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Wings for three speed regimes with potential for efficient flight are investigated. We compute and analyse our own data for the designs in search of a coherent explanation for why these aircraft are the shapes they are for the tasks they have to perform. The subcritical speed case is a straight, high-aspect-ratio wing designed to maintain attached flow to the trailing edge. Two swept wings for supercritical flow are studied: the first one is from the late 1940s, when transonic problems were not understood. The second is a modern transport wing (Common Research Model (CRM)) showing what was learned in 70 years of transonic wing design. Attached flow is harder to sustain since shock waves interacting with the boundary layer may cause premature separation and drag increase. The slender Mach 2 Concorde-like example is marked by its low-aspect-ratio delta-like wing. This class breaks the paradigm of attached flow. Instead, the design creates a lift-enhancing controlled vortex separating from the leading edge, as seen also on modern fighters. Most of the work analyzes a given shape for aerodynamic performance. In our discussion of the CRM wing, we examine the minimum wave drag shape produced by mathematical optimization to learn how the optimizer changed the geometry.
The General Framework requires case studies to progress its development. Case studies of HCI design knowledge can be successful or unsuccessful. Successful case-studies are considered to fall within the scope of the design knowledge being applied. Unsuccessful case studies are considered not to fall within its scope. Thus, successful and unsuccessful case studies together define the scope of the application of HCI design knowledge. Case studies are of two types: of the framework itself and of the HCI knowledge, acquired with its support by means of HCI research. In turn, these two types of case study can be divided into acquisition and validation case studies. The latter types of case study have yet to be carried out for the General Framework, comprising concepts of discipline, general as common, general problem, particular scope, general research, general knowledge and general practices. However, on the basis of case studies reported in the literature, and the validation proposal made here, suggestions are made as to the research needed to conduct such case studies.
This research textbook, designed for young Human-Computer Interaction (HCI) researchers beginning their careers, surveys the research models and methods in use today and offers a general framework to bring together the disparate concepts. HCI spans many disciplines and professions, including information science, applied psychology, computer science, informatics, software engineering and social science making it difficult for newcomers to get a good overview of the field and the available approaches. The book's rigorous 'approach-and-framework' response is to the challenge of retaining growth and diversification in HCI research by building up a general framework from approaches for Innovation, Art, Craft, Applied, Science and Engineering. This general framework is compared with other HCI frameworks and theories for completeness and coherence, all within a historical perspective of dissemination success. Readers can use this as a model to design and assess their own research frameworks and theories against those reported in the literature.
The Clinical and Translational Science Awards (CTSA) program of the National Center for Advancing Translational Sciences (NCATS) seeks to improve population health by accelerating the translation of scientific discoveries in the laboratory and clinic into practices for the community. CTSAs achieve this goal, in part, through their pilot project programs that fund promising early career investigators and innovative early-stage research projects across the translational research spectrum. However, there have been few reports on individual pilot projects and their impacts on the investigators who receive them and no studies on the long-term impact and outcomes of pilot projects.
The Georgia CTSA funded 183 pilot projects from 2007 to 2015. We used a structured evaluation framework, the payback framework, to document the outcomes of 16 purposefully-selected pilot projects supported by the Georgia CTSA. We used a case study approach including bibliometric analyses of publications associated with the selected projects, document review, and investigator interviews.
These pilot projects had positive impact based on outcomes in five “payback categories”: (1) knowledge; (2) research targeting, capacity building, and absorption; (3) policy and product development; (4) health benefits; and (5) broader economic benefits.
Results could inform our understanding of the diversity and breadth of outcomes resulting from Georgia CTSA-supported research and provide a framework for evaluating long-term pilot project outcomes across CTSAs.
