, Justin; Moore, Kenneth T.

2016-01-01

A new engine cycle analysis tool, called Pycycle, was built using the OpenMDAO framework. Pycycle provides analytic derivatives allowing for an efficient use of gradient-based optimization methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.

Evolutionary game theory meets social science: is there a unifying rule for human cooperation?

PubMed

Rosas, Alejandro

2010-05-21

Evolutionary game theory has shown that human cooperation thrives in different types of social interactions with a PD structure. Models treat the cooperative strategies within the different frameworks as discrete entities and sometimes even as contenders. Whereas strong reciprocity was acclaimed as superior to classic reciprocity for its ability to defeat defectors in public goods games, recent experiments and simulations show that costly punishment fails to promote cooperation in the IR and DR games, where classic reciprocity succeeds. My aim is to show that cooperative strategies across frameworks are capable of a unified treatment, for they are governed by a common underlying rule or norm. An analysis of the reputation and action rules that govern some representative cooperative strategies both in models and in economic experiments confirms that the different frameworks share a conditional action rule and several reputation rules. The common conditional rule contains an option between costly punishment and withholding benefits that provides alternative enforcement methods against defectors. Depending on the framework, individuals can switch to the appropriate strategy and method of enforcement. The stability of human cooperation looks more promising if one mechanism controls successful strategies across frameworks. Published by Elsevier Ltd.

Assessing the health impact of transnational corporations: a case study on McDonald's Australia.

PubMed

Anaf, Julia; Baum, Frances E; Fisher, Matt; Harris, Elizabeth; Friel, Sharon

2017-02-06

The practices of transnational corporations affect population health through production methods, shaping social determinants of health, or influencing the regulatory structures governing their activities. There has been limited research on community exposures to TNC policies and practices. Our pilot research used McDonald's Australia to test methods for assessing the health impacts of one TNC within Australia. We adapted existing Health Impact Assessment methods to assess McDonald's activities. Data identifying potential impacts were sourced through document analysis, including McDonald's corporate literature; media analysis and semi-structured interviews. We commissioned a spatial and socioeconomic analysis of McDonald's restaurants in Australia through Geographic Information System technology. The data was mapped against a corporate health impact assessment framework which included McDonald's Australia's political and business practices; products and marketing; workforce, social, environmental and economic conditions; and consumers' health related behaviours. We identified both positive and detrimental aspects of McDonald's Australian operations across the scope of the CHIA framework. We found that McDonald's outlets were slightly more likely to be located in areas of lower socioeconomic status. McDonald's workplace conditions were found to be more favourable than those in many other countries which reflects compliance with Australian employment regulations. The breadth of findings revealed the need for governments to strengthen regulatory mechanisms that are conducive to health; the opportunity for McDonald's to augment their corporate social responsibility initiatives and bolster reputational endorsement; and civil society actors to inform their advocacy towards health and equity outcomes from TNC operations. Our study indicates that undertaking a corporate health impact assessment is possible, with the different methods revealing sufficient information to realise that strong regulatory frameworks are need to help to avoid or to mediate negative health impacts.

A Framework for Integrating Environmental Justice in Regulatory Analysis

PubMed Central

Nweke, Onyemaechi C.

2011-01-01

With increased interest in integrating environmental justice into the process for developing environmental regulations in the United States, analysts and decision makers are confronted with the question of what methods and data can be used to assess disproportionate environmental health impacts. However, as a first step to identifying data and methods, it is important that analysts understand what information on equity impacts is needed for decision making. Such knowledge originates from clearly stated equity objectives and the reflection of those objectives throughout the analytical activities that characterize Regulatory Impact Analysis (RIA), a process that is traditionally used to inform decision making. The framework proposed in this paper advocates structuring analyses to explicitly provide pre-defined output on equity impacts. Specifically, the proposed framework emphasizes: (a) defining equity objectives for the proposed regulatory action at the onset of the regulatory process, (b) identifying specific and related sub-objectives for key analytical steps in the RIA process, and (c) developing explicit analytical/research questions to assure that stated sub-objectives and objectives are met. In proposing this framework, it is envisioned that information on equity impacts informs decision-making in regulatory development, and that this is achieved through a systematic and consistent approach that assures linkages between stated equity objectives, regulatory analyses, selection of policy options, and the design of compliance and enforcement activities. PMID:21776235

Extracting Effective Higgs Couplings in the Golden Channel

DOE PAGES

Chen, Yi; Vega-Morales, Roberto

2014-04-08

Kinematic distributions in Higgs decays to four charged leptons, the so called ‘golden channel, are a powerful probe of the tensor structure of its couplings to neutral electroweak gauge bosons. In this study we construct the first part of a comprehensive analysis framework designed to maximize the information contained in this channel in order to perform direct extraction of the various possible Higgs couplings. We first complete an earlier analytic calculation of the leading order fully differential cross sections for the golden channel signal and background to include the 4e and 4μ final states with interference between identical final states.more » We also examine the relative fractions of the different possible combinations of scalar-tensor couplings by integrating the fully differential cross section over all kinematic variables as well as show various doubly differential spectra for both the signal and background. From these analytic expressions we then construct a ‘generator level’ analysis framework based on the maximum likelihood method. Then, we demonstrate the ability of our framework to perform multi-parameter extractions of all the possible effective couplings of a spin-0 scalar to pairs of neutral electroweak gauge bosons including any correlations. Furthermore, this framework provides a powerful method for study of these couplings and can be readily adapted to include the relevant detector and systematic effects which we demonstrate in an accompanying study to follow.« less

A modeling framework for exposing risks in complex systems.

PubMed

Sharit, J

2000-08-01

This article introduces and develops a modeling framework for exposing risks in the form of human errors and adverse consequences in high-risk systems. The modeling framework is based on two components: a two-dimensional theory of accidents in systems developed by Perrow in 1984, and the concept of multiple system perspectives. The theory of accidents differentiates systems on the basis of two sets of attributes. One set characterizes the degree to which systems are interactively complex; the other emphasizes the extent to which systems are tightly coupled. The concept of multiple perspectives provides alternative descriptions of the entire system that serve to enhance insight into system processes. The usefulness of these two model components derives from a modeling framework that cross-links them, enabling a variety of work contexts to be exposed and understood that would otherwise be very difficult or impossible to identify. The model components and the modeling framework are illustrated in the case of a large and comprehensive trauma care system. In addition to its general utility in the area of risk analysis, this methodology may be valuable in applications of current methods of human and system reliability analysis in complex and continually evolving high-risk systems.

Combining Cryptography with EEG Biometrics

PubMed Central

Kazanavičius, Egidijus; Woźniak, Marcin

2018-01-01

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

Combining Cryptography with EEG Biometrics.

PubMed

Damaševičius, Robertas; Maskeliūnas, Rytis; Kazanavičius, Egidijus; Woźniak, Marcin

2018-01-01

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

The 4C framework for making reasonable adjustments for people with learning disabilities.

PubMed

Marsden, Daniel; Giles, Rachel

2017-01-18

Background People with learning disabilities experience significant inequalities in accessing healthcare. Legal frameworks, such as the Equality Act 2010, are intended to reduce such disparities in care, and require organisations to make 'reasonable adjustments' for people with disabilities, including learning disabilities. However, reasonable adjustments are often not clearly defined or adequately implemented in clinical practice. Aim To examine and synthesise the challenges in caring for people with learning disabilities to develop a framework for making reasonable adjustments for people with learning disabilities in hospital. This framework would assist ward staff in identifying and managing the challenges of delivering person-centred, safe and effective healthcare to people with learning disabilities in this setting. Method Fourth-generation evaluation, collaborative thematic analysis, reflection and a secondary analysis were used to develop a framework for making reasonable adjustments in the hospital setting. The authors attended ward manager and matron group meetings to collect their claims, concerns and issues, then conducted a collaborative thematic analysis with the group members to identify the main themes. Findings Four main themes were identified from the ward manager and matron group meetings: communication, choice-making, collaboration and coordination. These were used to develop the 4C framework for making reasonable adjustments for people with learning disabilities in hospital. Discussion The 4C framework has provided a basis for delivering person-centred care for people with learning disabilities. It has been used to inform training needs analyses, develop audit tools to review delivery of care that is adjusted appropriately to the individual patient; and to develop competencies for learning disability champions. The most significant benefit of the 4C framework has been in helping to evaluate and resolve practice-based scenarios. Conclusion Use of the 4C framework may enhance the care of people with learning disabilities in hospital, by enabling reasonable adjustments to be made in these settings.

Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

PubMed

Schouten, Kim; van der Weijde, Onne; Frasincar, Flavius; Dekker, Rommert

2018-04-01

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

Development of Implicit Methods in CFD NASA Ames Research Center 1970's - 1980's

NASA Technical Reports Server (NTRS)

Pulliam, Thomas H.

2010-01-01

The focus here is on the early development (mid 1970's-1980's) at NASA Ames Research Center of implicit methods in Computational Fluid Dynamics (CFD). A class of implicit finite difference schemes of the Beam and Warming approximate factorization type will be addressed. The emphasis will be on the Euler equations. A review of material pertinent to the solution of the Euler equations within the framework of implicit methods will be presented. The eigensystem of the equations will be used extensively in developing a framework for various methods applied to the Euler equations. The development and analysis of various aspects of this class of schemes will be given along with the motivations behind many of the choices. Various acceleration and efficiency modifications such as matrix reduction, diagonalization and flux split schemes will be presented.

Methods for associating or dissociating guest materials with a metal organic framework, systems for associating or dissociating guest materials within a series of metal organic frameworks, thermal energy transfer assemblies, and methods for transferring thermal energy

DOEpatents

McGrail, B. Peter; Brown, Daryl R.; Thallapally, Praveen K.

2016-08-02

Methods for releasing associated guest materials from a metal organic framework are provided. Methods for associating guest materials with a metal organic framework are also provided. Methods are provided for selectively associating or dissociating guest materials with a metal organic framework. Systems for associating or dissociating guest materials within a series of metal organic frameworks are provided. Thermal energy transfer assemblies are provided. Methods for transferring thermal energy are also provided.

Methods for associating or dissociating guest materials with a metal organic framework, systems for associating or dissociating guest materials within a series of metal organic frameworks, thermal energy transfer assemblies, and methods for transferring thermal energy

DOEpatents

McGrail, B. Peter; Brown, Daryl R.; Thallapally, Praveen K.

2014-08-05

Methods for releasing associated guest materials from a metal organic framework are provided. Methods for associating guest materials with a metal organic framework are also provided. Methods are provided for selectively associating or dissociating guest materials with a metal organic framework. Systems for associating or dissociating guest materials within a series of metal organic frameworks are provided. Thermal energy transfer assemblies are provided. Methods for transferring thermal energy are also provided.

Mathematical Problem Solving Ability of Junior High School Students through Ang’s Framework for Mathematical Modelling Instruction

NASA Astrophysics Data System (ADS)

Fasni, N.; Turmudi, T.; Kusnandi, K.

2017-09-01

This research background of this research is the importance of student problem solving abilities. The purpose of this study is to find out whether there are differences in the ability to solve mathematical problems between students who have learned mathematics using Ang’s Framework for Mathematical Modelling Instruction (AFFMMI) and students who have learned using scientific approach (SA). The method used in this research is a quasi-experimental method with pretest-postest control group design. Data analysis of mathematical problem solving ability using Indepent Sample Test. The results showed that there was a difference in the ability to solve mathematical problems between students who received learning with Ang’s Framework for Mathematical Modelling Instruction and students who received learning with a scientific approach. AFFMMI focuses on mathematical modeling. This modeling allows students to solve problems. The use of AFFMMI is able to improve the solving ability.

Linking theory with qualitative research through study of stroke caregiving families.

PubMed

Pierce, Linda L; Steiner, Victoria; Cervantez Thompson, Teresa L; Friedemann, Marie-Luise

2014-01-01

This theoretical article outlines the deliberate process of applying a qualitative data analysis method rooted in Friedemann's Framework of Systemic Organization through the study of a web-based education and support intervention for stroke caregiving families. Directed by Friedemann's framework, the analytic method involved developing, refining, and using a coding rubric to explore interactive patterns between caregivers and care recipients from this 3-month feasibility study using this education and support intervention. Specifically, data were gathered from the intervention's web-based discussion component between caregivers and the nurse specialist, as well as from telephone caregiver interviews. A theoretical framework guided the process of developing and refining this coding rubric for the purpose of organizing data; but, more importantly, guided the investigators' thought processes, allowing them to extract rich information from the data set, as well as synthesize this information to generate a broad understanding of the caring situation. © 2013 Association of Rehabilitation Nurses.

Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds.

PubMed

Hamad, Eradah O; Savundranayagam, Marie Y; Holmes, Jeffrey D; Kinsella, Elizabeth Anne; Johnson, Andrew M

2016-03-08

Twitter's 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

Validation of the theoretical domains framework for use in behaviour change and implementation research.

PubMed

Cane, James; O'Connor, Denise; Michie, Susan

2012-04-24

An integrative theoretical framework, developed for cross-disciplinary implementation and other behaviour change research, has been applied across a wide range of clinical situations. This study tests the validity of this framework. Validity was investigated by behavioural experts sorting 112 unique theoretical constructs using closed and open sort tasks. The extent of replication was tested by Discriminant Content Validation and Fuzzy Cluster Analysis. There was good support for a refinement of the framework comprising 14 domains of theoretical constructs (average silhouette value 0.29): 'Knowledge', 'Skills', 'Social/Professional Role and Identity', 'Beliefs about Capabilities', 'Optimism', 'Beliefs about Consequences', 'Reinforcement', 'Intentions', 'Goals', 'Memory, Attention and Decision Processes', 'Environmental Context and Resources', 'Social Influences', 'Emotions', and 'Behavioural Regulation'. The refined Theoretical Domains Framework has a strengthened empirical base and provides a method for theoretically assessing implementation problems, as well as professional and other health-related behaviours as a basis for intervention development.

Measuring organizational and individual factors thought to influence the success of quality improvement in primary care: a systematic review of instruments

PubMed Central

2012-01-01

Background Continuous quality improvement (CQI) methods are widely used in healthcare; however, the effectiveness of the methods is variable, and evidence about the extent to which contextual and other factors modify effects is limited. Investigating the relationship between these factors and CQI outcomes poses challenges for those evaluating CQI, among the most complex of which relate to the measurement of modifying factors. We aimed to provide guidance to support the selection of measurement instruments by systematically collating, categorising, and reviewing quantitative self-report instruments. Methods Data sources: We searched MEDLINE, PsycINFO, and Health and Psychosocial Instruments, reference lists of systematic reviews, and citations and references of the main report of instruments. Study selection: The scope of the review was determined by a conceptual framework developed to capture factors relevant to evaluating CQI in primary care (the InQuIRe framework). Papers reporting development or use of an instrument measuring a construct encompassed by the framework were included. Data extracted included instrument purpose; theoretical basis, constructs measured and definitions; development methods and assessment of measurement properties. Analysis and synthesis: We used qualitative analysis of instrument content and our initial framework to develop a taxonomy for summarising and comparing instruments. Instrument content was categorised using the taxonomy, illustrating coverage of the InQuIRe framework. Methods of development and evidence of measurement properties were reviewed for instruments with potential for use in primary care. Results We identified 186 potentially relevant instruments, 152 of which were analysed to develop the taxonomy. Eighty-four instruments measured constructs relevant to primary care, with content measuring CQI implementation and use (19 instruments), organizational context (51 instruments), and individual factors (21 instruments). Forty-one instruments were included for full review. Development methods were often pragmatic, rather than systematic and theory-based, and evidence supporting measurement properties was limited. Conclusions Many instruments are available for evaluating CQI, but most require further use and testing to establish their measurement properties. Further development and use of these measures in evaluations should increase the contribution made by individual studies to our understanding of CQI and enhance our ability to synthesise evidence for informing policy and practice. PMID:23241168

People with Disability in Vocational High Schools: between School and Work

NASA Astrophysics Data System (ADS)

Haryanti, R. H.

2018-02-01

Vocational education is positioned within the framework of Vocational Education for All. Therefore, the alignment between the world of education and the world of work is an issue that is always actual within the framework of vocational education, including being an actual issue for people with disabilities. This article aims to map how the state frames disability and vocational education issues within the framework of public policy. The research was conducted using qualitative research method in which the data obtained from the study of documentation. Analysis of the data using content analysis. The results of the study show that the State Policy has not fully framed the issue of vocational education for the disabled into special policies. The vocational education policy for the disabled is still integrated in the major policies in certain institutions. No policy innovations have yet significantly provided a special place for the disabled.

