Machine learning algorithms have emerged as an effective and popular decision-making tool for solving complicated engineering problems and challenges. Although introducing these algorithms can accelerate the optimization of fire retardants for polymeric materials by replacing traditional tedious and time-consuming trial-and-error methods, this tool remains at the elementary stage of designing fire retardants for polymeric materials, and thus to date there is a lack of insightful yet review on this topic. Herein, we review the most practical and accurate algorithms used to predict flame retardancy features, such as limiting oxygen index (LOI) and cone calorimetry results, of their polymeric materials. We highlight the merits of some current algorithms, including artificial neural network (ANN), Lasso, Ridge, ANN (L-ANN), and extreme gradient boosting (XGB). Finally, key challenges with existing algorithms for predicting next-generation fire retardants, followed by some proposed solution and future directions. This review will help expedite the development of optimized fire retardants accelerated by machine learning.

Existing organ transportation management systems face significant limitations as they do not allow patients to monitor the condition of the transplantable organ during its transportation from the donor's place to the recipient's venue. This undermines patients' confidence that the organ allotted to them remains uncontaminated, healthy, and unaffected by fluctuations in parameters including temperature, humidity, and the container's vibration, and orientation. Additionally, there is a lack of technology to ensure that the organ container remains securely closed throughout the shipment. Furthermore, the shipment data is often stored in centralized or with third-party systems, which are potentially vulnerable to security risks and single points of failure. These limitations highlight the need for a more secure, transparent, and reliable solution to improve organ transportation safety and data integrity. This paper addresses these challenges by proposing a cost-efficient, decentralized, and transparent framework for managing organ transportation that enhances data security and traceability. The proposed framework integrates Internet of Things sensors within the organ container and connects them to smart contracts via the InterPlanetary File System. The blockchain ensures data security, immutability, and decentralization, while sensors safeguard the organ by providing real-time updates on its condition. The smart contracts generate alerts for issues and notify stakeholders for prompt action. A key contribution of this work is the novel use of IPFS for off-chain data storage, which reduces blockchain storage requirements, Ether consumption, and overall system costs. A comparative analysis with existing IoT, IPFS, and blockchain-based transportation approaches demonstrates that the proposed framework consumes a negligible amount of Ether; approximately, 0.000023004, equivalent to approximately 5.52 INR; for deployment, while ensuring safe, transparent, cost-effective, and traceable organ transportation.