Zoekresultaten

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.

Embodiment of Wearable Technology: Qualitative Longitudinal Study

Background:Current technology innovations, such as wearables, have caused surprising reactions and feelings of deep connection to devices. Some researchers are calling mobile and wearable technologies cognitive prostheses, which are intrinsically connected to individuals as if they are part of the body, similar to a physical prosthesis. Additionally, while several studies have been performed on the phenomenology of receiving and wearing a physical prosthesis, it is unknown whether similar subjective experiences arise with technology.Objective:In one of the first qualitative studies to track wearables in a longitudinal investigation, we explore whether a wearable can be embodied similar to a physical prosthesis. We hoped to gain insights and compare the phases of embodiment (ie, initial adjustment to the prosthesis) and the psychological responses (ie, accept the prosthesis as part of their body) between wearables and limb prostheses. This approach allowed us to find out whether this pattern was part of a cyclical (ie, period of different usage intensity) or asymptotic (ie, abandonment of the technology) pattern.Methods:We adapted a limb prosthesis methodological framework to be applied to wearables and conducted semistructured interviews over a span of several months to assess if, how, and to what extent individuals come to embody wearables similar to prosthetic devices. Twelve individuals wore fitness trackers for 9 months, during which time interviews were conducted in the following three phases: after 3 months, after 6 months, and at the end of the study after 9 months. A deductive thematic analysis based on Murray’s work was combined with an inductive approach in which new themes were discovered.Results:Overall, the individuals experienced technology embodiment similar to limb embodiment in terms of adjustment, wearability, awareness, and body extension. Furthermore, we discovered two additional themes of engagement/reengagement and comparison to another device or person. Interestingly, many participants experienced a rarely reported phenomenon in longitudinal studies where the feedback from the device was counterintuitive to their own beliefs. This created a blurring of self-perception and a dilemma of “whom” to believe, the machine or one’s self.Conclusions:There are many similarities between the embodiment of a limb prosthesis and a wearable. The large overlap between limb and wearable embodiment would suggest that insights from physical prostheses can be applied to wearables and vice versa. This is especially interesting as we are seeing the traditionally “dumb” body prosthesis becoming smarter and thus a natural merging of technology and body. Future longitudinal studies could focus on the dilemma people might experience of whether to believe the information of the device over their own thoughts and feelings. These studies might take into account constructs, such as technology reliance, autonomy, and levels of self-awareness.