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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Adaptive governance describes the purposeful collective actions to resist, adapt, or transform when faced with shocks. As governments are reluctant to intervene in informal settlements, community based organisations (CBOs) self-organize and take he lead. This study explores under what conditions CBOs in Mathare informal settlement, Nairobi initiate and sustain resilience activities during Covid-19. Study findings show that CBOs engage in multiple resilience activities, varying from maladaptive and unsustainable to adaptive, and transformative. Two conditions enable CBOs to initiate resilience activities: bonding within the community and coordination with other actors. To sustain these activities over 2.5 years of Covid-19, CBOs also require leadership, resources, organisational capacity, and network capacity. The same conditions appear to enable CBOs to engage in transformative activities. How-ever, CBOs cannot transform urban systems on their own. An additional condition, not met in Mathare, is that governments, NGOs, and donor agencies facilitate, support, and build community capacities. This is the peer reviewed version of the following article: Adaptive governance by community-based organisations: Community resilience initiatives during Covid‐19 in Mathare, Nairobi. which has been published in final form at doi/10.1002/sd.2682. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
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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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Het RAAK-MKB project Aerobic heeft zich gericht op modulaire robotica (grippers, handling en vision systemen) en specifiek binpicking. Binnen dit project is veel kennis opgedaan die heeft geresulteerd in diverse fysieke demonstrators (robotopstellingen t.b.v. binpicking). Deze nieuw opgedane kennis is erg bruikbaar voor zowel de beroepspraktijk als studenten. Daarnaast is deze kennis praktisch gemaakt en laagdrempelig toepasbaar. Dat maakt het relevant voor (her)gebruik middels het nieuwe open-acces e-learning platform van Fontys: Open Learning Labs. Door trainingsmateriaal te ontwikkelen dat betrekking heeft op onder andere het aspect “binpicking” met behulp van robots, worden toekomstige engineers (onze studenten) en zittend personeel bij bedrijven bekend met nieuwe technieken die toepasbaar zijn in diverse sectoren waar met robots gewerkt wordt. Het doel van deze Top-up aanvraag is tweeledig: 1) het vergroten van de zichtbaarheid van de resultaten uit het initiële RAAK-project, zowel richting onderwijs, onderzoek en beroepspraktijk. 2) het realiseren van trainingsmateriaal t.b.v. het praktisch toepassen van kennis die betrekking heeft op de gerealiseerde binpicking-demonstrator binnen het RAAK project. Dit zal bij toekenning stapsgewijs uitgevoerd worden: 1. Definiëren inhoud lesmodule en bijbehorende didactische werkvormen 2. Realisatie PR- & instructievideo's en onderwijsopdrachten 3. Realisatie E-learning lesmodule Dit alles gekoppeld aan het open-acces e-learning platform Open Learning Labs van Fontys.
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.
