Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol

Screening diabetic retinopathy, a major cause of blindness, is time-consuming for ophthalmologists and has some constrains in achieving full coverage and attendance. The handheld fundus camera EyeFundusScope was recently developed to expand the scale of screening, drawing on images acquired in primary care and telescreening made by ophthalmologists or a computer-aided diagnosis (CADx) system. This study aims to assess the diagnostic accuracy of the interpretation of images captured using EyeFundusScope and perform its technical evaluation, including image quality, functionality, usability, and acceptance in a real-world clinical setting. Physicians and nurses without training in ophthalmology will use EyeFundusScope to take pictures of the retinas of patients with diabetes and the images will be classified for the presence or absence of diabetic retinopathy and image quality by a panel of ophthalmologists. A subgroup of patients will also be examined with the reference standard tabletop fundus camera. Screening results provided by the CADx system on images taken with EyeFundusScope will be compared against the ophthalmologists’ analysis of images taken with the tabletop fundus camera. Diagnostic accuracy measures with 95% confidence intervals (CIs) will be calculated for positive and negative test results. Proportion of each category of image quality will be presented. Usability and acceptance results will be presented qualitatively.

Assessing the Evaluability of Adaptation-Focused Interventions: Lessons from the Adaptation Fund

Evaluability assessments (EAs) have differing definitions, focus on various aspects of evaluation, and have been implemented inconsistently in the last several decades. Climate change adaptation (CCA) programming presents particular challenges for evaluation given shifting baselines, variable time horizons, adaptation as a moving target, and uncertainty inherent to climate change and its extreme and varied effects. The Adaptation Fund Technical Evaluation Reference Group (AF-TERG) developed a framework to assess the extent to which the Fund’s portfolio of projects has in place structures, processes, and resources capable of supporting credible and useful monitoring, evaluation, and learning (MEL). The framework was applied on the entire project portfolio to determine the level of evaluability and make recommendations for improvement. This chapter explores the assessment’s findings on designing programs and projects to help minimize the essential challenges in the field. It discusses how the process of EA can help identify opportunities for strengthening both evaluability and a project’s MEL more broadly. A key conclusion was that the strength and quality of a project’s overall approach to MEL is a major determinant of a project’s evaluability. Although the framework was used retroactively, EAs could also be used prospectively as quality assurance tools at the pre-implementation stage.

Enabling Systems Innovation in Climate Change Adaptation: Exploring the Role for MEL

Traditional monitoring, evaluation, and learning (MEL) approaches, methods, and tools no longer reflect the dynamic complexity of the severe (or “super-wicked”) problems that define the Anthropocene: climate change, environmental degradation, and global pandemics. In late 2019, the Adaptation Fund’s Technical Evaluation Reference Group (AF-TERG) commissioned a study to identify and assess innovative MEL approaches, methods, and technologies to better support and enable climate change adaptation (CCA) and to inform the Fund’s own approach to MEL. This chapter presents key findings from the study, with seven recommendations to support a systems innovation approach to CCA: Promote and lead with a CCA systems innovation approach, engaging with key concepts of complex systems, super-wicked problems, the Anthropocene, and socioecological systems. Engage better with participation, inclusivity, and voice in MEL. Overcome risk aversion in CCA and CCA MEL through field testing new, innovative, and often more risky MEL approaches. Demonstrate and promote using MEL to support and integrate adaptive management. Work across socioecological systems and scales. Advance MEL approaches to better support systematic evidence and learning for scaling and replicability. Adapt or develop MEL approaches, methods, and tools tailored to CCA systems innovation.

Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user. The presented platform allows for the utilization of artificial intelligence to analyze vibrations generated by the rotary drive system to determine the technical condition of a wind turbine model monitored by an image processing system that measures frequencies generated by the machine.