Focusing on process tracing and using the example of fieldwork in Donbas, I develop an argument on what theoretically grounded and empirically detailed methodological solutions can be considered to mitigate the challenges of research on conflict zones and assure the robustness of any causal claims made. I first outline my assumptions about process tracing as the central case study method and its application to research on conflict zones, and then discuss in more detail data requirements, data collection, and data analysis. Using two examples of case studies on the war in and over Donbas, I illustrate how three standards of best-practice in process tracing—the need for a theory-guided inquiry, the necessity to enhance causal inference by paying attention to (and ruling out) rival explanations, and the importance of transparency in the design and execution of research—can be applied in the challenging circumstances of fieldwork-based case studies of conflict zones. I conclude by suggesting that as a minimum threshold for reliance upon causal inferences, these three standards also should align with a standard of evidence that requires both the theoretical and empirical plausibility of any conclusions drawn.
Case Learning for Teachers: Strategic Knowledge for Professional Experience is a unique resource for Australian pre-service educators that draws on the author's experiences as an education researcher, lecturer and classroom teacher. This textbook uses a case stories approach to support pre-service teachers in developing the skills of observation and reflective practice necessary for professional experience placements and the transition to the classroom. Part 1 introduces the case learning approach and outlines strategies for reading and writing case stories. Part 2 is structured by the Australian Professional Standards for Teachers. The text includes case stories addressing topics like knowing your students, knowing content, planning for teaching, managing behaviour, diverse learners, assessment, and developing professional relationships in the school setting. Integrating threshold concepts and the case-learning model, the innovative approach taken by Case Learning for Teachers makes it an invaluable tool for pre-service teachers.
Stories are an everyday part of our lives: we make sense of the world through the stories we hear and tell. This chapter explores how you can build on your experience in storytelling and your knowledge of expository writing to write thought-provoking case stories. You will learn about the benefits of dividing story writing into two processes: ‘writing it down’ or gathering observations, and ‘writing it up’ or drawing conclusions and making compositional decisions. Separating observations from conclusions enables you to create stories that are rich in layers of detail. These details make them more ‘real’, and better represent the complex web of conditions and participants’ perspectives that characterise teaching situations. After reviewing the elements of narrative, you will learn to use a simple five-step model, the ‘SISDA’ steps, to help select, develop and refine your case story. You will also explore three variants of the SISDA steps. Opportunities for collaboration in writing and refining case stories will be highlighted throughout the chapter.
You might be tempted to skip or skim this chapter and jump straight into the case stories. This is understandable as the whole premise of this book is that learning through stories is often more inviting than learning with discursive texts. However, if you do choose to jump into the case stories first, you need to return to this chapter later in order to stand back and see the broader landscape across which you are travelling as you read and write case stories. This metacognitive distance will deepen your understanding of the ways case learning helps you build your skills in problem solving, perspective taking and conditional thinking, which in turn will help you better develop these foundational skills of reflective teaching practice.
How we think we read stories or real-life situations, and how we actually read them are often very different. This chapter explores what the differences are, and how they can get in the way of effectively interpreting case stories. You will see how applying a systematic approach to reading case stories helps you become more self-aware and skilful in your interpretive practices. Following a systematic approach will enable you to separate observations from interpretations or evaluations and make you less likely to jump to conclusions. The approach presented in this chapter is the ‘SNAAPI’ steps, a simple five-step inductive reasoning–based process that will help you make sense of both the case stories in this book and the real-life situations you will encounter in schools. The chapter will also introduce three variants of the SNAAPI steps that you can use when you want to be more specialised in your engagement with a case story. All the interpretive approaches can be undertaken individually, but you will gain most benefit from discussing your thinking with others at all stages of the process.
Welcome to Case Learning for Teachers: Strategic Knowledge for Professional Experience. This book is primarily aimed at preservice teachers preparing for placement in schools. However, both the case stories and the model of case learning also provide rich professional learning material for teachers at all career stages. The book’s approach stems from the recognition that reading and writing cases builds the kinds of personal and/or professional knowledge that leads to significant change in thinking, attitudes and practice.
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.