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

PubMed Central

Li, Weifeng; Cheng, Xiaoyun; Guo, Gaohua

2014-01-01

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework. PMID:25435865

Framework for Architecture Trade Study Using MBSE and Performance Simulation

NASA Technical Reports Server (NTRS)

Ryan, Jessica; Sarkani, Shahram; Mazzuchim, Thomas

2012-01-01

Increasing complexity in modern systems as well as cost and schedule constraints require a new paradigm of system engineering to fulfill stakeholder needs. Challenges facing efficient trade studies include poor tool interoperability, lack of simulation coordination (design parameters) and requirements flowdown. A recent trend toward Model Based System Engineering (MBSE) includes flexible architecture definition, program documentation, requirements traceability and system engineering reuse. As a new domain MBSE still lacks governing standards and commonly accepted frameworks. This paper proposes a framework for efficient architecture definition using MBSE in conjunction with Domain Specific simulation to evaluate trade studies. A general framework is provided followed with a specific example including a method for designing a trade study, defining candidate architectures, planning simulations to fulfill requirements and finally a weighted decision analysis to optimize system objectives.

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

Narratives about illness and medication: a neglected theme/new methodology within pharmacy practice research. Part II: medication narratives in practice.

PubMed

Ryan, Kath; Bissell, Paul; Morecroft, Charles

2007-08-01

Part 2 of this paper aims to provide a methodological framework for the study of medication narratives, including a semi-structured interview guide and suggested method of analysis, in an attempt to aid the development of narrative scholarship within pharmacy practice research. Examples of medication narratives are provided to illustrate their diversity and usefulness. The framework is derived from the work of other researchers and adapted for our specific purpose. It comes from social psychology, narrative psychology, narrative anthropology, sociology and critical theory and fits within the social constructionist paradigm. The suggested methods of analysis could broadly be described as narrative analysis and discourse analysis. Examples of medication narratives are chosen from a variety of sources and brief interpretations are presented by way of illustration. Narrative analysis, a neglected area of research in pharmacy practice, has the potential to provide new understanding about how people relate to their medicines, how pharmacists are engaged in producing narratives and the importance of narrative in the education of students. IMPACT OF THE ARTICLE: This article aims to have the following impact on pharmacy practice research: Innovative approach to researching and conceptualising the use of medicines. Introduction of a new theoretical perspective and methodology. Incorporation of social science research methods into pharmacy practice research. Development of narrative scholarship within pharmacy.

My-Forensic-Loci-queries (MyFLq) framework for analysis of forensic STR data generated by massive parallel sequencing.

PubMed

Van Neste, Christophe; Vandewoestyne, Mado; Van Criekinge, Wim; Deforce, Dieter; Van Nieuwerburgh, Filip

2014-03-01

Forensic scientists are currently investigating how to transition from capillary electrophoresis (CE) to massive parallel sequencing (MPS) for analysis of forensic DNA profiles. MPS offers several advantages over CE such as virtually unlimited multiplexy of loci, combining both short tandem repeat (STR) and single nucleotide polymorphism (SNP) loci, small amplicons without constraints of size separation, more discrimination power, deep mixture resolution and sample multiplexing. We present our bioinformatic framework My-Forensic-Loci-queries (MyFLq) for analysis of MPS forensic data. For allele calling, the framework uses a MySQL reference allele database with automatically determined regions of interest (ROIs) by a generic maximal flanking algorithm which makes it possible to use any STR or SNP forensic locus. Python scripts were designed to automatically make allele calls starting from raw MPS data. We also present a method to assess the usefulness and overall performance of a forensic locus with respect to MPS, as well as methods to estimate whether an unknown allele, which sequence is not present in the MySQL database, is in fact a new allele or a sequencing error. The MyFLq framework was applied to an Illumina MiSeq dataset of a forensic Illumina amplicon library, generated from multilocus STR polymerase chain reaction (PCR) on both single contributor samples and multiple person DNA mixtures. Although the multilocus PCR was not yet optimized for MPS in terms of amplicon length or locus selection, the results show excellent results for most loci. The results show a high signal-to-noise ratio, correct allele calls, and a low limit of detection for minor DNA contributors in mixed DNA samples. Technically, forensic MPS affords great promise for routine implementation in forensic genomics. The method is also applicable to adjacent disciplines such as molecular autopsy in legal medicine and in mitochondrial DNA research. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Multi-object segmentation framework using deformable models for medical imaging analysis.

PubMed

Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

2016-08-01

Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results.

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Data-driven discovery of partial differential equations

PubMed Central

Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2017-01-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable. PMID:28508044

Validation of Skills, Knowledge and Experience in Lifelong Learning in Europe

ERIC Educational Resources Information Center

Ogunleye, James

2012-01-01

The paper examines systems of validation of skills and experience as well as the main methods/tools currently used for validating skills and knowledge in lifelong learning. The paper uses mixed methods--a case study research and content analysis of European Union policy documents and frameworks--as a basis for this research. The selection of the…

A Framework for Understanding Community Colleges' Organizational Capacity for Data Use: A Convergent Parallel Mixed Methods Study

ERIC Educational Resources Information Center

Kerrigan, Monica Reid

2014-01-01

This convergent parallel design mixed methods case study of four community colleges explores the relationship between organizational capacity and implementation of data-driven decision making (DDDM). The article also illustrates purposive sampling using replication logic for cross-case analysis and the strengths and weaknesses of quantitizing…

Comparison of Information Dissemination Methods in Inle Lake: A Lesson for Reconsidering Framework for Environmental Education Strategies

ERIC Educational Resources Information Center

Oo, Htun Naing; Sutheerawatthana, Pitch; Minato, Takayuki

2010-01-01

This article analyzes the practice of information dissemination regarding pesticide usage in floating gardening in a rural area. The analysis reveals reasons why the current information dissemination methods employed by relevant stakeholders do not work. It then puts forward a proposition that information sharing within organizations of and among…

An Approach for Selecting a Theoretical Framework for the Evaluation of Training Programs

ERIC Educational Resources Information Center

Tasca, Jorge Eduardo; Ensslin, Leonardo; Ensslin, Sandra Rolim; Alves, Maria Bernardete Martins

2010-01-01

Purpose: This research paper proposes a method for selecting references related to a research topic, and seeks to exemplify it for the case of a study evaluating training programs. The method is designed to identify references with high academic relevance in databases accessed via the internet, using a bibliometric analysis to sift the selected…

Rethinking a Case Study Method in Educational Research: A Comparative Analysis Method in Qualitative Research

ERIC Educational Resources Information Center

Murakami, Yusuke

2013-01-01

There are two types of qualitative research that analyze a small number of cases or a single case: idiographic differentiation and nomothetic/generalization. There are few case studies of generalization. This is because theoretical inclination is weak in the field of education, and the binary framework of quantitative versus qualitative research…

Mindful Leaders in Highly Effective Schools: A Mixed-Method Application of Hoy's M-Scale

ERIC Educational Resources Information Center

Kearney, W. Sean; Kelsey, Cheryl; Herrington, David

2013-01-01

This article presents a mixed-method study utilizing teacher ratings of principal mindfulness from 149 public schools in Texas and follow-up qualitative data analysis through semi-structured interviews conducted with the top 10 percent of princeipals identified as mindful. This research is based on the theoretical framework of mindfulness as…

Creative strategies of businesses with the holistic eigensolution in manufacturing industries

NASA Astrophysics Data System (ADS)

Zeichen, Gerfried; Huray, Paul G.

1998-10-01

It is a mission of this contribution to recognize and synthesize all the efforts in industry and in management science to strengthen our techniques and tools for successfully solving increasingly complex leadership problems in manufacturing industries. With the high standard of the work sharing method--the so called Taylorism principle--for cost efficient and mass production, invented at the beginning of the 20th century and the opening of the world market for global sales of goods and services a gigantic progress in living standards was reached. But at the beginning of the 21st century we are needing new ideas and methods for the guidance of overcoming increasing complexity. The holistic eigensolution presents a new operational framework for viewing and controlling the behavior of businesses. In contrast to the traditional process for viewing complex business systems through the intricate analysis of every part of that system, the authors have employed a technique used by physicists to understand the characteristic of `eigen' behaviors of complex physical systems. This method of systems analysis is achieved by observing interactions between the parts in a whole. This kind of analysis has a rigorous mathematical foundation in the physical world and it can be employed to understand most natural phenomena. Within a holistic framework, the observer is challenged to view the system form just the right perspective so that characteristic eigenmodes reveal themselves. The conclusion of the article describes why exactly the intelligent manufacturing science--especially in a broader sense--has the responsibility and chance to develop the holistic eigensolution framework as a Taylorism II-principle for the 21st century.

Power Spectral Density Error Analysis of Spectral Subtraction Type of Speech Enhancement Methods

NASA Astrophysics Data System (ADS)

Händel, Peter

2006-12-01

A theoretical framework for analysis of speech enhancement algorithms is introduced for performance assessment of spectral subtraction type of methods. The quality of the enhanced speech is related to physical quantities of the speech and noise (such as stationarity time and spectral flatness), as well as to design variables of the noise suppressor. The derived theoretical results are compared with the outcome of subjective listening tests as well as successful design strategies, performed by independent research groups.

Distribution Grid Integration Costs Under High PV Penetrations Workshop |

Science.gov Websites

grids. These distribution grid integration costs are one component of a complete cost-benefit analysis . Engaging stakeholders to coalesce around transparent and mutually acceptable frameworks for cost-benefit -voltage only). In particular, there was be a focus on methods most appropriate for cost-benefit analysis

The Nexus of Place and Finance in the Analysis of Educational Attainment: A Spatial Econometric Approach

ERIC Educational Resources Information Center

Sutton, Farah

2012-01-01

This study examines the spatial distribution of educational attainment and then builds upon current predictive frameworks for understanding patterns of educational attainment by applying a spatial econometric method of analysis. The research from this study enables a new approach to the policy discussion on how to improve educational attainment…

Comparing the Effectiveness of SPSS and EduG Using Different Designs for Generalizability Theory

ERIC Educational Resources Information Center

Teker, Gulsen Tasdelen; Guler, Nese; Uyanik, Gulden Kaya

2015-01-01

Generalizability theory (G theory) provides a broad conceptual framework for social sciences such as psychology and education, and a comprehensive construct for numerous measurement events by using analysis of variance, a strong statistical method. G theory, as an extension of both classical test theory and analysis of variance, is a model which…

The Use of Gap Analysis to Increase Student Completion Rates at Travelor Adult School

ERIC Educational Resources Information Center

Gil, Blanca Estela

2013-01-01

This project applied the gap analysis problem-solving framework (Clark & Estes, 2008) in order to help develop strategies to increase completion rates at Travelor Adult School. The purpose of the study was to identify whether the knowledge, motivation and organization barriers were contributing to the identified gap. A mixed method approached…

Validation of Competencies in E-Portfolios: A Qualitative Analysis

ERIC Educational Resources Information Center

Zawacki-Richter, Olaf; Hanft, Anke; Baecker, Eva Maria

2011-01-01

This paper uses the example of an Internet-based advanced studies course to show how the portfolio method, as a competence-based form of examination, can be integrated in a blended learning design. Within the framework of a qualitative analysis of project portfolios, we examined which competencies are documented and how students reflected on their…

A Comparative Analysis of Numbers and Biology Content Domains between Turkey and the USA

ERIC Educational Resources Information Center

Incikabi, Lutfi; Ozgelen, Sinan; Tjoe, Hartono

2012-01-01

This study aimed to compare Mathematics and Science programs focusing on TIMSS content domains of Numbers and Biology that produced the largest achievement gap among students from Turkey and the USA. Specifically, it utilized the content analysis method within Turkish and New York State (NYS) frameworks. The procedures of study included matching…

Content Analysis of Curriculum-Related Studies in Turkey between 2000 and 2014

ERIC Educational Resources Information Center

Aksan, Elif; Baki, Adnan

2017-01-01

This study aims to carry out a content analysis determining the general framework of studies related to curriculum. For this purpose, 154 curriculum-related studies carried out in Turkey between 2000 and 2014 were examined in terms of year, sample, method, data collection technique, purpose, and result. The most studies related to curriculum were…

Investigating the potential of metal-organic framework material as an adsorbent for matrix solid-phase dispersion extraction of pesticides during analysis of dehydrated Hyptis pectinata medicinal plant by GC/MS.

PubMed

Aquino, Adriano; Ferreira, Jordana Alves; Navickiene, Sandro; Wanderley, Kaline A; de Sá, Gilberto F; Júnior, Severino A

2012-01-01

Metal-organic frameworks aluminum terephthalate MIL-53 and Cu-benzene-1,3,5-tricarboxylate (BTC) were tested for extraction of pyrimethanil, ametryn, dichlofluanid, tetraconazole, flumetralin, kresoximmethyl, and tebuconazole from the medicinal plant Hyptis pectinata, with analysis using GC/MS in the selected ion monitoring mode. Experiments carried out at different fortification levels (0.1, 0.5, and 1.0 microg/g) resulted in recoveries in the range 61 to 107% with RSD values between 3 and 12% for the metal-organic framework materials. Detection and quantification limits ranged from 0.02 to 0.07 and 0.05 to 0.1 microg/g, respectively, for the different pesticides studied. The method developed was linear over the range tested (0.04-20.0 microg/g), with correlation coefficients ranging from 0.9987 to 0.9998. Comparison of MIL-53 and Cu-BTC with C18-bonded silica showed good performance of the MIL-53 metal-organic framework as a sorbent for the pesticides tested.

Registering and Analyzing Rat fMRI Data in the Stereotaxic Framework by Exploiting Intrinsic Anatomical Features

PubMed Central

Lu, Hanbing; Scholl, Clara A.; Zuo, Yantao; Demny, Steven; Rea, William; Stein, Elliot A.; Yang, Yihong

2009-01-01

The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing fMRI data into common space in rodent functional magnetic resonance imaging (fMRI) studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking, and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region-of-interest (ROI) analysis. This technique is evaluated from a group of rats (n = 11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 μm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the AFNI framework and can be readily extended to other studies. PMID:19608368

Data-driven freeway performance evaluation framework for project prioritization and decision making.

DOT National Transportation Integrated Search

2017-01-01

This report describes methods that potentially can be incorporated into the performance monitoring and planning processes for freeway performance evaluation and decision making. Reliability analysis was conducted on the selected I-15 corridor by empl...

Data-driven freeway performance evaluation framework for project prioritization and decision making.

DOT National Transportation Integrated Search

2015-03-01

This report describes methods that potentially can be incorporated into the performance monitoring and planning : processes for freeway performance evaluation and decision making. Reliability analysis is conducted on the selected : I-15 corridor by e...

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

NASA Astrophysics Data System (ADS)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis shows that terrestrial carbon and water cycle simulations in monsoon Asia were greatly improved, and the use of multiple satellite observations with this framework is an effective way for improving terrestrial biosphere models.

Multifidelity Analysis and Optimization for Supersonic Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Willcox, Karen; March, Andrew; Haas, Alex; Rajnarayan, Dev; Kays, Cory

2010-01-01

Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.

Environmental impact assessment of transportation projects: An analysis using an integrated GIS, remote sensing, and spatial modeling approach

NASA Astrophysics Data System (ADS)

El-Gafy, Mohamed Anwar

Transportation projects will have impact on the environment. The general environmental pollution and damage caused by roads is closely associated with the level of economic activity. Although Environmental Impact Assessments (EIAs) are dependent on geo-spatial information in order to make an assessment, there are no rules per se how to conduct an environmental assessment. Also, the particular objective of each assessment is dictated case-by-case, based on what information and analyses are required. The conventional way of Environmental Impact Assessment (EIA) study is a time consuming process because it has large number of dependent and independent variables which have to be taken into account, which also have different consequences. With the emergence of satellite remote sensing technology and Geographic Information Systems (GIS), this research presents a new framework for the analysis phase of the Environmental Impact Assessment (EIA) for transportation projects based on the integration between remote sensing technology, geographic information systems, and spatial modeling. By integrating the merits of the map overlay method and the matrix method, the framework analyzes comprehensively the environmental vulnerability around the road and its impact on the environment. This framework is expected to: (1) improve the quality of the decision making process, (2) be applied both to urban and inter-urban projects, regardless of transport mode, and (3) present the data and make the appropriate analysis to support the decision of the decision-makers and allow them to present these data to the public hearings in a simple manner. Case studies, transportation projects in the State of Florida, were analyzed to illustrate the use of the decision support framework and demonstrate its capabilities. This cohesive and integrated system will facilitate rational decisions through cost effective coordination of environmental information and data management that can be tailored to specific projects. The framework would facilitate collecting, organizing, analyzing, archiving, and coordinating the information and data necessary to support technical and policy transportation decisions.

A Mixed-Methods Research Framework for Healthcare Process Improvement.

