We developed an application which allows learners to construct qualitative representations of dynamic systems to aid them in learning subject content knowledge and system thinking skills simultaneously. Within this application, we implemented a lightweight support function which automatically generates help from a norm-representation to aid learners as they construct these qualitative representations. This support can be expected to improve learning. Using this function it is not necessary to define in advance possible errors that learners may make and the subsequent feedback. Also, no data from (previous) learners is required. Such a lightweight support function is ideal for situations where lessons are designed for a wide variety of topics for small groups of learners. Here, we report on the use and impact of this support function in two lessons: Star Formation and Neolithic Age. A total of 63 ninth-grade learners from secondary school participated. The study used a pretest/intervention/post-test design with two conditions (no support vs. support) for both lessons. Learners with access to the support create better representations, learn more subject content knowledge, and improve their system thinking skills. Learners use the support throughout the lessons, more often than they would use support from the teacher. We also found no evidence for misuse, i.e., 'gaming the system', of the support function.
Personalization, production on-demand, and flexible manufacture facilities are growing within the European apparel sector, supported by national and regional public policy. These developments seem to embody a much waited “paradigm shift” in the fashion industry; a shift from global to local scale, from quantity to quality and from standard products to personalized services. Such values, however, are far from new, and scholars have already pointed out the similarities between emerging and pre-industrial systems of production and consumption. This article argues that in order to understand current developments in historical context, we should return to the process of industrialization of the apparel industry during the turn from the 19th to the 20th C, taking into account aspects of production as much as mediation and consumption. With this aim in mind, the article traces the rise of ready-made garments in the Netherlands and northwest Europe, and the associated decline in custom- and home-made garments in the region. Although available statistical data is insufficient to accurately map these phenomena, secondary sources suggest that both processes were not simultaneous and therefore there was not a straightforward substitution of custom- and home-made clothing by ready-mades. While availability and trade of mass-produced ready-mades was escalating since the early 19th C, it was not until mid 20th C that custom- and home-made clothing declined among the middle class. In this study, such a gap is explained by a steady increase in the amount of clothes acquired per person: an expanding culture of consumption during the period under consideration may have enabled these different systems to flourish all together. A parallelism of the findings above with current developments suggests that we should not regard emergent industrial formats as substitutionary of established ones, but as complementary. We may then reevaluate to what extent does the rise of the flexible factory enable a “revolution”, a shift from a problematic present to a contrasting and desirable future. This historical overview indicates that, on the contrary, emerging product-service-systems manufacturing personalized garments on-demand must be considered in relation to – and in coexistence with- traditional industrial models.
Recently several attempts were undertaken to unite the field of metaphor studies, trying to reconcile the conceptual/cognition and linguistic/discourse approaches to metaphor (Hampe, 2017b). The dynamic view of metaphor espoused by amongst others Gibbs (2017a) as a way to unify the field of metaphor studies is said to converge on findings and theoretical predictions found in cognition and discourse approaches. The author argues this focus on dynamical models to explain the multi-scale socio-cognitive aspects of metaphor as an emergent phenomenon is not robust enough. Complexity and dynamical systems are merely a modelling technique to deploy theory for empirical testing of hypotheses; a dynamic view of metaphor needs a coherent background theory to base its dynamic modelling of metaphor in action on (Chemero, 2009). I argue that it can be successfully based on the ecological-enactive framework available within the modern paradigm of 4E cognitive science. This framework makes possible explanation of both 'lower' cognition and 'higher' cognition emerging in the interaction of an organism with its environment. In addition, I sketch how recent theoretical insights from ecological-enactivism (Baggs and Chemero, 2018) concerning Gibson's notion of environment apply to the attempted unification of the field of metaphor studies. I close by suggesting how an understanding of metaphor as an ecological affordance of the socio-cultural environment can provide a rich basis for empirical hypotheses within a dynamical science of metaphor.
The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
DISCO aims at fast-tracking upscaling to new generation of urban logistics and smart planning unblocking the transition to decarbonised and digital cities, delivering innovative frameworks and tools, Physical Internet (PI) inspired. To this scope, DISCO will deploy and demonstrate innovative and inclusive urban logistics and planning solutions for dynamic space re-allocation integrating urban freight at local level, within efficiently operated network-of-networks (PI) where the nodes and infrastructure are fixed and mobile based on throughput demands. Solutions are co-designed with the urban logistics community – e.g., cities, logistics service providers, retailers, real estate/public and private infrastructure owners, fleet owners, transport operators, research community, civil society - all together moving a paradigm change from sprawl to data driven, zero-emission and nearby-delivery-based models.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
