A Qualitative and Quantitative Analysis of the Geriatrics Update: Clinical Pearls Course

Background The University of Calgary Cumming School of Medicine Annual Geriatrics Update: Clinical Pearls Course (Geriatrics Update) is a one-day, continuing medical education (CME) course designed to enhance geriatrics competency for family physicians (FPs), given increasing population age and complexity. We aimed to evaluate how the course meets FPs’ perceived learning needs and identify modifications that may better support FPs. Methods Descriptive data from 2018–2019 course evaluation surveys including demographic data, evaluations, and narrative feedback from participating FPs. Semi-structured phone and video-conferenced interviews with FPs were thematically analyzed each year. Results Evaluation surveys had high response rates of FPs (52 or 61% in 2018; 39 or 58% in 2019). Most FP respondents (84% in 2018 and 82% in 2019) intended to make practice changes. FPs were significantly (p=.001) more confident on course objectives after the course in both years. All interviewees (n=20) described fulfilled perceived and unperceived learning needs and planned to return. The Geriatrics Update course is the primary source of Geriatrics CME for 60% of interviewees. Conclusions Iterative evaluation of Geriatrics Update identified that the course is well received, and often FPs primary source of geriatric CME. Interviews provided additional context and descriptive feedback to improve course delivery and better meet FP learning needs.

Looking for Change? Roll the Dice and Demand Attention

Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, a new attention module, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new, memory efficient, spatial and channel convolution Attention layer: the FracTAL. We introduce two new efficient self-contained feature extraction convolution units: the CEECNet and FracTALResNet units. Further, we propose a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. The key insight in our approach is to facilitate the use of relative attention between two convolution layers in order to fuse them. We validate our approach by showing excellent performance and achieving state-of-the-art scores (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets.

A technique for evaluating and improving the semantic transparency of modeling language notations

The notation of a modeling language is of paramount importance for its efficient use and the correct comprehension of created models. A graphical notation, especially for domain-specific modeling languages, should therefore be aligned to the knowledge, beliefs, and expectations of the targeted model users. One quality attributed to notations is their semantic transparency, indicating the extent to which a notation intuitively suggests its meaning to untrained users. Method engineers should thus aim at semantic transparency for realizing intuitively understandable notations. However, notation design is often treated poorly—if at all—in method engineering methodologies. This paper proposes a technique that, based on iterative evaluation and improvement tasks, steers the notation toward semantic transparency. The approach can be efficiently applied to arbitrary modeling languages and allows easy integration into existing modeling language engineering methodologies. We show the feasibility of the technique by reporting on two cycles of Action Design Research including the evaluation and improvement of the semantic transparency of the Process-Goal Alignment modeling language notation. An empirical evaluation comparing the new notation against the initial one shows the effectiveness of the technique.

Experimental Determination of Heat Transfer Coefficient on Impingement Cooled Gear Flanks: Validation of the Evaluation Method

Understanding the heat transfer characteristics of impingement cooling of high-speed high-power gears is essential to design a reliable gearbox for a new generation of jet engines. However, experimental data on the impingement cooling of gears is limited in the literature. The experimental setup at the Institute of Thermal Turbomachinery aims at closing this gap. It includes a rotating gear instrumented with thermocouples. The measured temperatures are used to determine a spatially resolved heat transfer coefficient distribution on the gear tooth. The iterative evaluation approach applied in the post-processing of the experimental data is validated with two reference cases. First, it is shown that the interpolation of temperature data between thermocouple locations leads to inaccurate results and would not be valid for the evaluation of the experiments, even if the number of thermocouples were increased. The iterative evaluation approach can reproduce the reference heat transfer coefficient distributions very accurately even with a low spatial resolution of temperature data. A new iterative method based on the Levenberg-Marquardt algorithm is implemented within this study. The new method generally converges faster than the existing method. The difference in required computational time is negligible in the easy to evaluate high heat transfer case, whereas a speed-up of up to three times is observed in the relatively cumbersome evaluation of the low heat transfer case.

The CURE for Introductory, Large Enrollment, and Online Courses

A course-based undergraduate research experience (CURE) in a large, introductory course was offered online and in person at an open-door community college. Seated students collaborated during class, and online students collaborated asynchronously at the same pace over 8 wks.This study demonstrates how reflective and iterative evaluation and improvement in CURE integration for introductory courses and non-STEM majors across delivery formats develop best practices for broadening participation.

On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool

Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool’s reliability when assessing a MUI’s structural design problems with 71% accuracy.

A Systematic Review of Design Creativity in the Architectural Design Studio

Creativity is fundamental to design problem-solving. This paper sets out a systematic review of the literature in relation to its role in the architectural design studio in order to identify central issues that impact upon this activity. Challenges and best practices in relation to systematic reviews are outlined, and the procedure followed in this context is set out in detail. This involves an iterative evaluation process that resulted in a pool of 17 papers for analysis. Eleven themes emerged in the analysis of the papers, which were organized into five key categories dealing with: pedagogy, cognitive approach, interaction and socialization, information representation, and measuring ideation and creativity. A discussion of these categories contributed to the comparison and connections between the selected papers, and the identification of critical issues and directions for promoting creativity in the architectural design studio.

“Replanning” a Statewide Walking Program Through the Iterative Use of the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework

Background: Interventions undergo adaptations when moving from efficacy to effectiveness trials. What happens beyond these initial steps—that is, when the “research” is over—is often unknown. The degree to which implementation quality remains high and impacts remain robust is underreported as these data are often less valued by community entities. Comprehensive and iterative evaluation is recommended to ensure robust outcomes over time. Methods: The reach, effectiveness, adoption, implementation, and maintenance framework was used within an assess, plan, do, evaluate, report process to determine the degree to which a statewide physical activity promotion program aligned with evidence-based core components, assess who was reached and impacts on physical activity behaviors, and make decisions for future iterations. Results: Walk Across Arkansas was adopted by a majority of delivery agents and was effective at increasing physical activity levels postprogram, but those effects were not maintained after 6 months. Future decisions included recruitment strategies to reach a more diverse population and a blueprint document to reduce program drift. Conclusions: This article details the process of “replanning” a community-based physical activity intervention to understand public health impact and make decisions for future iterations. Pragmatic reach, effectiveness, adoption, implementation, and maintenance questions were useful throughout the assess, plan, do, evaluate, report process.