PubMed

Bastian, Nathaniel D; Munoz, David; Ventura, Marta

2016-01-01

The healthcare system in the United States is spiraling out of control due to ever-increasing costs without significant improvements in quality, access to care, satisfaction, and efficiency. Efficient workflow is paramount to improving healthcare value while maintaining the utmost standards of patient care and provider satisfaction in high stress environments. This article provides healthcare managers and quality engineers with a practical healthcare process improvement framework to assess, measure and improve clinical workflow processes. The proposed mixed-methods research framework integrates qualitative and quantitative tools to foster the improvement of processes and workflow in a systematic way. The framework consists of three distinct phases: 1) stakeholder analysis, 2a) survey design, 2b) time-motion study, and 3) process improvement. The proposed framework is applied to the pediatric intensive care unit of the Penn State Hershey Children's Hospital. The implementation of this methodology led to identification and categorization of different workflow tasks and activities into both value-added and non-value added in an effort to provide more valuable and higher quality patient care. Based upon the lessons learned from the case study, the three-phase methodology provides a better, broader, leaner, and holistic assessment of clinical workflow. The proposed framework can be implemented in various healthcare settings to support continuous improvement efforts in which complexity is a daily element that impacts workflow. We proffer a general methodology for process improvement in a healthcare setting, providing decision makers and stakeholders with a useful framework to help their organizations improve efficiency. Published by Elsevier Inc.

Document co-citation analysis to enhance transdisciplinary research

PubMed Central

Trujillo, Caleb M.; Long, Tammy M.

2018-01-01

Specialized and emerging fields of research infrequently cross disciplinary boundaries and would benefit from frameworks, methods, and materials informed by other fields. Document co-citation analysis, a method developed by bibliometric research, is demonstrated as a way to help identify key literature for cross-disciplinary ideas. To illustrate the method in a useful context, we mapped peer-recognized scholarship related to systems thinking. In addition, three procedures for validation of co-citation networks are proposed and implemented. This method may be useful for strategically selecting information that can build consilience about ideas and constructs that are relevant across a range of disciplines. PMID:29308433

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials.

PubMed

Daza, Eric J

2018-02-01

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful. Schattauer GmbH.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Program budgeting and marginal analysis: a case study in chronic airflow limitation.

PubMed

Crockett, A; Cranston, J; Moss, J; Scown, P; Mooney, G; Alpers, J

1999-01-01

Program budgeting and marginal analysis is a method of priority-setting in health care. This article describes how this method was applied to the management of a disease-specific group, chronic airflow limitation. A sub-program flow chart clarified the major cost drivers. After assessment of the technical efficiency of the sub-programs and careful and detailed analysis, incremental and decremental wish lists of activities were established. Program budgeting and marginal analysis provides a framework for rational resource allocation. The nurturing of a vigorous program management group, with members representing all participants in the process (including patients/consumers), is the key to a successful outcome.

A Causal Inference Analysis of the Effect of Wildland Fire ...

EPA Pesticide Factsheets

Wildfire smoke is a major contributor to ambient air pollution levels. In this talk, we develop a spatio-temporal model to estimate the contribution of fire smoke to overall air pollution in different regions of the country. We combine numerical model output with observational data within a causal inference framework. Our methods account for aggregation and potential bias of the numerical model simulation, and address uncertainty in the causal estimates. We apply the proposed method to estimation of ozone and fine particulate matter from wildland fires and the impact on health burden assessment. We develop a causal inference framework to assess contributions of fire to ambient PM in the presence of spatial interference.

Multidisciplinary Optimization Branch Experience Using iSIGHT Software

NASA Technical Reports Server (NTRS)

Padula, S. L.; Korte, J. J.; Dunn, H. J.; Salas, A. O.

1999-01-01

The Multidisciplinary Optimization (MDO) Branch at NASA Langley Research Center is investigating frameworks for supporting multidisciplinary analysis and optimization research. An optimization framework call improve the design process while reducing time and costs. A framework provides software and system services to integrate computational tasks and allows the researcher to concentrate more on the application and less on the programming details. A framework also provides a common working environment and a full range of optimization tools, and so increases the productivity of multidisciplinary research teams. Finally, a framework enables staff members to develop applications for use by disciplinary experts in other organizations. Since the release of version 4.0, the MDO Branch has gained experience with the iSIGHT framework developed by Engineous Software, Inc. This paper describes experiences with four aerospace applications: (1) reusable launch vehicle sizing, (2) aerospike nozzle design, (3) low-noise rotorcraft trajectories, and (4) acoustic liner design. All applications have been successfully tested using the iSIGHT framework, except for the aerospike nozzle problem, which is in progress. Brief overviews of each problem are provided. The problem descriptions include the number and type of disciplinary codes, as well as all estimate of the multidisciplinary analysis execution time. In addition, the optimization methods, objective functions, design variables, and design constraints are described for each problem. Discussions on the experience gained and lessons learned are provided for each problem. These discussions include the advantages and disadvantages of using the iSIGHT framework for each case as well as the ease of use of various advanced features. Potential areas of improvement are identified.

Modeling Criminal Activity in Urban Landscapes

NASA Astrophysics Data System (ADS)

Brantingham, Patricia; Glässer, Uwe; Jackson, Piper; Vajihollahi, Mona

Computational and mathematical methods arguably have an enormous potential for serving practical needs in crime analysis and prevention by offering novel tools for crime investigations and experimental platforms for evidence-based policy making. We present a comprehensive formal framework and tool support for mathematical and computational modeling of criminal behavior to facilitate systematic experimental studies of a wide range of criminal activities in urban environments. The focus is on spatial and temporal aspects of different forms of crime, including opportunistic and serial violent crimes. However, the proposed framework provides a basis to push beyond conventional empirical research and engage the use of computational thinking and social simulations in the analysis of terrorism and counter-terrorism.

A Clifford analysis approach to superspace

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

Bie, H. de; Sommen, F.

A new framework for studying superspace is given, based on methods from Clifford analysis. This leads to the introduction of both orthogonal and symplectic Clifford algebra generators, allowing for an easy and canonical introduction of a super-Dirac operator, a super-Laplace operator and the like. This framework is then used to define a super-Hodge coderivative, which, together with the exterior derivative, factorizes the Laplace operator. Finally both the cohomology of the exterior derivative and the homology of the Hodge operator on the level of polynomial-valued super-differential forms are studied. This leads to some interesting graphical representations and provides a better insightmore » in the definition of the Berezin-integral.« less

Integrated Design Engineering Analysis (IDEA) Environment Automated Generation of Structured CFD Grids using Topology Methods

NASA Technical Reports Server (NTRS)

Kamhawi, Hilmi N.

2012-01-01

This report documents the work performed from March 2010 to March 2012. The Integrated Design and Engineering Analysis (IDEA) environment is a collaborative environment based on an object-oriented, multidisciplinary, distributed framework using the Adaptive Modeling Language (AML) as a framework and supporting the configuration design and parametric CFD grid generation. This report will focus on describing the work in the area of parametric CFD grid generation using novel concepts for defining the interaction between the mesh topology and the geometry in such a way as to separate the mesh topology from the geometric topology while maintaining the link between the mesh topology and the actual geometry.

Micro-scale blood particulate dynamics using a non-uniform rational B-spline-based isogeometric analysis.

PubMed

Chivukula, V; Mousel, J; Lu, J; Vigmostad, S

2014-12-01

The current research presents a novel method in which blood particulates - biconcave red blood cells (RBCs) and spherical cells are modeled using isogeometric analysis, specifically Non-Uniform Rational B-Splines (NURBS) in 3-D. The use of NURBS ensures that even with a coarse representation, the geometry of the blood particulates maintains an accurate description when subjected to large deformations. The fundamental advantage of this method is the coupling of the geometrical description and the stress analysis of the cell membrane into a single, unified framework. Details on the modeling approach, implementation of boundary conditions and the membrane mechanics analysis using isogeometric modeling are presented, along with validation cases for spherical and biconcave cells. Using NURBS - based isogeometric analysis, the behavior of individual cells in fluid flow is presented and analyzed in different flow regimes using as few as 176 elements for a spherical cell and 220 elements for a biconcave RBC. This work provides a framework for modeling a large number of 3-D deformable biological cells, each with its own geometric description and membrane properties. To the best knowledge of the authors, this is the first application of the NURBS - based isogeometric analysis to model and simulate blood particulates in flow in 3D. Copyright © 2014 John Wiley & Sons, Ltd.

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies.

PubMed

Lyseen, A K; Nøhr, C; Sørensen, E M; Gudes, O; Geraghty, E M; Shaw, N T; Bivona-Tellez, C

2014-08-15

The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health.

A tool for optimization of the production and user analysis on the Grid, C. Grigoras for the ALICE Collaboration

NASA Astrophysics Data System (ADS)

Grigoras, Costin; Carminati, Federico; Vladimirovna Datskova, Olga; Schreiner, Steffen; Lee, Sehoon; Zhu, Jianlin; Gheata, Mihaela; Gheata, Andrei; Saiz, Pablo; Betev, Latchezar; Furano, Fabrizio; Mendez Lorenzo, Patricia; Grigoras, Alina Gabriela; Bagnasco, Stefano; Peters, Andreas Joachim; Saiz Santos, Maria Dolores

2011-12-01

With the LHC and ALICE entering a full operation and production modes, the amount of Simulation and RAW data processing and end user analysis computational tasks are increasing. The efficient management of all these tasks, all of which have large differences in lifecycle, amounts of processed data and methods to analyze the end result, required the development and deployment of new tools in addition to the already existing Grid infrastructure. To facilitate the management of the large scale simulation and raw data reconstruction tasks, ALICE has developed a production framework called a Lightweight Production Manager (LPM). The LPM is automatically submitting jobs to the Grid based on triggers and conditions, for example after a physics run completion. It follows the evolution of the job and publishes the results on the web for worldwide access by the ALICE physicists. This framework is tightly integrated with the ALICE Grid framework AliEn. In addition to the publication of the job status, LPM is also allowing a fully authenticated interface to the AliEn Grid catalogue, to browse and download files, and in the near future will provide simple types of data analysis through ROOT plugins. The framework is also being extended to allow management of end user jobs.

Scalable web services for the PSIPRED Protein Analysis Workbench.

PubMed

Buchan, Daniel W A; Minneci, Federico; Nugent, Tim C O; Bryson, Kevin; Jones, David T

2013-07-01

Here, we present the new UCL Bioinformatics Group's PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.

Development and application of a framework to assess community nutritionists' use of environmental strategies to prevent obesity.

PubMed

Lu, Angela H; Dickin, Katherine; Dollahite, Jamie

2014-01-01

To develop and apply a framework exploring the extent of involvement in promoting environmental changes to prevent obesity by a group of nutrition educators (NE). Cross-sectional, mixed methods: qualitative interviews informed framework development; survey applied framework to describe NE's involvement in environmental changes. Cooperative Extension in New York State. Interviewees (n = 7) selected to vary in environmental change activities and rural/urban location. Survey response rate was 100% (n = 58). Dimensions and degree of NE's involvement in promoting environmental change. Thematic analysis of qualitative data, triangulated with descriptive analyses of NE's performance of tasks in various settings. NE's promotion of environmental changes was characterized using framework based on settings and tasks, dimensions that emerged from qualitative analysis. NE's actions varied across these dimensions and ranged from low to high intensity of collaboration and leadership for environmental change. Most NE surveyed reported actions limited to providing information and recommendations on healthy eating and physical activity. Few reported intensive engagement in developing, implementing, and evaluating plans to change environments for obesity prevention. Framework identifies the levels of engagement in promoting environmental changes and supports future research and practice of community nutrition professionals by providing a roadmap for assessing their involvement on multiple levels to prevent obesity. Copyright © 2014 Society for Nutrition Education and Behavior. All rights reserved.

What Touched Your Heart? Collaborative Story Analysis Emerging From an Apsáalooke Cultural Context

PubMed Central

Hallett, John; Held, Suzanne; McCormick, Alma Knows His Gun; Simonds, Vanessa; Bird, Sloane Real; Martin, Christine; Simpson, Colleen; Schure, Mark; Turnsplenty, Nicole; Trottier, Coleen

2017-01-01

Community-based participatory research and decolonizing research share some recommendations for best practices for conducting research. One commonality is partnering on all stages of research; co-developing methods of data analysis is one stage with a deficit of partnering examples. We present a novel community-based and developed method for analyzing qualitative data within an Indigenous health study and explain incompatibilities of existing methods for our purposes and community needs. We describe how we explored available literature, received counsel from community Elders and experts in the field, and collaboratively developed a data analysis method consonant with community values. The method of analysis, in which interview/story remained intact, team members received story, made meaning through discussion, and generated a conceptual framework to inform intervention development, is detailed. We offer the development process and method as an example for researchers working with communities who want to keep stories intact during qualitative data analysis. PMID:27659019

A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye using Proper Orthogonal Decomposition

PubMed Central

Balasubramanian, Madhusudhanan; Žabić, Stanislav; Bowd, Christopher; Thompson, Hilary W.; Wolenski, Peter; Iyengar, S. Sitharama; Karki, Bijaya B.; Zangwill, Linda M.

2009-01-01

Glaucoma is the second leading cause of blindness worldwide. Often the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this work, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L1 and L2 norms, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUC) were used to compare the diagnostic performance of the POD induced parameters with the parameters of Topographic Change Analysis (TCA) method. The IMED and L2 norm parameters in the POD framework provided the highest AUC of 0.94 at 10° field of imaging and 0.91 at 15° field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88 respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management. PMID:19369163

Graduate Attributes for Master's Programs in Health Services and Policy Research: Results of a National Consultation

PubMed Central

Morgan, Steve; Orr, Karen; Mah, Catherine

2010-01-01

Objective: Our objective was to identify desirable attributes to be developed through graduate training in health services and policy research (HSPR) by identifying the knowledge, skills and abilities thought to be keys to success in HSPR-related careers. We aimed for a framework clear enough to serve as a touchstone for HSPR training programs across Canada yet flexible enough to permit diversity of specialization across and within those programs. Methods: Our approach involved several stages of data collection and analysis: a review of literature; telephone interviews with opinion leaders; online surveys of HSPR students, recent graduates and employers; an invitational workshop; and an interactive panel at a national conference. Our final framework was arrived at through an iterative process of thematic analysis, reflection on invited feedback from consultation participants and triangulation with existing competency frameworks. Results: Our final result was a framework that identifies traits, knowledge and abilities of master's-level graduates who are capable of fostering health system improvement through planning, management, analysis or monitoring that is informed by credible evidence and relevant theory. These attributes are organized into three levels: generic graduate attributes, knowledge related to health and health systems and, finally, attributes related to the application of knowledge for health system improvement. The HSPR-specific attributes include not only an understanding of HSPR theories and methods but also the skills related to the practical application of knowledge in the complex environments of health system decision-making and healthcare policy. Conclusion: Master's-level HSPR training programs should prepare students to pose and seek answers to important questions and provide them with the skills necessary to apply their knowledge within complex decision-making environments. PMID:21804839

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

NASA Astrophysics Data System (ADS)

Sharma, Sanjib

2017-08-01

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.

Two-stage Framework for a Topology-Based Projection and Visualization of Classified Document Collections

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

Oesterling, Patrick; Scheuermann, Gerik; Teresniak, Sven

During the last decades, electronic textual information has become the world's largest and most important information source available. People have added a variety of daily newspapers, books, scientific and governmental publications, blogs and private messages to this wellspring of endless information and knowledge. Since neither the existing nor the new information can be read in its entirety, computers are used to extract and visualize meaningful or interesting topics and documents from this huge information clutter. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topological analysis of high dimensional document point cloudsmore » given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To show the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections.« less

"Explosive" synthesis of metal-formate frameworks for methane capture: an experimental and computational study.

PubMed

Liu, Xiao-Wei; Guo, Ya; Tao, Andi; Fischer, Michael; Sun, Tian-Jun; Moghadam, Peyman Z; Fairen-Jimenez, David; Wang, Shu-Dong

2017-10-17

In this work, we show a solvent-free "explosive" synthesis (SFES) method for the ultrafast and low-cost synthesis of metal-formate frameworks (MFFs). A combination of experiments and in-depth molecular modelling analysis - using grand canonical Monte Carlo (GCMC) simulations - of the adsorption performance of the synthesized nickel-formate framework (Ni-FA) revealed extremely high quality products with permanent porosity, prominent CH 4 /N 2 selectivity (ca. 6.0), and good CH 4 adsorption capacity (ca. 0.80 mmol g -1 or 33.97 cm 3 cm -3 ) at 1 bar and 298 K. This performance is superior to those of many other state-of-the-art porous materials.

