Conflict lies at the core of urban sustainability transitions and the indispensable structural changes that accompany them. In this chapter we examine the RESILIO project, a multi-actor collaboration in Amsterdam aiming to transition towards a 'climate proof' city through smart water retention systems on urban roofs. The focus is on the conflict that emerged during discussions about controlling the smart valves on the rooftops which are designed to prevent urban flooding. Using a discourse analytical framework, the study analyses participant interactions, conflicting positions, and discursive strategies employed by the partners involved in the initiative. Participants utilised several discursive strategies, including identity, stake, and accountability management, to manage their positions in the conflict and influence the discourse. The study highlights the challenges of addressing conflict that involves redefining accountability and responsibility between public and private actors in the collaborative setting of transition initiatives. By doing so the findings contribute to a deeper understanding of how conflict can shape learning processes and foster sustainable urban transitions.
In deze rede willen we helder krijgen welke ontwikkelingen in het bedrijfsleven van invloed zijn op ondernemingen en het financieel management van deze ondernemingen. Met name het streven naar een circulaire economie wordt als een ontwikkeling van betekenis gezien.
Ontwikkelingen op het gebied van ICT en digitalisering hebben de afgelopen twee decennia een enorme vlucht genomen. Hoewel op het gebied van finance, controlling en accounting vele interessante kansen liggen, zijn de nieuwe ontwikkelingen slechts in beperkte mate geadopteerd door de beroepspraktijk. Dit komt onder andere door de enorme verbrokkeling van ICT en digitaliseringstechnologie. Daarnaast is er sprake van een enorm kennishiaat in de beroepspraktijk met betrekking tot de technologische mogelijkheden op het gebied van ICT en digitalisering. Daarenboven ontbreken vaak in de opleidingsprogramma’s van de hogescholen en de universiteiten adequate beroepsgerichte ICT en digitaliseringsmodules.
LINK
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is currently used as an important tool for biomedical applications (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this technology and its applications was recently published in a special edition of the Journal of Aerosol Sciences [1]. Even though EHDA is already applied in many different industrial processes, there are not many controlling tools commercially available which can be used to remotely operate the system as well as identify some spray characteristics, e.g. droplet size, operational mode, droplet production ratio. The AECTion project proposes the development of an innovative controlling system based on the electrospray current, signal processing & control and artificial intelligence to build a non-visual tool to control and characterize EHDA processes.
Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
