manufacturing data
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2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Diogo Costa ◽  
Miguel Teixeira ◽  
Armando N. Pinto ◽  
José Santos

AbstractIntegration of blockchain systems into industrial applications show promise in increasing security, trust, and transparency along the value-chain during product and process tracking. However, current solutions suffer performance deficiencies, or often disregard legacy devices still in operation. We propose a blockchain system built upon an IoT architecture that is secure, modular, easily scalable, and deployable for fast certification of manufacturing data, compatible with current industrial landscapes. First, the proposed architecture is presented along with elements required to manage network functions. Second, easing integration with existing manufacturing solutions, custom APIs are created and subsequently explained. This grants the platform plug-and-play capabilities, requiring minimal hardware and software configuration for deployment. Lastly, a prototype is designed to validate the solution, concluding the viability of the proposed architecture as a fast and secure certification method of manufacturing data.


2021 ◽  
Author(s):  
Mark Yampolskiy ◽  
Lynne Graves ◽  
Jacob Gatlin ◽  
Anthony Skjellum ◽  
Moti Yung

Author(s):  
Zeyang Li ◽  

This study explores the relationship between spatial agglomeration and innovation, taking Chinese manufacturing data as an example. Tractable model is built to explain the mechanism through which spatial concentration of firms in a city affects industrial innovation. Then in the empirical analysis, new agglomeration and innovation indicators are used to test the theoretical conclusions at the city-industry level. Results show that the geographical concentration of firms has significant negative effects on industrial innovation and growth. These overall effects can be divided into positive and negative categories after considering the interaction between the industrial labor scale and firm’s spatial agglomeration. Industries with a higher labor scale will bear more crowding effects of firms’ spatial agglomeration. These findings mean that moving to a less concentrated area might be a good choice for the labor-intensive firms which aim at innovation.


2021 ◽  
Author(s):  
Yajuan Sun ◽  
Jianlin Yu ◽  
Xiang Li ◽  
Ji Yan Wu ◽  
Wen Feng Lu

Author(s):  
Lise Kim ◽  
Esma Yahia ◽  
Frédéric Segonds ◽  
Philippe Véron ◽  
Victor Fau

2021 ◽  
Vol 1983 (1) ◽  
pp. 012104
Author(s):  
Xiyu Gao ◽  
Peng Liu ◽  
Qixun Zhang ◽  
Dawei Gao ◽  
Xin Huang

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