Zoekresultaten

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.

Sustainability of innovations in higher education

Innovation is crucial for higher education to ensure high-quality curricula that address the changing needs of students, labor markets, and society as a whole. Substantial amounts of resources and enthusiasm are devoted to innovations, but often they do not yield the desired changes. This may be due to unworkable goals, too much complexity, and a lack of resources to institutionalize the innovation. In many cases, innovations end up being less sustainable than expected or hoped for. In the long term, the disappointing revenues of innovations hamper the ability of higher education to remain future proof. Against the background of this need to increase the success of educational innovations, our colleague Klaartje van Genugten has explored the literature on innovations to reveal mechanisms that contribute to the sustainability of innovations. Her findings are synthesized in this report. They are particularly meaningful for directors of education programs, curriculum committees, educational consultants, and policy makers, who are generally in charge of defining the scope and set up of innovations. Her report offers a comprehensive view and provides food for thought on how we can strive for future-proof and sustainable innovations. I therefore recommend reading this report.

Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area

Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.

What Doughnut Economics means for business

The 21st century’s rapidly compounding crises – from climate and ecological breakdown to extreme social inequities of power and opportunity – make it irrefutably clear that the global economic system must be transformed if humanity and the rest of life on Earth are to thrive. Doughnut Economics provides an increasingly recognised compass for such a thriving future, and is focused on meeting the needs of all people within the means of the living planet, by creating economies that are regenerative and distributive by design. What are the implications for the role and transformation of business, if it is to be part of this future?This paper explores how the deep design of business – through a company’s Purpose, Networks, Governance, Ownership, and Finance – powerfully shapesits strategic decisions and operational impacts, and ultimately determines whether or not businesses can transform to become part of a regenerative and distributive future. By diving into five layers of deep design, this paper reveals both design blockages that prevent transformative action, and design innovations that can unlock its possibility. In addition, this paper recognises that industry-level and system-wide transformations are crucial for turning the inheritance of a degenerative and divisive economic system into the beginnings of a regenerative and distributive one.

Global Diffusion of Montessori Schools

Developments to come to a circular construction economy; experiences in facilitating a local soil and sand depot

The impact of the construction industry on the natural environment is severe, natural areas are changedinto predominantly hard solid surfaces, the energy use in the built environment is high and the industryputs huge claims on materials.