Using structural equation modeling for network meta-analysis.

PubMed

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison. SEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.

Screening of groundwater remedial alternatives for brownfield sites: a comprehensive method integrated MCDA with numerical simulation.

PubMed

Li, Wei; Zhang, Min; Wang, Mingyu; Han, Zhantao; Liu, Jiankai; Chen, Zhezhou; Liu, Bo; Yan, Yan; Liu, Zhu

2018-06-01

Brownfield sites pollution and remediation is an urgent environmental issue worldwide. The screening and assessment of remedial alternatives is especially complex owing to its multiple criteria that involves technique, economy, and policy. To help the decision-makers selecting the remedial alternatives efficiently, the criteria framework conducted by the U.S. EPA is improved and a comprehensive method that integrates multiple criteria decision analysis (MCDA) with numerical simulation is conducted in this paper. The criteria framework is modified and classified into three categories: qualitative, semi-quantitative, and quantitative criteria, MCDA method, AHP-PROMETHEE (analytical hierarchy process-preference ranking organization method for enrichment evaluation) is used to determine the priority ranking of the remedial alternatives and the solute transport simulation is conducted to assess the remedial efficiency. A case study was present to demonstrate the screening method in a brownfield site in Cangzhou, northern China. The results show that the systematic method provides a reliable way to quantify the priority of the remedial alternatives.

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

NASA Astrophysics Data System (ADS)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

BEATBOX v1.0: Background Error Analysis Testbed with Box Models

NASA Astrophysics Data System (ADS)

Knote, Christoph; Barré, Jérôme; Eckl, Max

2018-02-01

The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

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

Friese, Ryan; Khemka, Bhavesh; Maciejewski, Anthony A

Rising costs of energy consumption and an ongoing effort for increases in computing performance are leading to a significant need for energy-efficient computing. Before systems such as supercomputers, servers, and datacenters can begin operating in an energy-efficient manner, the energy consumption and performance characteristics of the system must be analyzed. In this paper, we provide an analysis framework that will allow a system administrator to investigate the tradeoffs between system energy consumption and utility earned by a system (as a measure of system performance). We model these trade-offs as a bi-objective resource allocation problem. We use a popular multi-objective geneticmore » algorithm to construct Pareto fronts to illustrate how different resource allocations can cause a system to consume significantly different amounts of energy and earn different amounts of utility. We demonstrate our analysis framework using real data collected from online benchmarks, and further provide a method to create larger data sets that exhibit similar heterogeneity characteristics to real data sets. This analysis framework can provide system administrators with insight to make intelligent scheduling decisions based on the energy and utility needs of their systems.« less

Implementation of the Veder contact method in daily nursing home care for people with dementia: a process analysis according to the RE-AIM framework.

PubMed

Boersma, Petra; van Weert, Julia C M; van Meijel, Berno; Dröes, Rose-Marie

2017-02-01

To perform a process analysis of the implementation of the Veder contact method for gaining insight into factors that influence successful implementation. Research showed that the original Veder method, which is a 'living-room theatre performance' provided by actors, positively influenced mood and quality of life of people with dementia. Training caregivers to execute such 'performances' and accomplish the same effects as actors proved difficult. However, key elements of the method were considered suitable for application in daily care, resulting in the development of a modified version of the method, named the Veder contact method. The Veder contact method combines elements from existing psychosocial interventions, e.g. reminiscence, validation and neuro-linguistic-programming with theatrical, poetic and musical communication, and applies this into daily care. For this process analysis a multiple case study design was used with the nursing home ward (n = 6) as the unit of analysis. Eight focus groups with caregivers (n = 42) and 12 interviews with stakeholders were held. Using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework, a thematic analysis was conducted. The reach of the intervention (43-86%) and aspects of implementation-effectiveness (e.g. increased experienced reciprocity in contact with residents) facilitated implementation. For adoption and implementation, both facilitators (e.g. development of competences, feasibility of the Veder contact method without requiring extra time investment) and barriers (e.g. insufficient support of management, resistance of caregivers against the Veder contact method, organisational problems) were identified. Little effort was put into maintenance: only one nursing home developed a long-term implementation strategy. The Veder contact method can be applied in daily care without additional time investments. Although adopted by many caregivers, some were reluctant using the Veder contact method. Organisational factors (e.g. staffing and management changes, budget cuts) impeded long-term implementation. The findings from this study can be used for the development of successful implementation strategies for the Veder contact method and other person-centred care methods. © 2016 John Wiley & Sons Ltd.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

NGSANE: a lightweight production informatics framework for high-throughput data analysis.

PubMed

Buske, Fabian A; French, Hugh J; Smith, Martin A; Clark, Susan J; Bauer, Denis C

2014-05-15

The initial steps in the analysis of next-generation sequencing data can be automated by way of software 'pipelines'. However, individual components depreciate rapidly because of the evolving technology and analysis methods, often rendering entire versions of production informatics pipelines obsolete. Constructing pipelines from Linux bash commands enables the use of hot swappable modular components as opposed to the more rigid program call wrapping by higher level languages, as implemented in comparable published pipelining systems. Here we present Next Generation Sequencing ANalysis for Enterprises (NGSANE), a Linux-based, high-performance-computing-enabled framework that minimizes overhead for set up and processing of new projects, yet maintains full flexibility of custom scripting when processing raw sequence data. Ngsane is implemented in bash and publicly available under BSD (3-Clause) licence via GitHub at https://github.com/BauerLab/ngsane. Denis.Bauer@csiro.au Supplementary data are available at Bioinformatics online.

Qualitative Research in Palliative Care: Applications to Clinical Trials Work.

PubMed

Lim, Christopher T; Tadmor, Avia; Fujisawa, Daisuke; MacDonald, James J; Gallagher, Emily R; Eusebio, Justin; Jackson, Vicki A; Temel, Jennifer S; Greer, Joseph A; Hagan, Teresa; Park, Elyse R

2017-08-01

While vast opportunities for using qualitative methods exist within palliative care research, few studies provide practical advice for researchers and clinicians as a roadmap to identify and utilize such opportunities. To provide palliative care clinicians and researchers descriptions of qualitative methodology applied to innovative research questions relative to palliative care research and define basic concepts in qualitative research. Body: We describe three qualitative projects as exemplars to describe major concepts in qualitative analysis of early palliative care: (1) a descriptive analysis of clinician documentation in the electronic health record, (2) a thematic content analysis of palliative care clinician focus groups, and (3) a framework analysis of audio-recorded encounters between patients and clinicians as part of a clinical trial. This study provides a foundation for undertaking qualitative research within palliative care and serves as a framework for use by other palliative care researchers interested in qualitative methodologies.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Diversity training for the community aged care workers: A conceptual framework for evaluation.

PubMed

Appannah, Arti; Meyer, Claudia; Ogrin, Rajna; McMillan, Sally; Barrett, Elizabeth; Browning, Colette

2017-08-01

Older Australians are an increasingly diverse population, with variable characteristics such as culture, sexual orientation, socioeconomic status, and physical capabilities potentially influencing their participation in healthcare. In response, community aged care workers may need to increase skills and uptake of knowledge into practice regarding diversity through appropriate training interventions. Diversity training (DT) programs have traditionally existed in the realm of business, with little research attention devoted to scientifically evaluating the outcomes of training directed at community aged care workers. A DT workshop has been developed for community aged care workers, and this paper focuses on the construction of a formative evaluative framework for the workshop. Key evaluation concepts and measures relating to DT have been identified in the literature and integrated into the framework, focusing on five categories: Training needs analysis; Reactions; Learning outcomes, Behavioural outcomes and Results The use of a mixed methods approach in the framework provides an additional strength, by evaluating long-term behavioural change and improvements in service delivery. As little is known about the effectiveness of DT programs for community aged care workers, the proposed framework will provide an empirical and consistent method of evaluation, to assess their impact on enhancing older people's experience of healthcare. Copyright © 2017 Elsevier Ltd. All rights reserved.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Management of Teacher Scientific-Methodical Work in Vocational Educational Institutions on the Basis of Project-Target Approach

ERIC Educational Resources Information Center

Shakuto, Elena A.; Dorozhkin, Evgenij M.; Kozlova, Anastasia A.

2016-01-01

The relevance of the subject under analysis is determined by the lack of theoretical development of the problem of management of teacher scientific-methodical work in vocational educational institutions based upon innovative approaches in the framework of project paradigm. The purpose of the article is to develop and test a science-based…

Fuzzy robust credibility-constrained programming for environmental management and planning.

PubMed

Zhang, Yimei; Hang, Guohe

2010-06-01

In this study, a fuzzy robust credibility-constrained programming (FRCCP) is developed and applied to the planning for waste management systems. It incorporates the concepts of credibility-based chance-constrained programming and robust programming within an optimization framework. The developed method can reflect uncertainties presented as possibility-density by fuzzy-membership functions. Fuzzy credibility constraints are transformed to the crisp equivalents with different credibility levels, and ordinary fuzzy inclusion constraints are determined by their robust deterministic constraints by setting a-cut levels. The FRCCP method can provide different system costs under different credibility levels (lambda). From the results of sensitivity analyses, the operation cost of the landfill is a critical parameter. For the management, any factors that would induce cost fluctuation during landfilling operation would deserve serious observation and analysis. By FRCCP, useful solutions can be obtained to provide decision-making support for long-term planning of solid waste management systems. It could be further enhanced through incorporating methods of inexact analysis into its framework. It can also be applied to other environmental management problems.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

DOE PAGES

Liao, Huafei N.; Groth, Katrina; Stevens-Adams, Susan

2015-07-29

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification aremore » discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.« less

A framework for interactive visual analysis of heterogeneous marine data in an integrated problem solving environment

NASA Astrophysics Data System (ADS)

Liu, Shuai; Chen, Ge; Yao, Shifeng; Tian, Fenglin; Liu, Wei

2017-07-01

This paper presents a novel integrated marine visualization framework which focuses on processing, analyzing the multi-dimension spatiotemporal marine data in one workflow. Effective marine data visualization is needed in terms of extracting useful patterns, recognizing changes, and understanding physical processes in oceanography researches. However, the multi-source, multi-format, multi-dimension characteristics of marine data pose a challenge for interactive and feasible (timely) marine data analysis and visualization in one workflow. And, global multi-resolution virtual terrain environment is also needed to give oceanographers and the public a real geographic background reference and to help them to identify the geographical variation of ocean phenomena. This paper introduces a data integration and processing method to efficiently visualize and analyze the heterogeneous marine data. Based on the data we processed, several GPU-based visualization methods are explored to interactively demonstrate marine data. GPU-tessellated global terrain rendering using ETOPO1 data is realized and the video memory usage is controlled to ensure high efficiency. A modified ray-casting algorithm for the uneven multi-section Argo volume data is also presented and the transfer function is designed to analyze the 3D structure of ocean phenomena. Based on the framework we designed, an integrated visualization system is realized. The effectiveness and efficiency of the framework is demonstrated. This system is expected to make a significant contribution to the demonstration and understanding of marine physical process in a virtual global environment.

Automatic design of synthetic gene circuits through mixed integer non-linear programming.

PubMed

Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

2012-01-01

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.

Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images.

PubMed

Ferro, Anabela; Mestre, Tânia; Carneiro, Patrícia; Sahumbaiev, Ivan; Seruca, Raquel; Sanches, João M

2017-05-01

In the past decades, there has been an amazing progress in the understanding of the molecular mechanisms of the cell cycle. This has been possible largely due to a better conceptualization of the cycle itself, but also as a consequence of technological advances. Herein, we propose a new fluorescence image-based framework targeted at the identification and segmentation of stained nuclei with the purpose to determine DNA content in distinct cell cycle stages. The method is based on discriminative features, such as total intensity and area, retrieved from in situ stained nuclei by fluorescence microscopy, allowing the determination of the cell cycle phase of both single and sub-population of cells. The analysis framework was built on a modified k-means clustering strategy and refined with a Gaussian mixture model classifier, which enabled the definition of highly accurate classification clusters corresponding to G1, S and G2 phases. Using the information retrieved from area and fluorescence total intensity, the modified k-means (k=3) cluster imaging framework classified 64.7% of the imaged nuclei, as being at G1 phase, 12.0% at G2 phase and 23.2% at S phase. Performance of the imaging framework was ascertained with normal murine mammary gland cells constitutively expressing the Fucci2 technology, exhibiting an overall sensitivity of 94.0%. Further, the results indicate that the imaging framework has a robust capacity to both identify a given DAPI-stained nucleus to its correct cell cycle phase, as well as to determine, with very high probability, true negatives. Importantly, this novel imaging approach is a non-disruptive method that allows an integrative and simultaneous quantitative analysis of molecular and morphological parameters, thus awarding the possibility of cell cycle profiling in cytological and histological samples.

Social Relations and Resident Health in Assisted Living: An Application of the Convoy Model

ERIC Educational Resources Information Center

Perkins, Molly M.; Ball, Mary M.; Kemp, Candace L.; Hollingsworth, Carole

2013-01-01

Purpose: This article, based on analysis of data from a mixed methods study, builds on a growing body of assisted living (AL) research focusing on the link between residents' social relationships and health. A key aim of this analysis, which uses the social convoy model as a conceptual and methodological framework, was to examine the relative…

Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

ERIC Educational Resources Information Center

Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

2007-01-01

Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

Alignment Content Analysis of NAEP 2009 Reading Assessment Analysis Based on Method of Surveys of Enacted Curriculum

ERIC Educational Resources Information Center

Blank, Rolf K.; Smithson, John

2010-01-01

Beginning in summer 2009, the complete set of NAEP student assessment items for grades 4 and 8 Science and Reading 2009 assessments were analyzed for comparison to the National Assessment of Educational Progress (NAEP) Item Specifications which are based on the NAEP Assessment Frameworks for these subjects (National Assessment Governing Board,…

GWASinlps: Nonlocal prior based iterative SNP selection tool for genome-wide association studies.

PubMed

Sanyal, Nilotpal; Lo, Min-Tzu; Kauppi, Karolina; Djurovic, Srdjan; Andreassen, Ole A; Johnson, Valen E; Chen, Chi-Hua

2018-06-19

Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using nonlocal priors in an iterative variable selection framework. We develop a variable selection method, named, iterative nonlocal prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations, and concatenates variable selection within that hierarchy. Extensive simulation studies with SNPs having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error, and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html. Supplementary data are available at Bioinformatics online.

