The Leading Impact of the Application of Nanotechnology on Reducing the Expected Production Costs in the Jordanian Industrial Companies
Bibliographic record
Abstract
Nanotechnology and nanomaterials are some of the most important modern concepts that when economic units enter into production, they will offer products with features that are superior to those produced in a traditional costing manner. Given the scientific progress and technological development that have led to increased competition, the industrial companies need to Apply concepts that help introduce nanomaterials into production and reap the benefits ,Therefore, This study aimed to find the leading impact of the application of Nanotechnology on reducing production costs in Jordanian industrial companies. The sampling unit in this study consists of the financial managers and cost managers of the listed industrial companies In Amman Financial Market, where the number of financial directors and cost managers of industrial companies was (105) , and (80) questionnaires have been distributed to companies, (75) questionnaires were retrieved , (5) questionnaires were incomplete, thus the number of questionnaires recovered and were ready for analysis were (70) questionnaires, The most important result of the study was the impact of the application of nanotechnology dimensions combined on reducing costs in the Jordanian industrial companies, while the most important recommendation of the study was the need of Jordanian industrial companies management's commitment to the moral values Of Nanotechnology to reach local and global industrial leadership. With the need of them to recognize the importance of the workforce in the deployment and adaptation of Nanotechnology to reach a global industry level. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".