A Fair and Efficient Transactive Energy Trading Framework Hosting Distributed Ledger Technology
Bibliographic record
Abstract
Blockchain technology (BT) is considered a proven secure platform for financial affairs. However, its applicability as an irrefutable attack-resistive platform for securing peer-to-peer (P2P) networks is a matter of debate among researchers. In this regard, this article concentrates on using the BT for securing fully P2P networks. A new fair and energy-efficient method is presented for consensus achievement in the blockchain layer for fully P2P transactive energy systems. Also, a new product differentiator index is represented in this article for bilateral trading among the agents that is more compatible with the security layer. Finally, a self-adaptive-self-tuning fast alternating direction method of multipliers distributed algorithm is presented to clear the P2P market. Results demonstrate the superiority of the proposed BT's consensus method in terms of fairness and energy efficiency compared to the proof-of-stake and proof-of-work consensus methods and its attack resistivity against Sybil attack and 51% attack.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".