"In het kader van het Programma Samenwerking DJI-3RO zijn zomer 2019 onder de noemer ‘Effectieve Praktijken’ experimenten gestart in negen verschillende Penitentiaire Inrichtingen (Heerhugowaard, Nieuwegein, Roermond, Schiphol, Veenhuizen, Vught, Zaanstad, Zutphen, Zwolle). Daarnaast startte in januari 2020 het project ‘Selectie Ondersteunend Model (SOM)’ in twee andere Penitentiaire Inrichtingen (Arnhem, Sittard). Doel van beide initiatieven is het betrekken van de specifieke expertise van de reclassering om re-integratietrajecten voor gedetineerden te versterken en terugval in delinquent gedrag te helpen voorkomen. Het (intensiveren van het) samenwerken met gemeenten en met andere ketenpartners ligt hierbij voor de hand. Hogeschool Utrecht (HU) heeft van september 2019 tot en met juni 2021 onderzoek gedaan naar de experimenten binnen het project Effectieve Praktijken en van februari 2020 tot en met juni 2021 naar de ontwikkeling van het SOM. Deel 1 van ‘Binnen beginnen om buiten te blijven’ (Eindrapport Effectieve Praktijken en Selectie Ondersteunend Model) beschrijft de eindresultaten van beide onderzoeken."
Voor de komende jaren wordt een toename in elektrisch vervoer voorzien. Naast lichte elektrische vrachtvoertuigen betreft het elektrische bestel- en vrachtwagens met een hoger laadvermogen. Het opladen van die elektrische voertuigen betekent een extra belasting voor de elektrische infrastructuur.Gebruikers weten vaak niet wat ze al aan elektriciteit verbruiken op hun locatie, en (dus) ook niet wat ze nog kunnen uitbreiden met elektrische voertuigen binnen de huidige aansluitvoorwaarden. Door de Hogeschool van Amsterdam is daartoe het EVEC (Electric Vehicle Expansion Calculator) model ontwikkeld. Met informatie over de verschillende laadbehoeften van EV’s en op basis van data van het eigen energieverbruik, (uit de slimme meter of met zelf gemeten data), is met het model inzicht te verkijgen in wat er nog mogelijk is op de locatie.
In order for techniques from Model Driven Engineering to be accepted at large by the game industry, it is critical that the effectiveness and efficiency of these techniques are proven for game development. There is no lack of game design models, but there is no model that has surfaced as an industry standard. Game designers are often reluctant to work with models: they argue these models do not help them design games and actually restrict their creativity. At the same time, the flexibility that model driven engineering allows seems a good fit for the fluidity of the game design process, while clearly defined, generic models can be used to develop automated design tools that increase the development’s efficiency.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
The scientific publishing industry is rapidly transitioning towards information analytics. This shift is disproportionately benefiting large companies. These can afford to deploy digital technologies like knowledge graphs that can index their contents and create advanced search engines. Small and medium publishing enterprises, instead, often lack the resources to fully embrace such digital transformations. This divide is acutely felt in the arts, humanities and social sciences. Scholars from these disciplines are largely unable to benefit from modern scientific search engines, because their publishing ecosystem is made of many specialized businesses which cannot, individually, develop comparable services. We propose to start bridging this gap by democratizing access to knowledge graphs – the technology underpinning modern scientific search engines – for small and medium publishers in the arts, humanities and social sciences. Their contents, largely made of books, already contain rich, structured information – such as references and indexes – which can be automatically mined and interlinked. We plan to develop a framework for extracting structured information and create knowledge graphs from it. We will as much as possible consolidate existing proven technologies into a single codebase, instead of reinventing the wheel. Our consortium is a collaboration of researchers in scientific information mining, Odoma, an AI consulting company, and the publisher Brill, sharing its data and expertise. Brill will be able to immediately put to use the project results to improve its internal processes and services. Furthermore, our results will be published in open source with a commercial-friendly license, in order to foster the adoption and future development of the framework by other publishers. Ultimately, our proposal is an example of industry innovation where, instead of scaling-up, we scale wide by creating a common resource which many small players can then use and expand upon.
