W.A.T.E.R. – a structured approach for training on advanced measurement and experimental research

W.A.T.E.R. stands for Workshop on Advanced measurement Techniques and Experimental Research. It is an initiative started in 2016, in the scope of the Experimental Methods and Instrumentation (EMI) committee of the International Association for Hydroenvironment Research (IAHR) aimed to advance the use of experimental techniques in hydraulics and fluid mechanics research. It provides a structured approach for the learning and training platform to postgraduate students, young researchers, and professionals with an experimental background in fluid mechanics. It offers an opportunity to learn about state-of-the-art instrumentation and measurement techniques and the latest developments in the field by partnering with manufacturers. The W.A.T.E.R. brings together academics, instrumentation manufacturers, and public sectors in a structured setting to share knowledge and to learn from good practices. It is about training people that already have certain knowledge and skill level but need to go deeper and/or wider in the field of measurement and experimental research.

MORPHOLOGICAL ASPECT IN TRANSLATING THERMODYNAMIC TERMINOLOGY

Several studies on translation have been carried out, namely on the problem of untranslation, translation of terms from various fields, and the formation of target language terms with spelling adjustments. One of them is the field of thermodynamics which is part of the field of Mechanical Engineering, which has many terms borrowed from Dutch and English. Therefore, the researchers are interested in investigating the morphological aspects of the translation of thermodynamic terms using the natural borrowing technique. This study used qualitative research methods. Researchers took terminology data from two books, namely The Fundamental of Engineering Thermodynamics and Fluid Mechanics. The results showed that the forms of borrowing that occurred were (1) borrowing by adjusting spelling and pronunciation adjustments; (2) borrowing with spelling adjustment without pronunciation adjustment; (3) borrowing without spelling adjustment, but with pronunciation adjustment; (4) adjustments to the spelling of prefixes and bound forms found 15 forms of adjustment; (5) suffix spelling adjustments found 20 forms of adjustment; and (6) a combination of translation and borrowing. In short, morphological aspects in translating thermodynamics terms are very important because they relate to the technique used.

Applying machine learning to study fluid mechanics

This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics. Graphic abstract

The Fluid Mechanics of Liquid Democracy

Liquid democracy is the principle of making collective decisions by letting agents transitively delegate their votes. Despite its significant appeal, it has become apparent that a weakness of liquid democracy is that a small subset of agents may gain massive influence. To address this, we propose to change the current practice by allowing agents to specify multiple delegation options instead of just one. Much like in nature, where—fluid mechanics teaches us—liquid maintains an equal level in connected vessels, we seek to control the flow of votes in a way that balances influence as much as possible. Specifically, we analyze the problem of choosing delegations to approximately minimize the maximum number of votes entrusted to any agent by drawing connections to the literature on confluent flow. We also introduce a random graph model for liquid democracy and use it to demonstrate the benefits of our approach both theoretically and empirically.