Tootfinder

From Licensing to Open Access: Designing a Sustainable Transition in Operational Weather Data
Emma Pidduck, Umberto Modigliani, Victoria L. Bennett, Fabio Venuti, Florian Pappenberger, Florence Rabier
https://arxiv.org/abs/2605.21673 https://arxiv.org/pdf/2605.21673 https://arxiv.org/html/2605.21673
arXiv:2605.21673v1 Announce Type: new
Abstract: This translational article documents the European Centre for Medium-Range Weather Forecasts (ECMWF) transition from a restricted data licensing model to open access under CC BY 4.0, completed in October 2025. The policy context included EU open data requirements and alignment with international data exchange frameworks. The transition was implemented through a tiered service model that kept core forecast data open while offering operationally supported delivery as a cost-recovered service. Between 2020 and 2025, ECMWF executed an iterative planning cycle: setting an annual target for revenue reduction, specifying additions to the open tier under that target, provisioning infrastructure, and assessing outcomes to update assumptions. Drawing on internal administrative records (2014 - 2025), we describe design choices, operational constraints, and early outcomes. In the six months following the end of the transition, more than 93% of previously paying organisations retained a Service Agreement, while open endpoint download volumes increased substantially. We discuss trade-offs in defining the open tier (resolution, parameters, schedule), the reduction of compliance overheads formerly associated with redistribution restrictions, and the scalability implications of global distribution. We note an emerging sustainability question as AI-based forecast products become freely available. The early evidence is consistent with the view that a tiered service model can be designed to reconcile open-access obligations with operational sustainability, subject to monitoring over longer contract renewal cycles (typically annual).
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Designing single-layer PDMS devices for micron to millimeter-scale deformations
Leon Valentin Gebhard, Alexandre S. Avaro, Gabriel Amselem, Charles N. Baroud
https://arxiv.org/abs/2605.17402 https://arxiv.org/pdf/2605.17402 https://arxiv.org/html/2605.17402
arXiv:2605.17402v1 Announce Type: new
Abstract: The elasticity of PDMS has played a central role in advancing important microfluidic technologies, ranging from early valves to sophisticated organ-on-a-chip systems. However, most deformable microfluidic devices are based on geometries that require complex multi-layer PDMS architectures and include thin membranes, leading to difficult microfabrication and poor stability. Recently, Jain, Belkadi et al. (Biofabrication 16.3 (2024): 035010) introduced a single-layer device in which a wide and long microfluidic channel was deformed by controlling the pressure in two independent and adjacent air chambers. While they demonstrated the ability to deform the channel ceiling to compress biological materials, the design parameters remain unexplored. Here, we perform a numerical study on 14,336 variants of this device and identify the height of the PDMS layer, the width of the microchannel and the width of the air chamber as the main features that determine the ceiling deformation. Three deformation modes are observed as the geometrical parameters are varied: A U shape with a central minimum, a W shape with two minima and a central maximum, or an inverse U shape with an upward-bulging single maximum. The numerical results are validated in experiments that reproduce the three shapes for the predicted geometries and demonstrate vertical ceiling deformations ranging from a few microns to the millimeter scale. The generality of this approach is demonstrated for two example applications: A fully closing single-layer microfluidic valve and an optical lens of controllable anisotropy. This work leverages the rapid prototyping enabled by 3D printing or micro-milling to open new perspectives in microfluidic actuation.
toXiv_bot_toot

A study of ~1,500 US workers finds AI use can reduce burnout but also cause "AI brain fry", a mental fatigue from using AI tools beyond one's cognitive capacity (Harvard Business Review)
https://hbr.org/2026/03/when-using-ai-leads-to-brain-fry

When Using AI Leads to “Brain Fry”
As firms increasingly incentivize employees to build and oversee complex teams of agents—for example, by measuring and rewarding token consumption as a proxy for performance—people are finding themselves pushed to their cognitive limits. Participants in a recent study described a “buzzing” feeling or a mental fog with difficulty focusing, slower decision-making, and headaches. The authors call this phenomenon “AI brain fry,” defined as mental fatigue from excessive use or oversight…

from my link log —
Finding a goroutine bug with TLA .
https://www.hillelwayne.com/post/tla-golang/
saved 2020-09-25 https://dotat.a…

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