Open-Innovation in the Electrical and Electronic Industries: Engineered Bio-Fluid Compositions Are Paving the Way, and Testing Therewith
Bibliographic record
Abstract
Engineered Biofluids are paving the way to industrialized and specifically produced and tailored functional fluids such as coolant and dielectric, high purity traceable media, for the electrical and electronic industries. Dielectric fluid compositions are commonly used in electrical devices, particularly in transformers. These liquids have the aim to isolate the various conductive elements of the device and to limit the heating of the equipment during its operation; in order to minimize, or maintain at higher power, the size of the device and to increase the lifespan of it. Concomitantly a number of bio and traditional processing advancements are made associated with pioneering process technologies which are outlined within the prime context of this paper. Enzyme Engineering and Cocktailing add “A New Dimension to Softer Greener Chem-Bio Approaches” which are referenced beyond curiosity purpose. Some of them are possibly “revolutionary” more than evolutionary. Testing has to evolve accordingly to appreciate the challenges ahead in semi-extreme conditions which are relevant to climate changes as well. A significant part of this orientation work addresses and exemplifies these necessary testing innovations, likely adaptable to smart and responsive connecting, and further down the line bigger data role and learning machine evolutionary concepts. One other innovative part of this exploratory work, is the influence that such illustrative localized testing, with integrated sensors/induced-tracers, and online interpretation, may have on the smart grid developments; whereby production, transmission, distribution and consumption of energy can be made more reliable, more effective and more predictive and can also have an impact on the performance, reliability and sustainability of the equipment itself. Those technologies and associated testing can support environmental, technological and societal awareness; helping to revert some trends being climate changes, fossil fuel preservation and other planetary challenges to maintain the overall and localized fundamental equilibriums.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".