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MultiversX is a blockchain protocol that seeks to offer extremely fast transaction speeds by using sharding. The project describes itself as a technology ecosystem for the new internet, which includes fintech, decentralized finance and the Internet of Things. Its smart contracts execution platform is reportedly capable of 15,000 transactions per second, six-second latency and a $0.001 transaction cost.

MultiversX rewards the community and active participants with EGLD tokens. EGLD, or Electronic Gold, is MultiversX's native token. It acts as a store of value currency to pay for network usage. The coin also serves as a medium of exchange between platform users and validators. Users pay transaction fees in EGLD and validators provide services.

EGLD allows developers to deploy smart contracts, protocols, and dApps on the platform. It empowers participants to perform any network action. Through staking and validation rewards, as well as transaction fees, EGLD manages the MultiversX network. Plus, EGLD is endowed with the functionality of a governance token, so its holders can vote on network decisions.

MultiversX was first announced in August 2019, and its mainnet went live in July 2020.

Wrapped EGLD

dHedge DAO

NEST Protocol

High Performance Blockchain is the first permissionless public blockchain to innovate with hardware acceleration and provably random numbers:

acceleration delivers fast, ultra low-fee, on-chain transactions

provably random numbers support 100% fairness

HPB (the coin) is used to pay transaction gas fees and to reward node operators.

Hyperblox

CyberVein's big-data solution is based on the PISR (private, interlink, secure, robust) distributed database, database operations are stored on CyberVein blockchain network which runs on a Proof-of-Contribution (PoC) consensus mechanism. The solution is focusing on enterprise-level "blockchain + big-data" customized services.

CVT is the native utility token that is used for:

Storage payment: The data owner pays the corresponding storage fee based on the file size and bandwidth consumed in such a process.

Computing power payment: Payments from user to owner for usage, and remuneration for software developers is going to be exclusively conducted in CVT.

Data exchange: On CyberVein's federated learning platform, the data acquirer trades the data at a price contracted between the two parties for distributed modeling applications.

CROSS NFT issuance payment: Dapp businesses are obligated to pay for Storage and Data Exchange in light of network resources occupied, and may set up CyberVein token as a means of payment to issue NFT or conduct business in accordance to their desired business model.

CyberVein tools include:

PISR Consortium database, creates better data management: through safe and reliable virtual machine operation and maintenance of user data, data is efficiently aggregated for upstream data, and data traceability and tracking are realized to ensure the reliability of data sources and outputs, and achieve cross-level, cross-departmental data sharing to improve the timeliness, diversity, stock and density of data to realize the commercial value of data, and truly solve the problems in data flow.

DAG storage chain, creates safer data storage, with better efficiency, no block confirmation, low transaction fees, and also eliminates miner, supports asynchronous verification and parallel processing of each node, the more nodes, the faster the speed, the stronger the scalability, which leads to better scalability. The database backup on DAG storage chain provides an extra layer of security for businesses.

Cytrix, allows GPU to be shared. Nodes can contribute part of their GPU to the network, in return for compensation through the Proof of Contribution mechanism. The contributed GPU can aid enterprises or individuals in data modeling with federated learning.

Federated Learning Platform, enables data to be monetized. Federated Learning is a distributed machine learning workflow. The datasets are first trained locally, and the data of all parties is kept locally, without revealing privacy or violating regulations.  Individual terminals return trained models to the server to create a shared model. Effectively protect data privacy and security, solve the problem of information silo, and thus realize the value of data. The platform will also provide algorithms to support data modeling operations by enterprises and individuals.

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