Praktische toepassingen van FinTech (FinTech=de digitale transformatie van, met name, de financieel-zakelijke dienstverlening)
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Het huidige wetsvoorstel inzake de km-heffing is uiterst oneerlijk, lost de files niet op en maakt het voor de maatschappij als geheel slechts complexer, intransparanter en veel duurder. Overheveling van de bpm naar de algemene lasten ("bereikbaarheidsheffing"), afschaffing van het kentekenonderscheid en overheveling van de houderschapsbelasting naar de accijnzen kunnen zonder noemenswaardige kosten worden doorgevoerd en zijn veel eerlijker dan de km-heffing.
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The finding of poor lighting conditions in nursing homes in combination with a high prevalence of visual problems (with cataract found to be the most common age related pathology), stretches the need of enhanced awareness of eye care by professional caregivers.
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De binnenvaart heeft een marktaandeel van 30% binnen het Nederlandse goederenvervoer en vervult daarbij een beduidende logistieke functie. Schepen zijn goedkoop, veroorzaken geen files en hebben een kleinere carbon-footprint dan vrachtwagens (Bureau Voorlichting Binnenvaart, 2019). Echter, is de betrouwbaarheid van de binnenvaart afhankelijk van de klimatologische omstandigheden. In extreem droge tijden kunnen schepen minder lading vervoeren in verband met de diepgang en kunnen schepen met een diepgang van boven de drie meter niet alle drempels passeren. Dit zorgt voor extra druk op het wegennet en verhoogd de transportkosten van ondernemingen. De Bedrijvenkring Zutphen en Provincie Gelderland hebben bereikbaarheid als speerpunt. Daarnaast werkt het Deltaprogramma Rijn aan toekomstscenario’s en adaptatiestrategieën die anticiperen op lange termijn klimaatverandering. De droogte van 2018 heeft er mede toe geleid dat hierbij ook expliciete aandacht is voor eventuele effecten van veranderende rivierafvoeren op de binnenvaart.
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
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
