Big data analytics received much attention in the last decade and is viewed as one of the next most important strategic resources for organizations. Yet, the role of employees' data literacy seems to be neglected in current literature. The aim of this study is twofold: (1) it develops data literacy as an organization competency by identifying its dimensions and measurement, and (2) it examines the relationship between data literacy and governmental performance (internal and external). Using data from a survey of 120 Dutch governmental agencies, the proposed model was tested using PLS-SEM. The results empirically support the suggested theoretical framework and corresponding measurement instrument. The results partially support the relationship of data literacy with performance as a significant effect of data literacy on internal performance. However, counter-intuitively, this significant effect is not found in relation to external performance.
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OBJECTIVE: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2peak) and peak power output (Wpeak).DESIGN: Cross-sectional study.SETTING: Multicenter.PARTICIPANTS: Cancer survivors (N=283) in 2 randomized controlled exercise trials.INTERVENTIONS: Not applicable.MAIN OUTCOME MEASURES: Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions.RESULTS: ICCs of SRT-predicted Vo2peak and Wpeak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo2peak and ±59W with a bias of 5W for Wpeak. Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of Wpeak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for Wpeak.CONCLUSIONS: Predictions of Vo2peak and Wpeak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for Wpeak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription.
Background: A Dutch nationwide prospective cohort study was initiated to investigate recovery trajectories of people recovering from coronavirus disease 2019 (COVID-19) and costs of treatment by primary care allied health professionals. Objectives: The study described recovery trajectories over a period of 12 months and associated baseline characteristics of participants recovering from COVID-19 who visited a primary care allied health professional. It also aimed to provide insight into the associated healthcare and societal costs. Methods: Participants completed participant-reported standardized outcomes on participation, health-related quality of life, fatigue, physical functioning, and costs at baseline (ie, start of the treatment), 3, 6, 9 and 12 months. Results: A total of 1451 participants (64 % women, 76 % mild/moderate severity) with a mean (SD) age of 49 (12) years were included. Linear mixed models showed significant and clinically relevant improvements over time in all outcome measures between baseline and 12 months. Between 6 and 12 months, we found significant but not clinically relevant improvements in most outcome measures. Having a worse baseline score was the only baseline factor that was consistently associated with greater improvement over time on that outcome. Total allied healthcare costs (mean €1921; SEM €48) made up about 3% of total societal costs (mean €64,584; SEM €3149) for the average participant in the cohort. Conclusions: The health status of participants recovering from COVID-19 who visited an allied health professional improved significantly over a 12-month follow-up period, but nearly the improvement occurred between baseline and 6 months. Most participants still reported severe impairments in their daily lives, and generated substantial societal costs. These issues, combined with the fact that baseline characteristics explained little of the variance in recovery over time, underscore the importance of continued attention for the management of people recovering from COVID-19. Trial registration: clinicaltrials.gov
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Entangled Machines is a project by Mariana Fernández Mora that interrogates the colonial and extractive legacies underpinning artificial intelligence (AI). By introducing slowness and digital kinship as critical frameworks, the project reconceptualises AI as embedded within intricate social and ecological networks, thereby contesting dominant narratives of efficiency and optimisation. Through participatory, practice-based methodologies such as the Material Playground, the project integrates feminist and non-Western epistemologies to articulate alternative models for ethical, sustainable, and equitable AI practices. Over a four-year period, Entangled Machines develops theory, engages diverse communities, and produces artistic outputs to reimagine human-AI interactions. In collaboration with partners including ARIAS Amsterdam, Archival Consciousness, and the Sandberg Institute, the research seeks to foster decolonial and interdisciplinary approaches to AI. Its culmination will be an “Anarchive” – a curated assemblage of artistic, theoretical, and archival outputs – that serves as a resource for rethinking AI’s socio-political and ecological impacts.
Het RaakPRO project INTRALOG (1/9/2015-31/8/2019) heeft inzicht opgeleverd in de toepassing van autonoom rijden op distributiecentra (DCs). Het project heeft door de samenwerking van hogescholen en universiteiten met bedrijven, kenniscentra en branchevertegenwoordigers, op de thema’s businessmodellen en technologische innovatie in de ontwikkeling van autonoom rijden, een uniek resultaat bereikt. Een autonoom rijdende Yard Truck, gestuurd door Multi Agent System voor het beheer van de logistieke infrastructuur op een distributiecentrum. De aansturing en organisatie van de externe logistiek op een distributiecentrum (DC) door een Multi Agent Systeem (MAS), borgt een optimale inzet van autonoom rijdende Yard Trucks. In de ontwikkeling van het MAS is onderzoek uitgevoerd bij de logistieke partners van INTRALOG. Doel was inzicht in de logistieke eisen en randvoorwaarden vervoersbewegingen op DCs en deze te vertalen in kritische prestatie indicatoren (KPIs) voor de autonome Yard Trucks. De realisatie van een autonoom rijdende Yard Truck op modelschaal is gedaan door de ontwikkeling van een rij-robot (controller) die in staat is om los van de infrastructuur een Yard Truck autonoom over en binnen een bestaande infrastructuur te dirigeren. De randvoorwaarden voor het uitvoeren van de voertuigbewegingen van de Yard Truck, zijn voortgekomen uit de KPIs die bepaald zijn aan de hand van de onderzochte businessmodellen. Centraal in de Top-Up staan twee aspecten: 1) de ontwikkeling van een video waarin het autonoom dokken centraal staat dat gestuurd wordt door een MAS-applicatie en 2) een seminar gericht op de toepassing van het resultaat van INTRALOG op distributiecentrums. De doelstelling van de Top-up is primair het verspreiden van het gedachtegoed van INTRALOG, maar ook het: o Vergroten van het netwerk van het consortium van INTRALOG; en o Demonstreren om versneld tot een real life product te komen.
This proposal aims to explore a radically different path towards a more sustainable fashion future through technology. Most research on fashion and technology focuses on high tech innovation and, as a result, overlooks knowledge that is already available and has been used, tested and improved for centuries. The proposed research project, however, looks backward to move forward. It aims to investigate ‘the blindingly obvious’ and asks the question how historical technologies could be used to solve contemporary environmental issues in fashion. It thus argues that technology from the past could inspire both designers and technologists to come up with new and exciting solutions to make the future of fashion more sustainable. The current fast fashion system has changed the relationship consumers have with their clothing. Clothing has become a throwaway object and this has severe environmental implications. This research project aims to find a solution by exploring historical technologies - such as folding, mending and reassembling-, because in the past a ‘sustainable’ attitude towards fashion was the norm simply because cloth and garments were expensive. It wants to examine what happens when consumers, fashion designers and technologists are confronted with these techniques. What would, for example, materialize when an aeronautical engineer takes the technique of folding as a starting point and aims to create clothes that can grow with babies and toddlers? The answer is the signature suit of the brand Petit Pli: a special folding technique allows their signature suit to grow with children from 3 months to 3 years. Much like the age-old folding techniques applied in traditional Dutch dress, which allowed the size women’s jackets to be altered, by simply adjusting the pleats. Similarly, this project aims to investigate how high tech solutions, can be initiated through historical techniques.
