Circular motion problems ucm
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This document contains 6 problems related to circular motion and rotational kinematics. It provides calculations for linear and angular displacement, velocity, and acceleration given information about radius, revolutions, time, and angular displacement. Key calculations include using circumference to find linear displacement from angular displacement, converting between angular velocity units of rev/s, rpm, and rad/s, and using angular velocity and time to find angular displacement.
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This document contains a 14-question physics worksheet on circular motion. The questions involve calculating average velocity, centripetal acceleration, tension in strings, and centripetal force for objects moving in circular paths, including cars on racetracks, balls on strings, masses on strings, and the Earth orbiting the Sun. Formulas provided are used to calculate requested values for speed, acceleration, force, and the effects when a cord breaks.
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Circular motion ws1
This document contains a 14-question physics worksheet on circular motion. The questions involve calculating average velocity, centripetal acceleration, tension in strings, and centripetal force for objects moving in circular paths, including cars on racetracks, balls on strings, masses on strings, and the Earth orbiting the Sun. Formulas provided are used to calculate requested values for speed, acceleration, force, and the effects when a cord breaks.
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Enviar correo a ayudatareas55@gmail.com
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The document discusses various aspects of automatic loom mechanisms, including:
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2. It explains different weft patterning motions like weft mixing, drop box, and circular box motions that allow inserting different weft yarns to create patterns.
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This document defines terms related to angles, circular motion, and problem solving using angles. It provides formulas for calculating arc length from central angle measures, linear speed from arc length and time, and angular speed from angle and time. Examples demonstrate using the formulas to find arc lengths, speeds, and angle measures. Exercises provide additional practice problems involving angles, arc lengths, speeds, and real-world applications like highway signs and latitude/longitude.
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
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