Open Access

Many high-quality educational innovations are freely available, and some are known to motivate evidence-based climate and sustainability action. Typically, eforts to propagate educational innovations rely on outreach and word-of-mouth difusion, but these approaches tend to achieve little. We develop and analyse a dynamic computational model to understand why and to test other propagation strategies. Our analysis reveals that outreach has limited impact and does little to accelerate word-of-mouth adoption under conditions typical in higher education. Instead, we fnd that community-based propagation can rapidly accelerate adoption, as is also shown by a small number of successful real-world scaling eforts. This approach supports a community of ‘ambassadors’, facilitating and rewarding their sharing the innovation with potential adopters. Community-based propagation can generate exponential growth in adopters, rapidly outpacing outreach and word-of-mouth propagation. Without it, we are unlikely to rapidly scale the educational innovations needed to build urgently needed capacity in sustainability.

Human-centric in the context of Industry 5.0 aims to support and empower workers. This paper presents a human-centered approach to workforce dispatching. Thereby, workers’ preferences, skills, availability, and individual strain in the task are considered. To do this, the strain of the workers in the task is predicted using Machine Learning. In this way, the strain on the worker can be reduced by raising autonomy and production can be adapted to the worker’s needs. The practical implementation and evaluation are done in a logistics learning factory.

Der Anteil mittelständischer Unternehmen, die Standorte im Ausland unterhalten, nimmt seit einigen Jahren zu. Oft finden Auslandsaktivitäten dieser Art in Niedriglohnländern statt. Dort ergeben sich u.a durch die infrastrukturellen Gegebenheiten und durch die verfügbaren Personalressourcen diverse Herausforderungen, insbesondere für die Produktivitätsermittlung und -bewertung innerhalb der Produktion. Dieser Beitrag soll für diese Herausforderungen geeignete Technologien und eine mögliche Vorgehensweise für deren Auswahl vor dem Hintergrund der ländertypischen Herausforderungen aufzeigen.

Aimed at the problem that the accuracy of face image classification in complex environment is not high, a network model F-Net suitable for aesthetic classification of face images is proposed. Based on LeNet-5, the model uses convolutional layers to extract facial image features in complex backgrounds, optimized parameters in the network model, and changes the number of convolutional layers and fully connected layer feature elements in the model. The experimental results show that the F-Net network model proposed in this paper has a face image classifation accuracy of 73% in complex environment background, which is better than other classical convolutional neural network classification models.

The present publication reports the purification effort of two natural bone blocks, that is, an allogeneic bone block (maxgraft®, botiss biomaterials GmbH, Zossen, Germany) and a xenogeneic block (SMARTBONE®, IBI S.A., Mezzovico Vira, Switzerland) in addition to previously published results based on histology. Furthermore, specialized scanning electron microscopy (SEM) and in vitro analyses (XTT, BrdU, LDH) for testing of the cytocompatibility based on ISO 10993-5/-12 have been conducted. The microscopic analyses showed that both bone blocks possess a trabecular structure with a lamellar subarrangement. In the case of the xenogeneic bone block, only minor remnants of collagenous structures were found, while in contrast high amounts of collagen were found associated with the allogeneic bone matrix. Furthermore, only island-like remnants of the polymer coating in case of the xenogeneic bone substitute seemed to be detectable. Finally, no remaining cells or cellular remnants were found in both bone blocks. The in vitro analyses showed that both bone blocks are biocompatible. Altogether, the purification level of both bone blocks seems to be favorable for bone tissue regeneration without the risk for inflammatory responses or graft rejection. Moreover, the analysis of the maxgraft® bone block showed that the underlying purification process allows for preserving not only the calcified bone matrix but also high amounts of the intertrabecular collagen matrix.