Coagulation Abnormalities in Dogs with Parvoviral Enteritis

Corda, Francesca; Ballocco, Isabella; Corda, Andrea; Mollica, Alessandra; Cilano, Anna; Polinas, Marta; Parpaglia, Maria Luisa Pinna

doi:10.3390/vetsci10010041

Cited by 8 publications

(2 citation statements)

References 44 publications

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“…Our work can serve as a foundation to hold similar discussions about other blockchain platforms. For instance, Corda and Multichain, which are two other popular permissioned blockchains, have more than 9 and 15 dynamically adaptable system parameters, respectively [18,19,52]. Corda utilizes notaries to endorse transactions, and distributing transactions across multiple notaries is expected to enhance throughput, similar to Fabric's endorsement policies [17].…”

Section: Technology Independencementioning

confidence: 99%

Why Do My Blockchain Transactions Fail?

Chacko

Mayer

Jacobsen

2021

Proceedings of the 2021 International Conference on Management of Data

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Similar to other transaction processing frameworks, blockchain systems need to be dynamically reconfigured to adapt to varying workloads and changes in network conditions. However, achieving optimal reconfiguration is particularly challenging due to the complexity of the blockchain stack, which has diverse configurable parameters. This paper explores the concept of self-driving blockchains, which have the potential to predict workload changes and reconfigure themselves for optimal performance without human intervention. We compare and contrast our discussions with existing research on databases and highlight aspects unique to blockchains. We identify specific parameters and components in Hyperledger Fabric, a popular permissioned blockchain system, that are suitable for autonomous adaptation and offer potential solutions for the challenges involved. Further, we implement three demonstrative locally autonomous systems, each targeting a different layer of the blockchain stack, and conduct experiments to understand the feasibility of our findings. Our experiments indicate up to 11% improvement in success throughput and a 30% decrease in latency, making this a significant step towards implementing a fully autonomous blockchain system in the future.

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Section: Technology Independencementioning

confidence: 99%

Why Do My Blockchain Transactions Fail?

Chacko

Mayer

Jacobsen

2021

Proceedings of the 2021 International Conference on Management of Data

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“…This research added provenance capturing in real time and predictive analytics using artificial intelligence in which, random forest algorithm was used. The blockchain framework selected for this research was CORDA [34,35], which is lightweight and can be installed, programmed, and executed in a personal laptop having i5 or i7 processor and 16GB of RAM. The blockchain contract rules were configured in the form of predictive auditing such that the updates are accepted only when the risks predicted by AI are within the prescribed limit.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Umer,

Belay,

Gouveia

2023

Preprint

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.Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to Internet opens the cloud manufacturing to the massive attack surface of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this goal by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with the predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks such that the blockchain peers can timely detect high deviations and take actions. The research defined the constraints related to physical boundaries and weight lifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This research followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, booster risk management, strengthen cybersecurity, and advance sustainability efforts.

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