Gender, sexuality and the discursive representation of access and equity in health services literature: implications for LGBT communities

PubMed Central

2011-01-01

Background This article considers how health services access and equity documents represent the problem of access to health services and what the effects of that representation might be for lesbian, gay, bisexual and transgender (LGBT) communities. We conducted a critical discourse analysis on selected access and equity documents using a gender-based diversity framework as determined by two objectives: 1) to identify dominant and counter discourses in health services access and equity literature; and 2) to develop understanding of how particular discourses impact the inclusion, or not, of LGBT communities in health services access and equity frameworks.The analysis was conducted in response to public health and clinical research that has documented barriers to health services access for LGBT communities including institutionalized heterosexism, biphobia, and transphobia, invisibility and lack of health provider knowledge and comfort. The analysis was also conducted as the first step of exploring LGBT access issues in home care services for LGBT populations in Ontario, Canada. Methods A critical discourse analysis of selected health services access and equity documents, using a gender-based diversity framework, was conducted to offer insight into dominant and counter discourses underlying health services access and equity initiatives. Results A continuum of five discourses that characterize the health services access and equity literature were identified including two dominant discourses: 1) multicultural discourse, and 2) diversity discourse; and three counter discourses: 3) social determinants of health (SDOH) discourse; 4) anti-oppression (AOP) discourse; and 5) citizen/social rights discourse. Conclusions The analysis offers a continuum of dominant and counter discourses on health services access and equity as determined from a gender-based diversity perspective. The continuum of discourses offers a framework to identify and redress organizational assumptions about, and ideological commitments to, sexual and gender diversity and health services access and equity. Thus, the continuum of discourses may serve as an important element of a health care organization's access and equity framework for the evaluation of access to good quality care for diverse LGBT populations. More specfically, the analysis offers four important points of consideration in relation to the development of a health services access and equity framework. PMID:21957894

Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

PubMed Central

Boström, Jan; Elger, Christian E.; Mormann, Florian

2016-01-01

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning. PMID:27930664

“Back on Track”: A Mobile App Observational Study Using Apple’s ResearchKit Framework

PubMed Central

Woias, Peter; Suedkamp, Norbert P; Niemeyer, Philipp

2017-01-01

Background In March 2015, Apple Inc announced ResearchKit, a novel open-source framework intended to help medical researchers to easily create apps for medical studies. With the announcement of this framework, Apple presented 5 apps built in a beta phase based on this framework. Objective The objective of this study was to better understand decision making in patients with acute anterior cruciate ligament (ACL) ruptures. Here, we describe the development of a ResearchKit app for this study. Methods A multilanguage observatory study was conducted. At first a suitable research topic, target groups, participating territories, and programming method were carefully identified. The ResearchKit framework was used to program the app. A secure server connection was realized via Secure Sockets Layer. A data storage and security concept separating personal information and study data was proposed. Furthermore, an efficient method to allow multilanguage support and distribute the app in many territories was presented. Ethical implications were considered and taken into account regarding privacy policies. Results An app study based on ResearchKit was developed without comprehensive iPhone Operating System (iOS) development experience. The Apple App Store is a major distribution channel causing significant download rates (>1.200/y) without active recruitment. Preliminary data analysis showed moderate dropout rates and a good quality of data. A total of 180 participants were currently enrolled with 107 actively participating and producing 424 completed surveys in 9 out of 24 months. Conclusions ResearchKit is an easy-to-use framework and powerful tool to create medical studies. Advantages are the modular built, the extensive reach of iOS devices, and the convenient programming environment. PMID:28246069

An eHealth Capabilities Framework for Graduates and Health Professionals: Mixed-Methods Study

PubMed Central

McGregor, Deborah; Keep, Melanie; Janssen, Anna; Spallek, Heiko; Quinn, Deleana; Jones, Aaron; Tseris, Emma; Yeung, Wilson; Togher, Leanne; Solman, Annette; Shaw, Tim

2018-01-01

Background The demand for an eHealth-ready and adaptable workforce is placing increasing pressure on universities to deliver eHealth education. At present, eHealth education is largely focused on components of eHealth rather than considering a curriculum-wide approach. Objective This study aimed to develop a framework that could be used to guide health curriculum design based on current evidence, and stakeholder perceptions of eHealth capabilities expected of tertiary health graduates. Methods A 3-phase, mixed-methods approach incorporated the results of a literature review, focus groups, and a Delphi process to develop a framework of eHealth capability statements. Results Participants (N=39) with expertise or experience in eHealth education, practice, or policy provided feedback on the proposed framework, and following the fourth iteration of this process, consensus was achieved. The final framework consisted of 4 higher-level capability statements that describe the learning outcomes expected of university graduates across the domains of (1) digital health technologies, systems, and policies; (2) clinical practice; (3) data analysis and knowledge creation; and (4) technology implementation and codesign. Across the capability statements are 40 performance cues that provide examples of how these capabilities might be demonstrated. Conclusions The results of this study inform a cross-faculty eHealth curriculum that aligns with workforce expectations. There is a need for educational curriculum to reinforce existing eHealth capabilities, adapt existing capabilities to make them transferable to novel eHealth contexts, and introduce new learning opportunities for interactions with technologies within education and practice encounters. As such, the capability framework developed may assist in the application of eHealth by emerging and existing health care professionals. Future research needs to explore the potential for integration of findings into workforce development programs. PMID:29764794

Analysis of Business Connections Utilizing Theory of Topology of Random Graphs

NASA Astrophysics Data System (ADS)

Trelewicz, Jennifer Q.; Volovich, Igor V.

2006-03-01

A business ecosystem is a system that describes interactions between organizations. In this paper, we build a theoretical framework that defines a model which can be used to analyze the business ecosystem. The basic concepts within the framework are organizations, business connections, and market, that are all defined in the paper. Many researchers analyze the performance and structure of business using the workflow of the business. Our work in business connections answers a different set of questions, concerning the monetary value in the business ecosystem, rather than the task-interaction view that is provided by workflow analysis. We apply methods for analysis of the topology of complex networks, characterized by the concepts of small path length, clustering, and scale-free degree distributions. To model the dynamics of the business ecosystem we analyze the notion of the state of an organization at a given instant of time. We point out that the notion of state in this case is fundamentally different from the concept of state of the system which is used in classical or quantum physics. To describe the state of the organization at a given time one has to know the probability of payments to contracts which in fact depend on the future behavior of the agents on the market. Therefore methods of p-adic analysis are appropriate to explore such a behavior. Microeconomic and macroeconomic factors are indivisible and moreover the actual state of the organization depends on the future. In this framework some simple models are analyzed in detail. Company strategy can be influenced by analysis of models, which can provide a probabilistic understanding of the market, giving degrees of predictability.

A Formal Semantics for the WS-BPEL Recovery Framework

NASA Astrophysics Data System (ADS)

Dragoni, Nicola; Mazzara, Manuel

While current studies on Web services composition are mostly focused - from the technical viewpoint - on standards and protocols, this work investigates the adoption of formal methods for dependable composition. The Web Services Business Process Execution Language (WS-BPEL) - an OASIS standard widely adopted both in academic and industrial environments - is considered as a touchstone for concrete composition languages and an analysis of its ambiguous Recovery Framework specification is offered. In order to show the use of formal methods, a precise and unambiguous description of its (simplified) mechanisms is provided by means of a conservative extension of the π-calculus. This has to be intended as a well known case study providing methodological arguments for the adoption of formal methods in software specification. The aspect of verification is not the main topic of the paper but some hints are given.

Addressing the issue of insufficient information in data-based bridge health monitoring : final report.

DOT National Transportation Integrated Search

2015-11-01

One of the most efficient ways to solve the damage detection problem using the statistical pattern recognition : approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, : damage detection assesse...

Classical Markov Chains: A Unifying Framework for Understanding Avian Reproductive Success

EPA Science Inventory

Traditional methods for monitoring and analysis of avian nesting success have several important shortcomings, including 1) inability to handle multiple classes of nest failure, and 2) inability to provide estimates of annual reproductive success (because birds can, and typically ...

MCMC multilocus lod scores: application of a new approach.

PubMed

George, Andrew W; Wijsman, Ellen M; Thompson, Elizabeth A

2005-01-01

On extended pedigrees with extensive missing data, the calculation of multilocus likelihoods for linkage analysis is often beyond the computational bounds of exact methods. Growing interest therefore surrounds the implementation of Monte Carlo estimation methods. In this paper, we demonstrate the speed and accuracy of a new Markov chain Monte Carlo method for the estimation of linkage likelihoods through an analysis of real data from a study of early-onset Alzheimer's disease. For those data sets where comparison with exact analysis is possible, we achieved up to a 100-fold increase in speed. Our approach is implemented in the program lm_bayes within the framework of the freely available MORGAN 2.6 package for Monte Carlo genetic analysis (http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml).

Design of Mobile Augmented Reality in Health Care Education: A Theory-Driven Framework

PubMed Central

Lilienthal, Anneliese; Shluzas, Lauren Aquino; Masiello, Italo; Zary, Nabil

2015-01-01

Background Augmented reality (AR) is increasingly used across a range of subject areas in health care education as health care settings partner to bridge the gap between knowledge and practice. As the first contact with patients, general practitioners (GPs) are important in the battle against a global health threat, the spread of antibiotic resistance. AR has potential as a practical tool for GPs to combine learning and practice in the rational use of antibiotics. Objective This paper was driven by learning theory to develop a mobile augmented reality education (MARE) design framework. The primary goal of the framework is to guide the development of AR educational apps. This study focuses on (1) identifying suitable learning theories for guiding the design of AR education apps, (2) integrating learning outcomes and learning theories to support health care education through AR, and (3) applying the design framework in the context of improving GPs’ rational use of antibiotics. Methods The design framework was first constructed with the conceptual framework analysis method. Data were collected from multidisciplinary publications and reference materials and were analyzed with directed content analysis to identify key concepts and their relationships. Then the design framework was applied to a health care educational challenge. Results The proposed MARE framework consists of three hierarchical layers: the foundation, function, and outcome layers. Three learning theories—situated, experiential, and transformative learning—provide foundational support based on differing views of the relationships among learning, practice, and the environment. The function layer depends upon the learners’ personal paradigms and indicates how health care learning could be achieved with MARE. The outcome layer analyzes different learning abilities, from knowledge to the practice level, to clarify learning objectives and expectations and to avoid teaching pitched at the wrong level. Suggestions for learning activities and the requirements of the learning environment form the foundation for AR to fill the gap between learning outcomes and medical learners’ personal paradigms. With the design framework, the expected rational use of antibiotics by GPs is described and is easy to execute and evaluate. The comparison of specific expected abilities with the GP personal paradigm helps solidify the GP practical learning objectives and helps design the learning environment and activities. The learning environment and activities were supported by learning theories. Conclusions This paper describes a framework for guiding the design, development, and application of mobile AR for medical education in the health care setting. The framework is theory driven with an understanding of the characteristics of AR and specific medical disciplines toward helping medical education improve professional development from knowledge to practice. Future research will use the framework as a guide for developing AR apps in practice to validate and improve the design framework. PMID:27731839

Prioritizing conservation activities using reserve site selection methods and population viability analysis.

PubMed

Newbold, Stephen C; Siikamäki, Juha

2009-10-01

In recent years a large literature on reserve site selection (RSS) has developed at the interface between ecology, operations research, and environmental economics. Reserve site selection models use numerical optimization techniques to select sites for a network of nature reserves for protecting biodiversity. In this paper, we develop a population viability analysis (PVA) model for salmon and incorporate it into an RSS framework for prioritizing conservation activities in upstream watersheds. We use spawner return data for three closely related salmon stocks in the upper Columbia River basin and estimates of the economic costs of watershed protection from NOAA to illustrate the framework. We compare the relative cost-effectiveness of five alternative watershed prioritization methods, based on various combinations of biological and economic information. Prioritization based on biological benefit-economic cost comparisons and accounting for spatial interdependencies among watersheds substantially outperforms other more heuristic methods. When using this best-performing prioritization method, spending 10% of the cost of protecting all upstream watersheds yields 79% of the biological benefits (increase in stock persistence) from protecting all watersheds, compared to between 20% and 64% for the alternative methods. We also find that prioritization based on either costs or benefits alone can lead to severe reductions in cost-effectiveness.

Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

PubMed Central

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

2015-01-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

PubMed

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

2016-07-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

Evaluating the evaluation of cancer driver genes

PubMed Central

Tokheim, Collin J.; Papadopoulos, Nickolas; Kinzler, Kenneth W.; Vogelstein, Bert; Karchin, Rachel

2016-01-01

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning–based ratiometric approach. We show that the driver genes predicted by each of the eight methods vary widely. Moreover, the P values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false-positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future. PMID:27911828

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

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

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

Statistical testing and power analysis for brain-wide association study.

PubMed

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. All rights reserved.

An Overview of SAL

NASA Technical Reports Server (NTRS)

Bensalem, Saddek; Ganesh, Vijay; Lakhnech, Yassine; Munoz, Cesar; Owre, Sam; Ruess, Harald; Rushby, John; Rusu, Vlad; Saiedi, Hassen; Shankar, N.

2000-01-01

To become practical for assurance, automated formal methods must be made more scalable, automatic, and cost-effective. Such an increase in scope, scale, automation, and utility can be derived from an emphasis on a systematic separation of concerns during verification. SAL (Symbolic Analysis Laboratory) attempts to address these issues. It is a framework for combining different tools to calculate properties of concurrent systems. The heart of SAL is a language, developed in collaboration with Stanford, Berkeley, and Verimag for specifying concurrent systems in a compositional way. Our instantiation of the SAL framework augments PVS with tools for abstraction, invariant generation, program analysis (such as slicing), theorem proving, and model checking to separate concerns as well as calculate properties (i.e., perform, symbolic analysis) of concurrent systems. We. describe the motivation, the language, the tools, their integration in SAL/PAS, and some preliminary experience of their use.

A framework for the meta-analysis of Bland-Altman studies based on a limits of agreement approach.

PubMed

Tipton, Elizabeth; Shuster, Jonathan

2017-10-15

Bland-Altman method comparison studies are common in the medical sciences and are used to compare a new measure to a gold-standard (often costlier or more invasive) measure. The distribution of these differences is summarized by two statistics, the 'bias' and standard deviation, and these measures are combined to provide estimates of the limits of agreement (LoA). When these LoA are within the bounds of clinically insignificant differences, the new non-invasive measure is preferred. Very often, multiple Bland-Altman studies have been conducted comparing the same two measures, and random-effects meta-analysis provides a means to pool these estimates. We provide a framework for the meta-analysis of Bland-Altman studies, including methods for estimating the LoA and measures of uncertainty (i.e., confidence intervals). Importantly, these LoA are likely to be wider than those typically reported in Bland-Altman meta-analyses. Frequently, Bland-Altman studies report results based on repeated measures designs but do not properly adjust for this design in the analysis. Meta-analyses of Bland-Altman studies frequently exclude these studies for this reason. We provide a meta-analytic approach that allows inclusion of estimates from these studies. This includes adjustments to the estimate of the standard deviation and a method for pooling the estimates based upon robust variance estimation. An example is included based on a previously published meta-analysis. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Molecular structure, interatomic interactions and vibrational analysis of 1,4-diazabicyclo[3.2.1]octane parent ring system

NASA Astrophysics Data System (ADS)

Britvin, Sergey N.; Rumyantsev, Andrey M.; Zobnina, Anastasia E.; Padkina, Marina V.

2017-02-01

Molecular structure of 1,4-diazabicyclo[3.2.1]octane, a parent ring of TAN1251 family of alkaloids, is herein characterized for the first time in comparison with the structure of nortropane (8-azabicyclo[3.2.1]octane), the parent framework of tropane ring system. The methods of study involve X-ray structural analysis, DFT geometry optimizations with infrared frequency calculations followed by natural bond orbital (NBO) analysis, and vibrational analysis of infrared spectrum.

COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

PubMed

Ebrahim, Ali; Lerman, Joshua A; Palsson, Bernhard O; Hyduke, Daniel R

2013-08-08

COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. http://opencobra.sourceforge.net/

Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

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

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao

In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a systemcentric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologiesmore » within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.« less

Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework

NASA Astrophysics Data System (ADS)

Mukherjee, Subhayan; Guddeti, Ram Mohana Reddy

2014-09-01

We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries' disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries' disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users' non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU-GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 × 2,304).

Topology Optimization using the Level Set and eXtended Finite Element Methods: Theory and Applications

NASA Astrophysics Data System (ADS)

Villanueva Perez, Carlos Hernan

Computational design optimization provides designers with automated techniques to develop novel and non-intuitive optimal designs. Topology optimization is a design optimization technique that allows for the evolution of a broad variety of geometries in the optimization process. Traditional density-based topology optimization methods often lack a sufficient resolution of the geometry and physical response, which prevents direct use of the optimized design in manufacturing and the accurate modeling of the physical response of boundary conditions. The goal of this thesis is to introduce a unified topology optimization framework that uses the Level Set Method (LSM) to describe the design geometry and the eXtended Finite Element Method (XFEM) to solve the governing equations and measure the performance of the design. The methodology is presented as an alternative to density-based optimization approaches, and is able to accommodate a broad range of engineering design problems. The framework presents state-of-the-art methods for immersed boundary techniques to stabilize the systems of equations and enforce the boundary conditions, and is studied with applications in 2D and 3D linear elastic structures, incompressible flow, and energy and species transport problems to test the robustness and the characteristics of the method. A comparison of the framework against density-based topology optimization approaches is studied with regards to convergence, performance, and the capability to manufacture the designs. Furthermore, the ability to control the shape of the design to operate within manufacturing constraints is developed and studied. The analysis capability of the framework is validated quantitatively through comparison against previous benchmark studies, and qualitatively through its application to topology optimization problems. The design optimization problems converge to intuitive designs and resembled well the results from previous 2D or density-based studies.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Project Assessment Framework through Design (PAFTD) - A Project Assessment Framework in Support of Strategic Decision Making

NASA Technical Reports Server (NTRS)

Depenbrock, Brett T.; Balint, Tibor S.; Sheehy, Jeffrey A.

2014-01-01

Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.

Finite dimensional approximation of a class of constrained nonlinear optimal control problems

NASA Technical Reports Server (NTRS)

Gunzburger, Max D.; Hou, L. S.

1994-01-01

An abstract framework for the analysis and approximation of a class of nonlinear optimal control and optimization problems is constructed. Nonlinearities occur in both the objective functional and in the constraints. The framework includes an abstract nonlinear optimization problem posed on infinite dimensional spaces, and approximate problem posed on finite dimensional spaces, together with a number of hypotheses concerning the two problems. The framework is used to show that optimal solutions exist, to show that Lagrange multipliers may be used to enforce the constraints, to derive an optimality system from which optimal states and controls may be deduced, and to derive existence results and error estimates for solutions of the approximate problem. The abstract framework and the results derived from that framework are then applied to three concrete control or optimization problems and their approximation by finite element methods. The first involves the von Karman plate equations of nonlinear elasticity, the second, the Ginzburg-Landau equations of superconductivity, and the third, the Navier-Stokes equations for incompressible, viscous flows.

