Coupling Interval Hyper-Active Drying (IHAD) with Instant Controlled Pressure Drop (D.I.C.) to define new swell-drying processes
Bibliographic record
Abstract
To respond to the various issues of conventional drying (textural defects of collapse-shrinkage and case hardening, microbiological contamination, poor rehydrability, degradation of active molecules caused by high wet-bulb temperature, weak drying kinetics, and marketing difficulty), swell-drying has proven high relevance in combining conventional drying processes with the Instant Controlled Pressure Drop (D.I.C.). The international team of "Research & Engineering Platform for intensifying Drying Processes (REPID)" has studied phenomenological models and experimental trials combining D.I.C. with new Interval Hyper-Active Drying (IHAD) processes. Currently, IHAD includes interval starting accessibility drying (ISAD), interval infrared airflow drying (IIRAD), and interval microwave airflow drying (IMAD). All these "interval operations" are sandwiched into cycles, each comprising double separate independent mechanisms; a short, hyper-focused active period (that generates and sweeps out vapor to the surrounding environment) alternated by a passive period of internal water diffusion/moisture homogenization. Since surface evaporation is highly intensive, the wet-bulb temperature stays low, although highly effective drying kinetics. Thus, the operation results in avoiding biochemical damage risks. Coupled with the D.I.C. texturing-decontamination, these drying operations are very effective in terms of drying kinetics, energy consumption, and product quality of heat-sensitive solids. It has led to the manufacturing of effective drying equipment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".