Inflight icing data gathering during routine flight operations — a case study

2001 ◽  
Vol 105 (1051) ◽  
pp. 501-516 ◽  
Author(s):  
A. P. Brown
Keyword(s):  
Data Gathering ◽  
Winter Season ◽  
Leading Edge ◽  
General Aviation ◽  
Data Sets ◽  
Large Droplet ◽  
Event Data ◽  
Flight Operations ◽  
Lower Corner

Abstract For the purpose of the design and certification of inflight icing protection systems for transport and general aviation aircraft, the eventual re-definition/expansion of the icing environment of FAR 25/JAR 25, Appendix C is under consideration. Such a re-definition will be aided by gathering as much inflight icing event data as reasonably possible, from widely-different geographic locations. The results of a 12-month pilot programme of icing event data gathering are presented. Using non-instrumented turboprop aircraft flying upon mid-altitude routine air transport operations, the programme has gathered observational data from across the British Isles and central France. By observing a number of metrics, notably windscreen lower-corner ice impingement limits, against an opposing corner vortex-flow, supported by wing leading edge impingement limits, the observed icing events have been classified as ‘small’, ‘medium’ or ‘large’ droplet. Using the guidance of droplet trajectory modelling, MVD values for the three droplet size bins have been conjectured to be 15, 40 and 80mm. Hence, the ‘large’ droplet category would be in exceedance of FAR/JAR 25, Appendix C. Data sets of 117 winter-season and 55 summer-season icing events have been statistically analysed. As defined above, the data sets include 11 winter and five summer large droplet icing encounters. Icing events included ‘sandpaper’ icing from short-duration ‘large’ droplets, and a singular ridge formation icing event in ‘large’ droplet. The frequency of ‘large’ droplet icing events amounted to 1 in 20 flight hours in winter and 1 in 35 flight hours in summer. These figures reflect ‘large’ droplet icing encounter probabilities perhaps substantially greater than previously considered. The ‘large’ droplet events were quite localised, mean scale-size being about 6nm.

Overcoming Challenges of International Migration Research: A Case Study Approach in Southern Mexico

2016 ◽  
Vol 13 (1) ◽  
pp. 131-143
Author(s):  
Julie Boyles
Keyword(s):  
International Migration ◽  
Common Ground ◽  
Data Gathering ◽  
Ethnographic Case Study ◽  
Case Study Approach ◽  
Southern Mexico ◽  
Mixed Methods Approach ◽  
Study Approach ◽  
Migration Research

An ethnographic case study approach to understanding women’s actions and reactions to husbands’ emigration—or potential emigration—offers a distinct set of challenges to a U.S.-based researcher.  International migration research in a foreign context likely offers challenges in language, culture, lifestyle, as well as potential gender norm impediments. A mixed methods approach contributed to successfully overcoming barriers through an array of research methods, strategies, and tactics, as well as practicing flexibility in data gathering methods. Even this researcher’s influence on the research was minimized and alleviated, to a degree, through ascertaining common ground with many of the women. Research with the women of San Juan Guelavía, Oaxaca, Mexico offered numerous and constant challenges, each overcome with ensuing rewards.

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

scholarly journals Rapid, remote and responsive research during COVID-19

2021 ◽  
Vol 14 (1) ◽  
pp. 205979912110085
Author(s):  
Jane Richardson ◽  
Barry Godfrey ◽  
Sandra Walklate
Keyword(s):  
Criminal Justice ◽  
Social Science ◽  
Health Research ◽  
Applied Research ◽  
Data Gathering ◽  
Policy And Practice ◽  
Research And Innovation ◽  
The Uk ◽  
Practical Recommendations

In March 2020, the UK Research and Innovation announced an emergency call for research to inform policy and practice responses to the COVID-19 pandemic. This call implicitly and explicitly required researchers to work rapidly, remotely and responsively. In this article, we briefly review how rapid response methods developed in health research can be used in other social science fields. After outlining the literature in this area, we use the early stages of our applied research into criminal justice responses to domestic abuse during COVID-19 as a case study to illustrate some of the practical challenges we faced in responding to this rapid funding call. We review our use of and experience with remote research methods and describe how we used and adapted these methods in our research, from data gathering through to transcription and analysis. We reflect on our experiences to date of what it means to be responsive in fast-changing research situations. Finally, we make some practical recommendations for conducting applied research in a ‘nimble’ way to meet the demands of working rapidly, remotely, responsively and, most importantly, responsibly.