Service quality framework for clinical laboratories.

PubMed

Ramessur, Vinaysing; Hurreeram, Dinesh Kumar; Maistry, Kaylasson

2015-01-01

The purpose of this paper is to illustrate a service quality framework that enhances service delivery in clinical laboratories by gauging medical practitioner satisfaction and by providing avenues for continuous improvement. The case study method has been used for conducting the exploratory study, with focus on the Mauritian public clinical laboratory. A structured questionnaire based on the SERVQUAL service quality model was used for data collection, analysis and for the development of the service quality framework. The study confirms the pertinence of the following service quality dimensions within the context of clinical laboratories: tangibility, reliability, responsiveness, turnaround time, technology, test reports, communication and laboratory staff attitude and behaviour. The service quality framework developed, termed LabSERV, is vital for clinical laboratories in the search for improving service delivery to medical practitioners. This is a pioneering work carried out in the clinical laboratory sector in Mauritius. Medical practitioner expectations and perceptions have been simultaneously considered to generate a novel service quality framework for clinical laboratories.

Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background

NASA Astrophysics Data System (ADS)

McEwen, J. D.; Feeney, S. M.; Peiris, H. V.; Wiaux, Y.; Ringeval, C.; Bouchet, F. R.

2017-12-01

Cosmic strings are a well-motivated extension to the standard cosmological model and could induce a subdominant component in the anisotropies of the cosmic microwave background (CMB), in addition to the standard inflationary component. The detection of strings, while observationally challenging, would provide a direct probe of physics at very high-energy scales. We develop a framework for cosmic string inference from observations of the CMB made over the celestial sphere, performing a Bayesian analysis in wavelet space where the string-induced CMB component has distinct statistical properties to the standard inflationary component. Our wavelet-Bayesian framework provides a principled approach to compute the posterior distribution of the string tension Gμ and the Bayesian evidence ratio comparing the string model to the standard inflationary model. Furthermore, we present a technique to recover an estimate of any string-induced CMB map embedded in observational data. Using Planck-like simulations, we demonstrate the application of our framework and evaluate its performance. The method is sensitive to Gμ ∼ 5 × 10-7 for Nambu-Goto string simulations that include an integrated Sachs-Wolfe contribution only and do not include any recombination effects, before any parameters of the analysis are optimized. The sensitivity of the method compares favourably with other techniques applied to the same simulations.

Human factors considerations in designing for infection prevention and control in neonatal care - findings from a pre-design inquiry.

PubMed

Trudel, Chantal; Cobb, Sue; Momtahan, Kathryn; Brintnell, Janet; Mitchell, Ann

2018-01-01

Qualitative data collection methods drawn from the early stages of human-centred design frameworks combined with thematic analysis were used to develop an understanding of infection prevention practice within an existing neonatal intensive care unit. Findings were used to generate a framework of understanding which in turn helped inform a baseline approach for future research and design development. The study revealed that a lack of clarity between infection transmission zones and a lack of design attributes needed to uphold infection prevention measures may be undermining healthcare workers' understanding and application of good practice. The issue may be further complicated by well-intentioned behavioural attitudes to meeting work objectives; undue influences from spatial constraints; the influence of inadvertent and excessive touch-based interactions; physical and/or cognitive exertion to maintain transmission barriers; and the impact of expanding job design and increased workload to supplement for lack of effective barriers. Practitioner Summary: Despite high hand hygiene compliance within a neonatal intensive care unit, healthcare workers expressed concerns about the unit design and infection prevention practice. Early inquiry methods from human-centred design and thematic analysis helped develop a framework to understand how design can be used to aid infection prevention.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Development of an "Alert Framework" Based on the Practices in the Medical Front.

PubMed

Sakata, Takuya; Araki, Kenji; Yamazaki, Tomoyoshi; Kawano, Koichi; Maeda, Minoru; Kushima, Muneo; Araki, Sanae

2018-05-09

At the University of Miyazaki Hospital (UMH), we have accumulated and semantically structured a vast amount of medical information since the activation of the electronic health record system approximately 10 years ago. With this medical information, we have decided to develop an alert system for aiding in medical treatment. The purpose of this investigation is to not only to integrate an alert framework into the electronic heath record system, but also to formulate a modeling method of this knowledge. A trial alert framework was developed for the staff in various occupational categories at the UMH. Based on findings of subsequent interviews, a more detailed and upgraded alert framework was constructed, resulting in the final model. Based on our current findings, an alert framework was developed with four major items. Based on the analysis of the medical practices from the trial model, it has been concluded that there are four major risk patterns that trigger the alert. Furthermore, the current alert framework contains detailed definitions which are easily substituted into the database, leading to easy implementation of the electronic health records.

Influence of finish line in the distribution of stress trough an all ceramic implant-supported crown.

PubMed Central

SANNINO, G.; GLORIA, F.; OTTRIA, L.; BARLATTANI, A.

2010-01-01

SUMMARY Porpose. The aim of this study was to evaluate, by finite element analysis (FEA), the influence of finish line on stress distribution and resistance to the loads of a ZrO2 crown and porcelain in implant-supported. Material and methods. The object of this analysis consisted of a fxture, an abutment, a passing screw, a layer of cement, a framework crown, a feldspatic porcelain veneering. The abutment’s marginal design was used in 3 different types of preparation: feather edge, slight chamfer and 50°, each of them was of 1 mm depth over the entire circumference. The ZrO2Y-TZP coping was 0.6 mm thick. Two material matching for the abutment and the framework was used for the simulations: ZrO2 framework and ZrO2 abutment, ZrO2 framework and T abutment. A 600 N axial force distributed over the entire surface of the crown was applied. The numerical simulations with finite elements were used to verify the different distribution of equivalent von Mises stress for three different geometries of abutment and framework. Results Slight chamfer on the matching ZrO2 - ZrO2 is the geometry with minimum equivalent stress of von Mises. Even for T abutment and ZrO2 framework slight chamfer is the best configuration to minimize the localized stress. Geometry that has the highest average stress is one with abutment at 50°, we see a downward trend for all three configurations using only zirconium for both components. Conclusions Finite element analysis. performed for the manifacturing of implant-supported crown, gives exact geometric guide lines about the choice of chamfer preparation, while the analysis of other marginal geometries suggests a possible improved behavior of the mating between ZrO2 abutment and ZrO2 coping. for three different geometries of the abutment and the coping. PMID:23285359

Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.

Influence of different tightening forces before laser welding to the implant/framework fit.

PubMed

da Silveira-Júnior, Clebio Domingues; Neves, Flávio Domingues; Fernandes-Neto, Alfredo Júlio; Prado, Célio Jesus; Simamoto-Júnior, Paulo César

2009-06-01

The aim of the present study was to evaluate the influence of abutment screw tightening force before laser welding procedures on the vertical fit of metal frameworks over four implants. To construct the frameworks, prefabricated titanium abutments and cylindrical titanium bars were joined by laser welding to compose three groups: group of manual torque (GMT), GT10 and GT20. Before welding, manual torque simulating routine laboratory procedure was applied to GTM. In GT10 and GT20, the abutment screws received 10 and 20 Ncm torque, respectively. After welding, the implant/framework interfaces were assessed by optical comparator microscope using two methods. First, the single screw test (SST) was used, in which the interfaces of the screwed and non-screwed abutments were assessed, considering only the abutments at the framework extremities. Second, the interfaces of all the abutments were evaluated when they were screwed. In the SST, intergroup analysis (Kruskal Wallis) showed no significant difference among the three conditions of tightening force; that is, the different tightening force before welding did not guarantee smaller distortions. Intragroup analysis (Wilcoxon) showed that for all groups, the interfaces of the non-screwed abutments were statistically greater than the interfaces of the screwed abutments, evidencing distortions in all the frameworks. ANOVA was applied for the comparison of interfaces when all the abutments were screwed and showed no significant difference among the groups. Under the conditions of this study, pre-welding tightness on abutment screws did not influence the vertical fit of implant-supported metal frameworks.

Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video.

PubMed

Gunay, Osman; Toreyin, Behçet Ugur; Kose, Kivanc; Cetin, A Enis

2012-05-01

In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.

On the Use of CAD and Cartesian Methods for Aerodynamic Optimization

NASA Technical Reports Server (NTRS)

Nemec, M.; Aftosmis, M. J.; Pulliam, T. H.

2004-01-01

The objective for this paper is to present the development of an optimization capability for Curt3D, a Cartesian inviscid-flow analysis package. We present the construction of a new optimization framework and we focus on the following issues: 1) Component-based geometry parameterization approach using parametric-CAD models and CAPRI. A novel geometry server is introduced that addresses the issue of parallel efficiency while only sparingly consuming CAD resources; 2) The use of genetic and gradient-based algorithms for three-dimensional aerodynamic design problems. The influence of noise on the optimization methods is studied. Our goal is to create a responsive and automated framework that efficiently identifies design modifications that result in substantial performance improvements. In addition, we examine the architectural issues associated with the deployment of a CAD-based approach in a heterogeneous parallel computing environment that contains both CAD workstations and dedicated compute engines. We demonstrate the effectiveness of the framework for a design problem that features topology changes and complex geometry.

A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.

PubMed

Soneson, Charlotte; Fontes, Magnus

2012-01-01

Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.

Understanding Unintended Consequences and Health Information Technology:

PubMed Central

Randell, R.; Borycki, E. M.

2016-01-01

Summary Objective No framework exists to identify and study unintended consequences (UICs) with a focus on organizational and social issues (OSIs). To address this shortcoming, we conducted a literature review to develop a framework for considering UICs and health information technology (HIT) from the perspective of OSIs. Methods A literature review was conducted for the period 2000-2015 using the search terms “unintended consequences” and “health information technology”. 67 papers were screened, of which 18 met inclusion criteria. Data extraction was focused on the types of technologies studied, types of UICs identified, and methods of data collection and analysis used. A thematic analysis was used to identify themes related to UICs. Results We identified two overarching themes. One was the definition and terminology of how people classify and discuss UICs. Second was OSIs and UICs. For the OSI theme, we also identified four sub-themes: process change and evolution, individual-collaborative interchange, context of use, and approaches to model, study, and understand UICs. Conclusions While there is a wide body of research on UICs, there is a lack of overall consensus on how they should be classified and reported, limiting our ability to understand the implications of UICs and how to manage them. More mixed-methods research and better proactive identification of UICs remain priorities. Our findings and framework of OSI considerations for studying UICs and HIT extend existing work on HIT and UICs by focusing on organizational and social issues. PMID:27830231

Task–Technology Fit of Video Telehealth for Nurses in an Outpatient Clinic Setting

PubMed Central

Finkelstein, Stanley M.

2014-01-01

Abstract Background: Incorporating telehealth into outpatient care delivery supports management of consumer health between clinic visits. Task–technology fit is a framework for understanding how technology helps and/or hinders a person during work processes. Evaluating the task–technology fit of video telehealth for personnel working in a pediatric outpatient clinic and providing care between clinic visits ensures the information provided matches the information needed to support work processes. Materials and Methods: The workflow of advanced practice registered nurse (APRN) care coordination provided via telephone and video telehealth was described and measured using a mixed-methods workflow analysis protocol that incorporated cognitive ethnography and time–motion study. Qualitative and quantitative results were merged and analyzed within the task–technology fit framework to determine the workflow fit of video telehealth for APRN care coordination. Results: Incorporating video telehealth into APRN care coordination workflow provided visual information unavailable during telephone interactions. Despite additional tasks and interactions needed to obtain the visual information, APRN workflow efficiency, as measured by time, was not significantly changed. Analyzed within the task–technology fit framework, the increased visual information afforded by video telehealth supported the assessment and diagnostic information needs of the APRN. Conclusions: Telehealth must provide the right information to the right clinician at the right time. Evaluating task–technology fit using a mixed-methods protocol ensured rigorous analysis of fit within work processes and identified workflows that benefit most from the technology. PMID:24841219

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Domestic violence at the intersections of race, class, and gender: challenges and contributions to understanding violence against marginalized women in diverse communities.

PubMed

Sokoloff, Natalie J; Dupont, Ida

2005-01-01

This article provides a comprehensive review of the emerging domestic violence literature using a race, class, gender, sexual orientation intersectional analysis and structural framework fostered by women of color and their allies to understand the experiences and contexts of domestic violence for marginalized women in U.S. society. The first half of the article lays out a series of challenges that an intersectional analysis grounded in a structural framework provides for understanding the role of culture in domestic violence. The second half of the article points to major contributions of such an approach to feminist methods and practices in working with battered women on the margins of society.

Synthesis of Metal-organic Frameworks Based on Zr4+ and Benzene 1,3,5-Tricarboxylate Linker as Heterogeneous Catalyst in the Esterification Reaction of Palmitic Acid

NASA Astrophysics Data System (ADS)

Larasati, I.; Winarni, D.; Putri, F. R.; Hanif, Q. A.; Lestari, W. W.

2017-07-01

The conversion of the biomass into biodiesels via catalytic esterification and trans-esterification became an interesting topic due to the depletion of fossil-based energy. Homogenous catalysts such as HCl, H2SO4 and NaOH commonly used as catalyst, however, the use of this kind of catalyst causes more problems, such as the difficulties on the separation from the product and the pollution effect on the environment. Heterogeneous catalysts, such as Metal-Organic Frameworks (MOFs) give an alternative promising way to substitute these limitations due to their strong catalytic site, porosity, high specific surface area, and easy-separation and reusable properties. Herein, we reported the synthesis of MOFs based on zirconium(IV) and H3BTC linker (H3BTC = benzene-1,3,5-tricarboxylic acid) by solvothermal and reflux method. Solvothermal reaction at 120 °C was found to be the optimum method, that was indicated by most crystalline product compared to the simulated pattern in XRD analysis. The formation of the framework was characterized by FTIR analysis, which showed a significant shift from 1722 cm-1 to 1620 cm-1. The synthesized Zr(IV)-BTC was thermally stable up to 322°C as shown by TG/DTA analysis. This high thermal stability was related to the high oxidation state of Zr(IV), which give a significant covalent character to the Zr-O bond.

Deciphering Rashomon: an approach to verbal autopsies of maternal deaths.

PubMed

Iyer, Aditi; Sen, Gita; Sreevathsa, Anuradha

2013-01-01

The paper discusses an approach to verbal autopsies that engages with the Rashomon phenomenon affecting ex post facto constructions of death and responds to the call for maternal safety. This method differs from other verbal autopsies in its approach to data collection and its framework of analysis. In our approach, data collection entails working with and triangulating multiple narratives, and minimising power inequalities in the investigation process. The framework of analysis focuses on the missed opportunities for death prevention as an alternative to (or deepening of) the Three Delays Model. This framework assesses the behavioural responses of health providers, as well as community and family members at each opportunity for death prevention and categorises them into four groups: non-actions, inadequate actions, inappropriate actions and unavoidably delayed actions. We demonstrate the application of this approach to show how verbal autopsies can delve beneath multiple narratives and rigorously identify health system, behavioural and cultural factors that contribute to avoidable maternal mortality.

Addressing multi-label imbalance problem of surgical tool detection using CNN.

PubMed

Sahu, Manish; Mukhopadhyay, Anirban; Szengel, Angelika; Zachow, Stefan

2017-06-01

A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.

Inverse problems in heterogeneous and fractured media using peridynamics

DOE PAGES

Turner, Daniel Z.; van Bloemen Waanders, Bart G.; Parks, Michael L.

2015-12-10

The following work presents an adjoint-based methodology for solving inverse problems in heterogeneous and fractured media using state-based peridynamics. We show that the inner product involving the peridynamic operators is self-adjoint. The proposed method is illustrated for several numerical examples with constant and spatially varying material parameters as well as in the context of fractures. We also present a framework for obtaining material parameters by integrating digital image correlation (DIC) with inverse analysis. This framework is demonstrated by evaluating the bulk and shear moduli for a sample of nuclear graphite using digital photographs taken during the experiment. The resulting measuredmore » values correspond well with other results reported in the literature. Lastly, we show that this framework can be used to determine the load state given observed measurements of a crack opening. Furthermore, this type of analysis has many applications in characterizing subsurface stress-state conditions given fracture patterns in cores of geologic material.« less

A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area.