This comment concurs with Skarbek's paper that much more room should be made for qualitative evidence in economics. However, it raises questions about the modalities through which case studies could carry general lessons when it comes to broad institutional issues. It also suggests the need to extend the set of qualitative evidence beyond case studies and to complement them with formal approaches as well as with quantitative analysis. Persuading economists to open windows to alternative methods is at stake
− ESG–Agency scholars have embraced the notion that agent influence is complex, contingent, and context dependent, with the success of environmental governance depending considerably on propitious environmental and social conditions. − Scholars have shifted from an earlier focus on how agents influence behaviours and environmental quality in earth system governance to how they influence governance processes, with increasing focus on democracy, participation, legitimacy, transparency, and accountability. − ESG–Agency scholars employ increasingly diverse methods to integrate insights from case studies, interviews, surveys, statistical analyses, and other approaches leading to deeper and more nuanced understanding of agency in earth system governance. − Adopting more interdisciplinary, multidisciplinary, and transdisciplinary approaches to evaluating agency can foster future understandings of and contributions to earth system governance.
We look at how to design a comparative interpretive project and tackle the perennial case selection question. The problem here is one of justifying unorthodox comparison: a lot has been written on comparative case selection from a naturalist perspective, but this language is often an uncomfortable fit for interpretive projects. We argue that case selection is not something that is designed into a project from inception. For interpretive research, it changes as we go. We therefore suggest different strategies of case selection for different phases of a comparative interpretive project. We identify rules of thumb to guide design choices as the project or programme evolves.
Chapter 2 – Sense-making analysis – discusses the study’s theoretical and methodological foundations from the perspective of dialogical communication theory and the literature on sense-making resources. The chapter discusses the roles of narratives, framing, categorization and metaphors in sense-making. The book’s different empirical materials are described: peer reviewed research literature, policy documents, international media texts and focus group interviews.
A clearly formulated research question is vital in science because it determines the data we need to collect, the methods we use, and, ultimately, the success of a project. Developing a research question is an iterative process of reading and thinking, as we define a problem and specify the contribution we hope to make to resolving it. This is not easy, and we learn through experience, and (if we’re lucky) from our mentors. In this chapter I first explain research questions and the case studies we use to address them, then look at where questions come from. I examine what makes a good research question and end with why reading is essential to the development of research ideas
Modular design allows to reduce costs based on scaling effects. However, due to strong alternating effects between the resulting modules and products, methods and tools are required that enable engineers to use specific views in which the respective information can be linked and retrieved according to the situation. Within the scope of this paper, the model-based systems engineering (MBSE) approach is used to model the complex real-world problem of vehicle modular kits. The aim is to investigate the potentials in this context, how modular kits and products can be efficiently modeled and finally how MBSE can support modular design. In order to investigate this in detail, two extensive studies are carried out in a company over a period of three years. The studies show that modular kits lead to an increased complexity of development. Across industries and companies, the demand for reference product models is shown, which facilitate the unification of inhomogeneous partial models and serve as a knowledge repository for the development of future product generations. On this basis, a framework is derived which enables the reuse of large proportions of the product models of previous product generations. This framework is evaluated on the basis of five case studies.
The aim of this study was to conduct a systematic literature review to ascertain whether cognitive behavioural therapy (CBT) for social anxiety disorder (SAD) can be successfully used in non-Western contexts and demonstrate sufficient effectiveness. This area is largely under-researched with conflicting evidence presented in quantitative studies, with virtually no qualitative studies published. This review utilized realist review methodology and focused on qualitative case studies presented by clinicians. A systematic search of EBSCO HOST, The Cochrane Library Database, Google, Google Scholar and reference mining, using various combinations of terms relating to: (1) CBT, (2) social anxiety and (3) cultural diversity were employed. Seven case studies of cultural adaptations of CBT treatment for culturally diverse SAD sufferers were included. The treatment outcomes were generally promising in all cases (reporting significant decrease of SAD symptoms, maintained over time) and the success of therapy was often attributed to culturally specific modifications introduced. CBT can be an acceptable and effective treatment for culturally diverse SAD sufferers with ‘modest’ modifications, without major diversions from the original CBT models and protocols, but this finding must be treated with caution and more methodologically rigorous research (qualitative and quantitative) is needed to more fully understand what works, for whom and in what circumstances.