Application of Qualitative Methods in Health Research: An Overview

Qualitative research is type of formative research that includes specialized techniques for obtaining in-depth responses about what people think and how they feel. It is seen as the research that seeks answer to the questions in the real world. Qualitative researchers gather what they see, hear, read from people and places, from events and activities, with the purpose to learn about the community and to generate new understanding that can be used by the social world. Qualitative research have often been conducted to answer the question “why” rather than “what”. A purpose of qualitative research is the construction of new understanding. Here, we present an overview of application of qualitative methods in health research. We have discussed here the different types of qualitative methods and how we and others have used them in different settings/scenarios; sample size and sampling techniques; analysis of qualitative data; validity in qualitative research; and ethical issues

Logical Concurrency Control from Sequential Proofs

We are interested in identifying and enforcing the isolation requirements of a concurrent program, i.e., concurrency control that ensures that the program meets its specification. The thesis of this paper is that this can be done systematically starting from a sequential proof, i.e., a proof of correctness of the program in the absence of concurrent interleavings. We illustrate our thesis by presenting a solution to the problem of making a sequential library thread-safe for concurrent clients. We consider a sequential library annotated with assertions along with a proof that these assertions hold in a sequential execution. We show how we can use the proof to derive concurrency control that ensures that any execution of the library methods, when invoked by concurrent clients, satisfies the same assertions. We also present an extension to guarantee that the library methods are linearizable or atomic

Investigation of Direct Force Control for Planetary Aerocapture at Neptune

In this work, a direct force control numerical predictor-corrector guidance architecture is developed to enable Neptune aerocapture using flight-heritage blunt body aeroshells. A linear aerodynamics model is formulated for a Mars Science Laboratory-derived aeroshell. The application of calculus of variations shows that the optimal angle of attack and side-slip angle control laws are bang-bang. A closed-loop numerical predictor-corrector direct force control guidance algorithm is developed and numerically simulated using the Program to Optimize Simulated Trajectories II. The Monte Carlo simulated trajectories are demonstrated to be robust to the modeled dispersions in aerodynamics, atmospheric density, and entry state. An aerocapture technology trade study demonstrates that blunt body direct force control aerocapture enables similar performance as slender body bank angle control but halves the peak g-loading

Multi-echelon Repair Inventory Systems: Select Issues in Modular Electronic Equipment

Flow of modules/printed circuit boards (PCBs) in a multi-echelon repair inventory system pertaining to modular electronic equipment for large maintenance organisations having large inventory in range and depth, like defence has been critically examined using a case study. Desirable features of the proposed system are identified and a general framework suggested for examining its feasibility and implementation in an organisation. An analytical model with an objective to reduce number of echelons is also suggested and compared with the base model. It is suggested that various models can be compared through simulation and their performance measured using balanced scorecard approach.Defence Science Journal, 2010, 60(5), pp.514-524, DOI:http://dx.doi.org/10.14429/dsj.60.57

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The Effect of Turbulence Modeling on the Mixing Characteristics of Several Fuel Injectors at Hypervelocity Flow Conditions

CFD analysis is presented on the effects of turbulence modeling choices on the mixing characteristics and performance of three fuel injectors at hypervelocity flow conditions. The analyses were carried out with the VULCAN-CFD solver using Reynolds-Averaged Simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The three injectors are the baseline configurations used in the experiments and represent three categories of injectors typically considered individually or in combination for fueling high-speed propulsive devices. The current work discusses the impact of the turbulence model and the turbulent Schmidt number on the mixing flow field behavior and the mixing performance as described by the one-dimensional values of the Mach number, total pressure recovery, and the mixing efficiency. Because planar laser induced fluorescence (PLIF) images are available from the EIMP experiments, the sensitivity of the synthetic LIF signal to turbulence modeling choices is also examined to determine whether PLIF can be extended beyond its intended qualitative visualization purpose and used to guide CFD turbulence model and parameter selections. It is found that the mixing performance, as quantified using mixing efficiency, exhibits a strong sensitivity to both turbulence model choice and turbulent Schmidt number value. However, the synthetic LIF signal only demonstrates a modest level of sensitivity, which suggests that PLIF is of limited use for guiding CFD turbulence model and parameter selections

An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

Evaluating the informatics for integrating biology and the bedside system for clinical research

pre-printBackground: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive. Methods: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone. Results: We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre-or post-processing, modifications to the hive metadata, etc. Conclusion: The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data