BLOCKTRUST FOR CLOUD MANUFACTURING
Cloud manufacturing is a service-oriented cyber-physical system for inter-organizational collaboration which integrates various recognized engineering paradigms (i.e. IoT, digital twins, capability) with the power of cloud computing. It is a centralized system for sharing different kinds of capabilities, where the system holds a pool containing soft capabilities (e.g. such as optimization, consultancy services, and cyber-based facility control), and hard capabilities (e.g. products and services). It works under “Pay as You Go” terminology and every enrolled form can share or get few capabilities from the pool.
Although significant advancements have been achieved in using real-time data for performance improvement in CM, there are unsolved issues for how to apply the real-time data-driven decision to trust problems in CM due to an increase in process complexity and unpredictable exceptions. This issue is summarized as follows. How to design a new and effective CM framework to offer real-time trust score for an IoT-enabled flexible job shop as a provider or a consumer of a service. Blockchain Technology (BCT) is an emerging decentralized and transactional data sharing technology across a network of untrusted participants. It is developed to reach agreement among collaborating parties without a central authority assistant. BCT peer to peer networks may provide a fully trusted system by running autonomous smart contracts. An effective trust system in CM affects many aspects of the system operations including reductions on search to partners, more effective search for a capability, improvements in cloud computing utilization, and faster and easier disaster recovery. With all these benefits, CM needs a universal, feasible, and empirical BCT-based trust system.
To address the above-mentioned challenge, a new trust system is developed to provide trust to the collaborators by extending the BCT to the CM field. Under this system, capabilities can be embedded with trust scores. Then the nodes of the network can interact with each other in the selection, during, and after collaboration stages. The proposed peer to peer trust system is called “Blocktrust”, working in such a way that no central authority contributed to the trust, but it is maintained.
Blockchain enabled Learning management system
Learning management system (LMS) is a platform for presenting course content, related online resources, supplying assignments and quizzes, communication and discussion among teachers and students, evaluation, and developing collaboration and teamwork in an online learning environment. Some of the commonly used LMS in the higher education are Moodle, Docebo, TakentLMS, and Looop.
This project intends to develop an adaptive learning management system embedded by blockchain technology. The blockchain-based SRL adaptive metacognitive learning management system can gain the background information, individual needs, and goals learners through pre and post-test SRL questionnaires, reflective essay, and SRL indicators during the course activity by trace data. Furthermore, this approach, by identifying learners’ background information, and pre-test and traces data can predict learners’ weaknesses and failures and provide desired SRL adaptive intervention and teachers’ feedback to support them at the beginning and throughout the course towards learning achievement.
Blockchain technology application in clinical research
The estimations reported that the costs of developing a new drug exceed 1.2 billion dollars and take an average of 12 years from creation to market, causing pharmaceutical companies to experience a pronounced “profitably gap”. As pressures on the industry continue to mount, quality and safety issues become a greater concern. Companies should build safety, quality, and efficacy into their new pharmaceutical products as early as possible.
This work attempt to provides a blockchain-based QbD architecture in the clinical trial stage of drug development to improve the quality of clinical trials by first, design and developing the safety control strategies of the critical quality attributes in clinical trials using the knowledge available on the blockchain ledger and second, conducting the trials according to the safety control strategies restrictions and providing the knowledge to the blockchain ledgers, and third, correlating control strategies restrictions of the trials with real safety-related knowledge, and finally re-design and updating control strategies of the critical quality attributes.