Challenges that surveys are facing are increasing data collection costs and declining budgets. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done into the effect on quality and costs of reducing the use of interviewers in mixed-mode surveys starting with internet observation, followed by telephone or face-to-face observation of internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. Currently, adaptive survey design is an option in redesigns of social surveys at Statistics Netherlands. In 2018 it has been implemented in the Health Survey and the Public Opinion Survey, in 2019 in the Life Style Monitor and the Leisure Omnibus, in 2021 in the Labour Force Survey, and in 2022 it is planned for the Social Coherence Survey. This paper elaborates on the development of the adaptive survey design for the Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and the six-month parallel design with corresponding response results.
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In Eastern Africa, increasing climate variability and changing socioeconomic conditions are exacerbating the frequency and intensity of drought disasters. Droughts pose a severe threat to food security in this region, which is characterized by a large dependency on smallholder rain-fed agriculture and a low level of technological development in the food production systems. Future drought risk will be determined by the adaptation choices made by farmers, yet few drought risk models … incorporate adaptive behavior in the estimation of drought risk. Here, we present an innovative dynamic drought risk adaptation model, ADOPT, to evaluate the factors that influence adaptation decisions and the subsequent adoption of measures, and how this affects drought risk for agricultural production. ADOPT combines socio-hydrological and agent-based modeling approaches by coupling the FAO crop model AquacropOS with a behavioral model capable of simulating different adaptive behavioral theories. In this paper, we compare the protection motivation theory, which describes bounded rationality, with a business-as-usual and an economic rational adaptive behavior. The inclusion of these scenarios serves to evaluate and compare the effect of different assumptions about adaptive behavior on the evolution of drought risk over time. Applied to a semi-arid case in Kenya, ADOPT is parameterized using field data collected from 250 households in the Kitui region and discussions with local decision-makers. The results show that estimations of drought risk and the need for emergency food aid can be improved using an agent-based approach: we show that ignoring individual household characteristics leads to an underestimation of food-aid needs. Moreover, we show that the bounded rational scenario is better able to reflect historic food security, poverty levels, and crop yields. Thus, we demonstrate that the reality of complex human adaptation decisions can best be described assuming bounded rational adaptive behavior; furthermore, an agent-based approach and the choice of adaptation theory matter when quantifying risk and estimating emergency aid needs.
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Adaptive survey design has attracted great interest in recent years, but the number of case studies describing actual implementation is still thin. Reasons for this may be the gap between survey methodology and data collection, practical complications in differentiating effort across sample units and lack of flexibility of survey case management systems. Currently, adaptive survey design is a standard option in redesigns of person and household surveys at Statistics Netherlands and it has been implemented for the Dutch Health survey in 2018. In this article, the implementation of static adaptive survey designs is described and motivated with a focus on practical feasibility.
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The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Het doel van dit interdisciplinaire SIA KIEM project Fluïde Eigenschap in de Creatieve Industrie is te onderzoeken of en hoe gedeelde vormen van eigenaarschap in de creatieve industrie kunnen bijdragen aan het creëren van een democratischer en duurzamer economie, waarin ook het MKB kan participeren in digitale innovatie. Het project geeft een overzicht van beschikbare vormen van (gedeeld) eigenaarschap, hun werking en hoe deze creatieve professionals kunnen ondersteunen bij de transitie naar de platformeconomie. Dit wordt toegepast op een concrete case, dat van een digitale breimachine. Naast het leveren van een goede praktijk, moet het project leiden tot een groter internationaal onderzoeksvoorstel over Fluid Ownership in the Creative Industry, dat dieper ingaat op de beschikbare eigendomsoplossingen en hoe deze waarde zullen creëren voor de creatieve professional.
The textile industry contributes over 8% of global greenhouse gas emissions and 20% of the world's wastewater, exceeding emissions from international flights and shipping combined. In the European Union, textile purchases in 2020 resulted in about 270 kg of CO₂ emissions per person, yet only 1% of used clothes are recycled into new garments.To address these challenges, the Textile Hub Groningen (THG) aims to assist small and medium-sized enterprises (SMEs) and stakeholders in forming circular textile value chains, hence reducing waste. Designing circular value chains is complex due to conflicting interests, lack of shared understanding, knowledge gaps regarding circular design principles and emerging technologies, and inadequate tools for collaborative business model development. The potential key stakeholders in the circular textile value chain find it hard to use existing tools and methods for designing these value chains as they are often abstract, not designed to be used in a collaborative setting that fosters collective sense making, immersive learning and experimentation. Consequently, the idea of circular textile value chain remains abstract and hard to realize.Serious games have been used in the past to learn about, simulate and experiment with complex adaptive systems. In this project we aim to answer the following research:How can serious games be leveraged to design circular textile value chains in the region?The expected outcomes of this project are: • Serious game: Facilitates the design of circular textile value chains• Academic Publication: Publish findings to contribute to scholarly discourse.• Future Funding Preparation: Mobilize partners and prepare proposals for follow-up funding to expand the approach to other domains.By leveraging game-based collaborative circular value chain and business model design experiences, this project aims to overcome barriers in designing viable circular value chains in the textile industry.
