Bibliographic record
Abstract
Blockchains Empowering Technologies and Industrial Applications A comprehensive guide to the most recent developments in blockchains in theoretical and industrial perspectives Originally introduced as a method to keep track of Bitcoin transactions over a peer-to-peer network, blockchain is a continuously growing list of records, called blocks, which are linked and secured using cryptography into a chain held in public databases. The use of this technology has grown since its cryptocurrency creation and now store three types of information: 1) transactions, including the date, time, and value of purchases; 2) records of participates in transactions; and 3) unique code known as a “hash” that distinguishes one block from another. A single block on the blockchain can hold 1 MB of data, or potentially thousands of transactions — this then can allow for hundreds of thousands of transactions to be recorded as each block can join the state-of-the-art blockchain. Blockchains provides a detailed overview of the latest and most innovative concepts, techniques, and applications related to the developing blockchain. Aimed at novices and experts on the subject, the book focuses on blockchain technologies, integrated systems, and use cases, specifically by looking at three major technical areas: blockchain platforms and distributed database technologies, consensus and fault tolerance, and Blockchain as a Service (BaaS). These avenues of research are essential to support blockchain functionalities, such as acquiring and updating existing data, securing data resources and the recovery of failures, and using blockchains in various services that range from cryptocurrencies to cloud automation. Blockchains readers will also find: Brainstorming activities that gradually builds the knowledge of readers on the described technology and deployment scenarios Investigation of specific topics such as novel networking protocols, wireless techniques, new infrastructure designs, operations management, and deployment strategies Discussion of technical challenges in blockchain, as well as how to manage cloud-based networks, service automation, and cyber security Numerous elementary and advanced examples on various topics at the end of the book that can be used for training purposes Illustrations including tables and diagrams to help elucidate points made throughout the volume Glossary of relevant terminology to blockchains in enterprise Blockchains is a useful reference for researchers in vehicular networking and computer science, as well as cloud storage providers and governmental offices for data management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.872 | 0.860 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".