A Blockchain-IoT Framework for Preventing Counterfeit Medical Supplies via Ride-Sharing Networks
Bibliographic record
Abstract
The increasing prevalence of counterfeit drugs and medical supplies poses significant threats to public health, particularly during last-mile delivery.Therefore, to optimize the delivery of critical medical supplies and enhance the authenticity and security of medical supply chains, a novel solution is proposed to integrate Blockchain technology, the Internet of Things (IoT), and ride-sharing applications.Utilizing a Blockchain-based framework with smart contracts facilitates real-time validation of product authenticity.IoT sensors monitor environmental conditions, such as temperature and humidity, ensuring that medical products comply with regulatory requirements.Ride-sharing services facilitate decentralized, efficient delivery of medical products.Additionally, the Proof of Elapsed Time (PoET) consensus mechanism helps reduce energy consumption and ensures fast, secure transactions.The proposed system is prototyped using the Hyperledger Sawtooth platform, and the Hyperledger Caliper benchmarking tool is used to assess performance.The results demonstrate that Blockchain integration with ride-sharing applications significantly reduces the likelihood of counterfeit products entering the supply chain, ensuring safer delivery of medical supplies.Metrics like delivery efficiency, transaction speed, and counterfeit detection rates are validated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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".