PubMed

Azadeh, Ali; Zarrin, Mansour; Hamid, Mehdi

2016-02-01

Road accidents can be caused by different factors such as human factors. Quality of the decision-making process of drivers could have a considerable impact on preventing disasters. The main objective of this study is the analysis of factors affecting road accidents by considering the severity of accidents and decision-making styles of drivers. To this end, a novel framework is proposed based on data envelopment analysis (DEA) and statistical methods (SMs) to assess the factors affecting road accidents. In this study, for the first time, dominant decision-making styles of drivers with respect to severity of injuries are identified. To show the applicability of the proposed framework, this research employs actual data of more than 500 samples in Tehran, Iran. The empirical results indicate that the flexible decision style is the dominant style for both minor and severe levels of accident injuries. Copyright © 2015 Elsevier Ltd. All rights reserved.

Free vibration of functionally graded beams and frameworks using the dynamic stiffness method

NASA Astrophysics Data System (ADS)

Banerjee, J. R.; Ananthapuvirajah, A.

2018-05-01

The free vibration analysis of functionally graded beams (FGBs) and frameworks containing FGBs is carried out by applying the dynamic stiffness method and deriving the elements of the dynamic stiffness matrix in explicit algebraic form. The usually adopted rule that the material properties of the FGB vary continuously through the thickness according to a power law forms the fundamental basis of the governing differential equations of motion in free vibration. The differential equations are solved in closed analytical form when the free vibratory motion is harmonic. The dynamic stiffness matrix is then formulated by relating the amplitudes of forces to those of the displacements at the two ends of the beam. Next, the explicit algebraic expressions for the dynamic stiffness elements are derived with the help of symbolic computation. Finally the Wittrick-Williams algorithm is applied as solution technique to solve the free vibration problems of FGBs with uniform cross-section, stepped FGBs and frameworks consisting of FGBs. Some numerical results are validated against published results, but in the absence of published results for frameworks containing FGBs, consistency checks on the reliability of results are performed. The paper closes with discussion of results and conclusions.

Experiences of using the Theoretical Domains Framework across diverse clinical environments: a qualitative study

PubMed Central

Phillips, Cameron J; Marshall, Andrea P; Chaves, Nadia J; Jankelowitz, Stacey K; Lin, Ivan B; Loy, Clement T; Rees, Gwyneth; Sakzewski, Leanne; Thomas, Susie; To, The-Phung; Wilkinson, Shelley A; Michie, Susan

2015-01-01

Background The Theoretical Domains Framework (TDF) is an integrative framework developed from a synthesis of psychological theories as a vehicle to help apply theoretical approaches to interventions aimed at behavior change. Purpose This study explores experiences of TDF use by professionals from multiple disciplines across diverse clinical settings. Methods Mixed methods were used to examine experiences, attitudes, and perspectives of health professionals in using the TDF in health care implementation projects. Individual interviews were conducted with ten health care professionals from six disciplines who used the TDF in implementation projects. Deductive content and thematic analysis were used. Results Three main themes and associated subthemes were identified including: 1) reasons for use of the TDF (increased confidence, broader perspective, and theoretical underpinnings); 2) challenges using the TDF (time and resources, operationalization of the TDF) and; 3) future use of the TDF. Conclusion The TDF provided a useful, flexible framework for a diverse group of health professionals working across different clinical settings for the assessment of barriers and targeting resources to influence behavior change for implementation projects. The development of practical tools and training or support is likely to aid the utility of TDF. PMID:25834455

Decentralized indirect methods for learning automata games.

PubMed

Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

2011-10-01

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

Development and implementation of a peer-based mental health support programme for adolescents orphaned by HIV/AIDS in South Africa.

PubMed

Thupayagale-Tshweneagae, Gloria

2011-12-01

The article describes a framework and the process for the development of the peer-based mental health support programme and its implementation. The development of a peer-based mental health support programme is based on Erikson's theory on the adolescent phase of development, the psycho-educational processes; the peer approach and the orphaned adolescents lived experiences as conceptual framework. A triangulation of five qualitative methods of photography, reflective diaries, focus groups, event history calendar and field notes were used to capture the lived experiences of adolescents orphaned to HIV and AIDS. Analysis of data followed Colaizzi's method of data analysis. The combination of psycho-education, Erikson's stages of development and peer support assisted the participants to gain knowledge and skills to overcome adversity and to assist them to become to more resilient. The peer based mental health support programme if used would enhance the mental health of adolescent orphans.

Measurement methods for human exposure analysis.

PubMed Central

Lioy, P J

1995-01-01

The general methods used to complete measurements of human exposures are identified and illustrations are provided for the cases of indirect and direct methods used for exposure analysis. The application of the techniques for external measurements of exposure, microenvironmental and personal monitors, are placed in the context of the need to test hypotheses concerning the biological effects of concern. The linkage of external measurements to measurements made in biological fluids is explored for a suite of contaminants. This information is placed in the context of the scientific framework used to conduct exposure assessment. Examples are taken from research on volatile organics and for a large scale problem: hazardous waste sites. PMID:7635110

Developing biodiversity indicators on a stakeholders' opinions basis: the gypsum industry Key Performance Indicators framework.

PubMed

Pitz, Carline; Mahy, Grégory; Vermeulen, Cédric; Marlet, Christine; Séleck, Maxime

2016-07-01

This study aims to establish a common Key Performance Indicators (KPIs) framework for reporting about the gypsum industry biodiversity at the European level. In order to integrate different opinions and to reach a consensus framework, an original participatory process approach has been developed among different stakeholder groups: Eurogypsum, European and regional authorities, university scientists, consulting offices, European and regional associations for the conservation of nature, and the extractive industry. The strategy is developed around four main steps: (1) building of a maximum set of indicators to be submitted to stakeholders based on the literature (Focus Group method); (2) evaluating the consensus about indicators through a policy Delphi survey aiming at the prioritization of indicator classes using the Analytic Hierarchy Process method (AHP) and of individual indicators; (3) testing acceptability and feasibility through analysis of Environmental Impact Assessments (EIAs) and visits to three European quarries; (4) Eurogypsum final decision and communication. The resulting framework contains a set of 11 indicators considered the most suitable for all the stakeholders. Our KPIs respond to European legislation and strategies for biodiversity. The framework aims at improving sustainability in quarries and at helping to manage biodiversity as well as to allow the creation of coherent reporting systems. The final goal is to allow for the definition of the actual biodiversity status of gypsum quarries and allow for enhancing it. The framework is adaptable to the local context of each gypsum quarry.

Multiplicative Multitask Feature Learning

PubMed Central

Wang, Xin; Bi, Jinbo; Yu, Shipeng; Sun, Jiangwen; Song, Minghu

2016-01-01

We investigate a general framework of multiplicative multitask feature learning which decomposes individual task’s model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. PMID:28428735

Revealing representational content with pattern-information fMRI--an introductory guide.

PubMed

Mur, Marieke; Bandettini, Peter A; Kriegeskorte, Nikolaus

2009-03-01

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

A development framework for semantically interoperable health information systems.

PubMed

Lopez, Diego M; Blobel, Bernd G M E

2009-02-01

Semantic interoperability is a basic challenge to be met for new generations of distributed, communicating and co-operating health information systems (HIS) enabling shared care and e-Health. Analysis, design, implementation and maintenance of such systems and intrinsic architectures have to follow a unified development methodology. The Generic Component Model (GCM) is used as a framework for modeling any system to evaluate and harmonize state of the art architecture development approaches and standards for health information systems as well as to derive a coherent architecture development framework for sustainable, semantically interoperable HIS and their components. The proposed methodology is based on the Rational Unified Process (RUP), taking advantage of its flexibility to be configured for integrating other architectural approaches such as Service-Oriented Architecture (SOA), Model-Driven Architecture (MDA), ISO 10746, and HL7 Development Framework (HDF). Existing architectural approaches have been analyzed, compared and finally harmonized towards an architecture development framework for advanced health information systems. Starting with the requirements for semantic interoperability derived from paradigm changes for health information systems, and supported in formal software process engineering methods, an appropriate development framework for semantically interoperable HIS has been provided. The usability of the framework has been exemplified in a public health scenario.

Student Background, School Climate, School Disorder, and Student Achievement: An Empirical Study of New York City's Middle Schools

ERIC Educational Resources Information Center

Chen, Greg; Weikart, Lynne A.

2008-01-01

This study develops and tests a school disorder and student achievement model based upon the school climate framework. The model was fitted to 212 New York City middle schools using the Structural Equations Modeling Analysis method. The analysis shows that the model fits the data well based upon test statistics and goodness of fit indices. The…

Policy-Impact Analysis: A Rational Method to Respond to the Challenges Faced by Higher Education in the Eighties.

ERIC Educational Resources Information Center

Morrison, James L.

Policy-impact analysis is introduced as a model to aid higher education in dealing with the significant problems in the decade of the 1980s. The model provides a framework within which a variety of futures research techniques are tied to policy development, implementation, and evaluation. The utility of the model is that it structures…

The "con" of concept analysis A discussion paper which explores and critiques the ontological focus, reliability and antecedents of concept analysis frameworks.

PubMed

Beckwith, Sue; Dickinson, Angela; Kendall, Sally

2008-12-01

This paper draws on the work of Paley and Duncan et al in order to extend and engender debate regarding the use of Concept Analysis frameworks. Despite the apparent plethora of Concept Analysis frameworks used in nursing studies we found that over half of those used were derived from the work of one author. This paper explores the suitability and use of these frameworks and is set at a time when the numbers of published concept analysis papers are increasing. For the purpose of this study thirteen commonly used frameworks, identified from the nursing journals 1993 to 2005, were explored to reveal their origins, ontological and philosophical stance, and any common elements. The frameworks were critiqued and links made between their antecedents. It was noted if the articles contained discussion of any possible tensions between the ontological perspective of the framework used, the process of analysis, praxis and possible nursing theory developments. It was found that the thirteen identified frameworks are mainly based on hermeneutic propositions regarding understandings and are interpretive procedures founded on self-reflective modes of discovery. Six frameworks rely on or include the use of casuistry. Seven of the frameworks identified are predicated on, or adapt the work of Wilson, a school master writing for his pupils. Wilson's framework has a simplistic eleven step, binary and reductionist structure. Other frameworks identified include Morse et al's framework which this article suggests employs a contestable theory of concept maturity. Based on the findings revealed through our exploration of the use of concept analysis frameworks in the nursing literature, concerns were raised regarding the unjustified adaptation and alterations and the uncritical use of the frameworks. There is little evidence that these frameworks provide the necessary depth, rigor or replicability to enable the development in nursing theory which they underpin.

An Applied Ecological Framework for Evaluating Infrastructure to Promote Walking and Cycling: The iConnect Study

PubMed Central

Bull, Fiona; Powell, Jane; Cooper, Ashley R.; Brand, Christian; Mutrie, Nanette; Preston, John; Rutter, Harry

2011-01-01

Improving infrastructure for walking and cycling is increasingly recommended as a means to promote physical activity, prevent obesity, and reduce traffic congestion and carbon emissions. However, limited evidence from intervention studies exists to support this approach. Drawing on classic epidemiological methods, psychological and ecological models of behavior change, and the principles of realistic evaluation, we have developed an applied ecological framework by which current theories about the behavioral effects of environmental change may be tested in heterogeneous and complex intervention settings. Our framework guides study design and analysis by specifying the most important data to be collected and relations to be tested to confirm or refute specific hypotheses and thereby refine the underlying theories. PMID:21233429

An elastography framework for use in dermoscopy

NASA Astrophysics Data System (ADS)

Miga, Michael I.; Ou, Jao J.; Ellis, Darrel L.

2007-03-01

Multiple skin conditions exist which involve clinically significant changes in elastic properties. Early detection of such changes may prove critical in formulating a proper treatment plan. However, most diagnoses still rely primarily on visual inspection followed by biopsy for histological analysis. As a result, there would be considerable clinical benefit if a noninvasive technology to study the skin were available. The primary hypothesis of this work is that skin elasticity may serve as an important method for assisting diagnosis and treatment. Perhaps the most apparent application would be for the differentiation of skin cancers, which are a growing health concern in the United States as total annual cases are now being reported in the millions by the American Cancer Society. In this paper, we use our novel modality independent elastography (MIE) method to perform dermoscopic skin elasticity evaluation. The framework involves applying a lateral stretching to the skin in which dermoscopic images are acquired before and after mechanical excitation. Once collected, an iterative elastographic reconstruction method is used to generate images of tissue elastic properties and is based on a twodimensional (2-D) membrane model framework. Simulation studies are performed that show the effects of three-dimensional data, varying subdermal tissue thickness, and nonlinear large deformations on the framework. In addition, a preliminary in vivo reconstruction is demonstrated. The results are encouraging and indicate good localization with satisfactory degrees of elastic contrast resolution.

Synthesis and Characterization of Functionalized Metal-organic Frameworks

PubMed Central

Karagiaridi, Olga; Bury, Wojciech; Sarjeant, Amy A.; Hupp, Joseph T.; Farha, Omar K.

2014-01-01

Metal-organic frameworks have attracted extraordinary amounts of research attention, as they are attractive candidates for numerous industrial and technological applications. Their signature property is their ultrahigh porosity, which however imparts a series of challenges when it comes to both constructing them and working with them. Securing desired MOF chemical and physical functionality by linker/node assembly into a highly porous framework of choice can pose difficulties, as less porous and more thermodynamically stable congeners (e.g., other crystalline polymorphs, catenated analogues) are often preferentially obtained by conventional synthesis methods. Once the desired product is obtained, its characterization often requires specialized techniques that address complications potentially arising from, for example, guest-molecule loss or preferential orientation of microcrystallites. Finally, accessing the large voids inside the MOFs for use in applications that involve gases can be problematic, as frameworks may be subject to collapse during removal of solvent molecules (remnants of solvothermal synthesis). In this paper, we describe synthesis and characterization methods routinely utilized in our lab either to solve or circumvent these issues. The methods include solvent-assisted linker exchange, powder X-ray diffraction in capillaries, and materials activation (cavity evacuation) by supercritical CO2 drying. Finally, we provide a protocol for determining a suitable pressure region for applying the Brunauer-Emmett-Teller analysis to nitrogen isotherms, so as to estimate surface area of MOFs with good accuracy. PMID:25225784

Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

PubMed

Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

2016-05-27

Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.

An approach for quantitative image quality analysis for CT

NASA Astrophysics Data System (ADS)

Rahimi, Amir; Cochran, Joe; Mooney, Doug; Regensburger, Joe

2016-03-01

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T2 statistical analysis method to compare, qualify, and detect faults in the tested systems.

Real Time 3D Facial Movement Tracking Using a Monocular Camera

PubMed Central

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-01-01

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference. PMID:27463714

Real Time 3D Facial Movement Tracking Using a Monocular Camera.

PubMed

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-07-25

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference.

Critical look at physics identity: An operationalized framework for examining race and physics identity

NASA Astrophysics Data System (ADS)

Hyater-Adams, Simone; Fracchiolla, Claudia; Finkelstein, Noah; Hinko, Kathleen

2018-06-01

Studies on physics identity are appearing more frequently and often responding to increased awareness of the underrepresentation of students of color in physics. In our broader research, we focus our efforts on understanding how racial identity and physics identity are negotiated throughout the experiences of Black physicists. In this paper, we present a Critical Physics Identity framework that can be used to examine racialized physics identity and demonstrate the utility of this framework by analyzing interviews with four physicists. Our framework draws from prior constructs of physics identity and racialized identity and provides operational definitions of six interacting dimensions. In this paper, we present the operationalized constructs, demonstrate how we use these constructs to code narrative data, as well as outline three methods of analysis that may be applied to study systems and structures and their influences on the experiences of Black students.

A framework for service enterprise workflow simulation with multi-agents cooperation

NASA Astrophysics Data System (ADS)

Tan, Wenan; Xu, Wei; Yang, Fujun; Xu, Lida; Jiang, Chuanqun

2013-11-01

Process dynamic modelling for service business is the key technique for Service-Oriented information systems and service business management, and the workflow model of business processes is the core part of service systems. Service business workflow simulation is the prevalent approach to be used for analysis of service business process dynamically. Generic method for service business workflow simulation is based on the discrete event queuing theory, which is lack of flexibility and scalability. In this paper, we propose a service workflow-oriented framework for the process simulation of service businesses using multi-agent cooperation to address the above issues. Social rationality of agent is introduced into the proposed framework. Adopting rationality as one social factor for decision-making strategies, a flexible scheduling for activity instances has been implemented. A system prototype has been developed to validate the proposed simulation framework through a business case study.

Intercalation of Coordinatively Unsaturated Fe(III) Ion within Interpenetrated Metal-Organic Framework MOF-5.