scholarly journals The DWQ-EMR Embedded Tool to Enhance the Family Physician-Caregiver Connection: A Pilot Case Study

Geriatrics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
Kristina Marie Kokorelias ◽  
Einat Danieli ◽  
Sheila Dunn ◽  
Sid Feldman ◽  
David Patrick Ryan ◽  
...  
Keyword(s):  
Family Caregivers ◽  
Best Practice ◽  
Data Gathering ◽  
Family Physicians ◽  
Efficient Manner ◽  
Efficient Access ◽  
Increased Risk ◽  
Starting Point ◽  
To Receive

The number of family caregivers to individuals with dementia is increasing. Family physicians are often the first point of access to the health care system for individuals with dementia and their caregivers. Caregivers are at an increased risk of developing negative physical, cognitive and affective health problems themselves. Caregivers also describe having unmet needs to help them sustain care in the community. Family physicians are in a unique position to help support caregivers and individuals with dementia, but often struggle with keeping up with best practice dementia service knowledge. The Dementia Wellness Questionnaire was designed to serve as a starting point for discussions between caregivers and family physicians by empowering caregivers to communicate their needs and concerns and to enhance family physicians’ access to specific dementia support information. The DWQ aims to alert physicians of caregiver and patient needs. This pilot study aimed to explore the experiences of physicians and caregivers of people using the Questionnaire in two family medicine clinics in Ontario, Canada. Interviews with physicians and caregivers collected data on their experiences using the DWQ following a 10-month data gathering period. Data was analyzed using content analysis. Results indicated that family physicians may have an improved efficacy in managing dementia by having dementia care case specific guidelines integrated within electronic medical records. By having time-efficient access to tailored supports, family physicians can better address the needs of the caregiver–patient dyad and help support family caregivers in their caregiving role. Caregivers expressed that the Questionnaire helped them remember concerns to bring up with physicians, in order to receive help in a more efficient manner.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

scholarly journals Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data

2016 ◽  
Vol 41 (4) ◽  
pp. 357-388 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Anna Rhodes
Keyword(s):  
Head Start ◽  
Randomized Trial ◽  
Treatment Effect ◽  
External Validity ◽  
Randomized Trials ◽  
Target Population ◽  
Data Sets ◽  
Common Data Elements ◽  
Ex Post

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

Design and performance of deep well dewatering: a case study

1998 ◽  
Vol 35 (1) ◽  
pp. 81-95 ◽  
Author(s):  
D W Sargent ◽  
R D Beckie ◽  
G Smith
Keyword(s):  
Soil Mechanics ◽  
Data Gathering ◽  
Monitoring Program ◽  
Treatment Plant ◽  
Pumping Test ◽  
Observational Method ◽  
Design Development ◽  
Deep Well ◽  
And Performance

This paper reviews the process used to design the construction dewatering system at the Influent Pumping Station at Annacis Island Wastewater Treatment Plant. The design process followed the "observational method," as applied to soil mechanics by K. Terzaghi and set out by R.B. Peck in the Ninth Rankine Lecture. The design was based on a working hypothesis of behaviour anticipated under the most probable conditions identified in the data gathering and assessment program. The sensitivity of the design was evaluated by considering potentially unfavourable conditions evident in the available data. The design development included a review of monitoring feedback obtained during the pumping-well installation, a pumping test, and the dewatering system start-up. The monitoring program and review process are presented.Key words: dewatering, observational method, case study, pumping test.

scholarly journals Empirical Underidentification with the Bifactor Model: A Case Study

2017 ◽  
Vol 78 (5) ◽  
pp. 717-736 ◽  
Author(s):  
Samuel Green ◽  
Yanyun Yang
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Bifactor Model ◽  
Data Sets ◽  
Bifactor Models

Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.

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