PubMed

Holmberg, Rebecca J; Burns, Thomas; Greer, Samuel M; Kobera, Libor; Stoian, Sebastian A; Korobkov, Ilia; Hill, Stephen; Bryce, David L; Woo, Tom K; Murugesu, Muralee

2016-06-01

Coordinatively unsaturated Fe(III) metal sites were successfully incorporated into the iconic MOF-5 framework. This new structure, Fe(III) -iMOF-5, is the first example of an interpenetrated MOF linked through intercalated metal ions. Structural characterization was performed with single-crystal and powder XRD, followed by extensive analysis by spectroscopic methods and solid-state NMR, which reveals the paramagnetic ion through its interaction with the framework. EPR and Mössbauer spectroscopy confirmed that the intercalated ions were indeed Fe(III) , whereas DFT calculations were employed to ascertain the unique pentacoordinate architecture around the Fe(III) ion. Interestingly, this is also the first crystallographic evidence of pentacoordinate Zn(II) within the MOF-5 SBU. This new MOF structure displays the potential for metal-site addition as a framework connector, thus creating further opportunity for the innovative development of new MOF materials. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SparRec: An effective matrix completion framework of missing data imputation for GWAS

NASA Astrophysics Data System (ADS)

Jiang, Bo; Ma, Shiqian; Causey, Jason; Qiao, Linbo; Hardin, Matthew Price; Bitts, Ian; Johnson, Daniel; Zhang, Shuzhong; Huang, Xiuzhen

2016-10-01

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

Developing a framework for transferring knowledge into action: a thematic analysis of the literature

PubMed Central

Ward, Vicky; House, Allan; Hamer, Susan

2010-01-01

Objectives Although there is widespread agreement about the importance of transferring knowledge into action, we still lack high quality information about what works, in which settings and with whom. Whilst there are a large number of models and theories for knowledge transfer interventions, they are untested meaning that their applicability and relevance is largely unknown. This paper describes the development of a conceptual framework of translating knowledge into action and discusses how it can be used for developing a useful model of the knowledge transfer process. Methods A narrative review of the knowledge transfer literature identified 28 different models which explained all or part of the knowledge transfer process. The models were subjected to a thematic analysis to identify individual components and the types of processes used when transferring knowledge into action. The results were used to build a conceptual framework of the process. Results Five common components of the knowledge transfer process were identified: problem identification and communication; knowledge/research development and selection; analysis of context; knowledge transfer activities or interventions; and knowledge/research utilization. We also identified three types of knowledge transfer processes: a linear process; a cyclical process; and a dynamic multidirectional process. From these results a conceptual framework of knowledge transfer was developed. The framework illustrates the five common components of the knowledge transfer process and shows that they are connected via a complex, multidirectional set of interactions. As such the framework allows for the individual components to occur simultaneously or in any given order and to occur more than once during the knowledge transfer process. Conclusion Our framework provides a foundation for gathering evidence from case studies of knowledge transfer interventions. We propose that future empirical work is designed to test and refine the relevant importance and applicability of each of the components in order to build more useful models of knowledge transfer which can serve as a practical checklist for planning or evaluating knowledge transfer activities. PMID:19541874

Discovering System Health Anomalies Using Data Mining Techniques

NASA Technical Reports Server (NTRS)

Sriastava, Ashok, N.

2005-01-01

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

The EMBL-EBI bioinformatics web and programmatic tools framework.

PubMed

Li, Weizhong; Cowley, Andrew; Uludag, Mahmut; Gur, Tamer; McWilliam, Hamish; Squizzato, Silvano; Park, Young Mi; Buso, Nicola; Lopez, Rodrigo

2015-07-01

Since 2009 the EMBL-EBI Job Dispatcher framework has provided free access to a range of mainstream sequence analysis applications. These include sequence similarity search services (https://www.ebi.ac.uk/Tools/sss/) such as BLAST, FASTA and PSI-Search, multiple sequence alignment tools (https://www.ebi.ac.uk/Tools/msa/) such as Clustal Omega, MAFFT and T-Coffee, and other sequence analysis tools (https://www.ebi.ac.uk/Tools/pfa/) such as InterProScan. Through these services users can search mainstream sequence databases such as ENA, UniProt and Ensembl Genomes, utilising a uniform web interface or systematically through Web Services interfaces (https://www.ebi.ac.uk/Tools/webservices/) using common programming languages, and obtain enriched results with novel visualisations. Integration with EBI Search (https://www.ebi.ac.uk/ebisearch/) and the dbfetch retrieval service (https://www.ebi.ac.uk/Tools/dbfetch/) further expands the usefulness of the framework. New tools and updates such as NCBI BLAST+, InterProScan 5 and PfamScan, new categories such as RNA analysis tools (https://www.ebi.ac.uk/Tools/rna/), new databases such as ENA non-coding, WormBase ParaSite, Pfam and Rfam, and new workflow methods, together with the retirement of depreciated services, ensure that the framework remains relevant to today's biological community. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

PubMed Central

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

PubMed

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme.

PubMed

Ovaska, Kristian; Laakso, Marko; Haapa-Paananen, Saija; Louhimo, Riku; Chen, Ping; Aittomäki, Viljami; Valo, Erkka; Núñez-Fontarnau, Javier; Rantanen, Ville; Karinen, Sirkku; Nousiainen, Kari; Lahesmaa-Korpinen, Anna-Maria; Miettinen, Minna; Saarinen, Lilli; Kohonen, Pekka; Wu, Jianmin; Westermarck, Jukka; Hautaniemi, Sampsa

2010-09-07

Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed. We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available. We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website. Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/

Loss of Coolant Accident (LOCA) / Emergency Core Coolant System (ECCS Evaluation of Risk-Informed Margins Management Strategies for a Representative Pressurized Water Reactor (PWR)

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

Szilard, Ronaldo Henriques

A Risk Informed Safety Margin Characterization (RISMC) toolkit and methodology are proposed for investigating nuclear power plant core, fuels design and safety analysis, including postulated Loss-of-Coolant Accident (LOCA) analysis. This toolkit, under an integrated evaluation model framework, is name LOCA toolkit for the US (LOTUS). This demonstration includes coupled analysis of core design, fuel design, thermal hydraulics and systems analysis, using advanced risk analysis tools and methods to investigate a wide range of results.

BCILAB: a platform for brain-computer interface development

NASA Astrophysics Data System (ADS)

Kothe, Christian Andreas; Makeig, Scott

2013-10-01

Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

Evaluation of the suitability of root cause analysis frameworks for the investigation of community-acquired pressure ulcers: a systematic review and documentary analysis.

PubMed

McGraw, Caroline; Drennan, Vari M

2015-02-01

To evaluate the suitability of root cause analysis frameworks for the investigation of community-acquired pressure ulcers. The objective was to identify the extent to which these frameworks take account of the setting where the ulcer originated as being the person's home rather than a hospital setting. Pressure ulcers involving full-thickness skin loss are increasingly being regarded as indicators of nursing patient safety failure, requiring investigation using root cause analysis frameworks. Evidence suggests that root cause analysis frameworks developed in hospital settings ignore the unique dimensions of risk in home healthcare settings. A systematic literature review and documentary analysis of frameworks used to investigate community-acquired grade three and four pressure ulcers by home nursing services in England. No published papers were identified for inclusion in the review. Fifteen patient safety investigative frameworks were collected and analysed. Twelve of the retrieved frameworks were intended for the investigation of community-acquired pressure ulcers; seven of which took account of the setting where the ulcer originated as being the patient's home. This study provides evidence to suggest that many of the root cause analysis frameworks used to investigate community-acquired pressure ulcers in England are unsuitable for this purpose. This study provides researchers and practitioners with evidence of the need to develop appropriate home nursing root cause analysis frameworks to investigate community-acquired pressure ulcers. © 2014 John Wiley & Sons Ltd.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams.

PubMed

Wu, Yingcai; Chen, Zhutian; Sun, Guodao; Xie, Xiao; Cao, Nan; Liu, Shixia; Cui, Weiwei

2017-10-18

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.

Methodology Series Module 10: Qualitative Health Research

PubMed Central

Setia, Maninder Singh

2017-01-01

Although quantitative designs are commonly used in clinical research, some studies require qualitative methods. These designs are different from quantitative methods; thus, researchers should be aware of data collection methods and analyses for qualitative research. Qualitative methods are particularly useful to understand patient experiences with the treatment or new methods of management or to explore issues in detail. These methods are useful in social and behavioral research. In qualitative research, often, the main focus is to understand the issue in detail rather than generalizability; thus, the sampling methods commonly used are purposive sampling; quota sampling; and snowball sampling (for hard to reach groups). Data can be collected using in-depth interviews (IDIs) or focus group discussions (FGDs). IDI is a one-to-one interview with the participant. FGD is a method of group interview or discussion, in which more than one participant is interviewed at the same time and is usually led by a facilitator. The commonly used methods for data analysis are: thematic analysis; grounded theory analysis; and framework analysis. Qualitative data collection and analysis require special expertise. Hence, if the reader plans to conduct qualitative research, they should team up with a qualitative researcher. PMID:28794545

Methodology Series Module 10: Qualitative Health Research.

PubMed

Setia, Maninder Singh

2017-01-01

Although quantitative designs are commonly used in clinical research, some studies require qualitative methods. These designs are different from quantitative methods; thus, researchers should be aware of data collection methods and analyses for qualitative research. Qualitative methods are particularly useful to understand patient experiences with the treatment or new methods of management or to explore issues in detail. These methods are useful in social and behavioral research. In qualitative research, often, the main focus is to understand the issue in detail rather than generalizability; thus, the sampling methods commonly used are purposive sampling; quota sampling; and snowball sampling (for hard to reach groups). Data can be collected using in-depth interviews (IDIs) or focus group discussions (FGDs). IDI is a one-to-one interview with the participant. FGD is a method of group interview or discussion, in which more than one participant is interviewed at the same time and is usually led by a facilitator. The commonly used methods for data analysis are: thematic analysis; grounded theory analysis; and framework analysis. Qualitative data collection and analysis require special expertise. Hence, if the reader plans to conduct qualitative research, they should team up with a qualitative researcher.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Classroom Observation Techniques. IDEA Paper No. 4.

ERIC Educational Resources Information Center

Acheson, Keith A.

Techniques for observing the classroom behavior of teachers and students are examined. These techniques provide a framework for analyzing and understanding classroom interaction, for making decisions about what should be happening, and for changing instructional behavior when it is necessary. The observation methods allow collection, analysis, and…

EPA's Reference and Equivalent Supporting NAAQS Implementation through Methods Research Program: Research, Development, and Analysis

EPA Science Inventory

. To assess the ambient concentration levels of the six criteria air pollutants regulated by National Ambient Air Quality Standards (NAAQS), the U.S. Environmental Protection Agency (EPA) developed a systematic framework of: (a) field measurements of ambient air pollutant levels ...

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

NASA Astrophysics Data System (ADS)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by electricity prices and climate change projections. The presented framework is expected to be a valuable tool for the next generation of UWS decision-making and the application demonstrates a novel and valuable integration of metrics and methods for UWS analysis.

Why Map Issues? On Controversy Analysis as a Digital Method

PubMed Central

2015-01-01

This article takes stock of recent efforts to implement controversy analysis as a digital method in the study of science, technology, and society (STS) and beyond and outlines a distinctive approach to address the problem of digital bias. Digital media technologies exert significant influence on the enactment of controversy in online settings, and this risks undermining the substantive focus of controversy analysis conducted by digital means. To address this problem, I propose a shift in thematic focus from controversy analysis to issue mapping. The article begins by distinguishing between three broad frameworks that currently guide the development of controversy analysis as a digital method, namely, demarcationist, discursive, and empiricist. Each has been adopted in STS, but only the last one offers a digital “move beyond impartiality.” I demonstrate this approach by analyzing issues of Internet governance with the aid of the social media platform Twitter. PMID:26336325

Design and Analysis Tool for External-Compression Supersonic Inlets

NASA Technical Reports Server (NTRS)

Slater, John W.

2012-01-01

A computational tool named SUPIN has been developed to design and analyze external-compression supersonic inlets for aircraft at cruise speeds from Mach 1.6 to 2.0. The inlet types available include the axisymmetric outward-turning, two-dimensional single-duct, two-dimensional bifurcated-duct, and streamline-traced Busemann inlets. The aerodynamic performance is characterized by the flow rates, total pressure recovery, and drag. The inlet flowfield is divided into parts to provide a framework for the geometry and aerodynamic modeling and the parts are defined in terms of geometric factors. The low-fidelity aerodynamic analysis and design methods are based on analytic, empirical, and numerical methods which provide for quick analysis. SUPIN provides inlet geometry in the form of coordinates and surface grids useable by grid generation methods for higher-fidelity computational fluid dynamics (CFD) analysis. SUPIN is demonstrated through a series of design studies and CFD analyses were performed to verify some of the analysis results.

A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

DOE PAGES

Tuo, Rui; Jeff Wu, C. F.

2016-07-19

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the Lmore » 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.« less

Supervised Learning for Dynamical System Learning.

PubMed

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Orthographic Software Modelling: A Novel Approach to View-Based Software Engineering

NASA Astrophysics Data System (ADS)

Atkinson, Colin

The need to support multiple views of complex software architectures, each capturing a different aspect of the system under development, has been recognized for a long time. Even the very first object-oriented analysis/design methods such as the Booch method and OMT supported a number of different diagram types (e.g. structural, behavioral, operational) and subsequent methods such as Fusion, Kruchten's 4+1 views and the Rational Unified Process (RUP) have added many more views over time. Today's leading modeling languages such as the UML and SysML, are also oriented towards supporting different views (i.e. diagram types) each able to portray a different facets of a system's architecture. More recently, so called enterprise architecture frameworks such as the Zachman Framework, TOGAF and RM-ODP have become popular. These add a whole set of new non-functional views to the views typically emphasized in traditional software engineering environments.

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE PAGES

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.; ...

2017-09-07

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Applicability Analysis of Validation Evidence for Biomedical Computational Models

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

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Lateral ventricle morphology analysis via mean latitude axis.

PubMed

Paniagua, Beatriz; Lyall, Amanda; Berger, Jean-Baptiste; Vachet, Clement; Hamer, Robert M; Woolson, Sandra; Lin, Weili; Gilmore, John; Styner, Martin

2013-03-29

Statistical shape analysis has emerged as an insightful method for evaluating brain structures in neuroimaging studies, however most shape frameworks are surface based and thus directly depend on the quality of surface alignment. In contrast, medial descriptions employ thickness information as alignment-independent shape metric. We propose a joint framework that computes local medial thickness information via a mean latitude axis from the well-known spherical harmonic (SPHARM-PDM) shape framework. In this work, we applied SPHARM derived medial representations to the morphological analysis of lateral ventricles in neonates. Mild ventriculomegaly (MVM) subjects are compared to healthy controls to highlight the potential of the methodology. Lateral ventricles were obtained from MRI scans of neonates (9-144 days of age) from 30 MVM subjects as well as age- and sex-matched normal controls (60 total). SPHARM-PDM shape analysis was extended to compute a mean latitude axis directly from the spherical parameterization. Local thickness and area was straightforwardly determined. MVM and healthy controls were compared using local MANOVA and compared with the traditional SPHARM-PDM analysis. Both surface and mean latitude axis findings differentiate successfully MVM and healthy lateral ventricle morphology. Lateral ventricles in MVM neonates show enlarged shapes in tail and head. Mean latitude axis is able to find significant differences all along the lateral ventricle shape, demonstrating that local thickness analysis provides significant insight over traditional SPHARM-PDM. This study is the first to precisely quantify 3D lateral ventricle morphology in MVM neonates using shape analysis.

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies.

PubMed

Koch, Lisa M; Rajchl, Martin; Bai, Wenjia; Baumgartner, Christian F; Tong, Tong; Passerat-Palmbach, Jonathan; Aljabar, Paul; Rueckert, Daniel

2017-08-22

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

Low-rank structure learning via nonconvex heuristic recovery.

PubMed

Deng, Yue; Dai, Qionghai; Liu, Risheng; Zhang, Zengke; Hu, Sanqing

2013-03-01

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce how to combine the widely used ℓp norm (0 < p < 1) and log-sum term into the framework of low-rank structure learning. Although the proposed optimization is no longer convex, it still can be effectively solved by a majorization-minimization (MM)-type algorithm, with which the nonconvex objective function is iteratively replaced by its convex surrogate and the nonconvex problem finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iterations. The proposed model is applied to solve two typical problems: robust principal component analysis and low-rank representation. Experimental results on low-rank structure learning demonstrate that our nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 < p < 1) for both data with higher rank and with denser corruptions.

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

PubMed Central

Jha, Abhinav K.; Mena, Esther; Caffo, Brian; Ashrafinia, Saeed; Rahmim, Arman; Frey, Eric; Subramaniam, Rathan M.

2017-01-01

Abstract. Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis. PMID:28331883

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

PubMed Central

Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

2012-01-01

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits. PMID:22536398