Abstract

Abstract
This article presents a literature review and the discussion about the key concepts associated with data markets. Data markets have multiple centralized and decentralized approaches. The main problem is the trust and reliability of supplies, inflows, and suppliers. The proposed study object is the decentralized marketplace data supported by Blockchain technology to ensure confidence in the supply chain of data, in the actors involved in the market and the data sources. The application scenarios are proposed in a model with four levels, data provision, data delivery, rights management, and producer internal sources. That will be done with Blockchain technology, through contracting using smart contracts, the controlled delivery of data by the data producers, the management of flows of data, and access control to data. © 2019, Springer Nature Switzerland AG.

Abstract
The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions. Copyright

Abstract
Smart governance is one of the characteristics of smart cities, having its roots in e-government, in the principles of good governance, and in the assumptions of citizens' participation and involvement in public decision-making. This chapter aims to answer the question: “What smart governance practices are being implemented in smart cities” through an extensive literature review in the areas of e-government, good governance, smart cities and smart governance, and content analysis of the websites of seven smart cities: Amsterdam, Barcelona, Copenhagen, Lisbon, Manchester, Singapore, and Stockholm. The objective was to identify the presence of factors related with e-participation; e-services; and public administration functioning on the cities' websites. The chapter ends with directions for future research and the conclusion that all the smart cities analyzed presented some factors related with smart governance, but with different levels of development and application.

Abstract
Smart Governence as its roots in e-government, in the principles of good governance, and in the assumptions of citizens' participation and involvement in public decision-making and is considered one of the six main characteristics of smart cities. The present investigation was intended to answer the question: "What smart governance practices are being implemented in European smart cities" through an extensive literature review and content analysis of the websites of six European smart cities: Amsterdam; Barcelona; Copenhagen; Lisbon; Manchester and Stockholm. The objective was to identify the presence of factors related with e-participation; e-services; and the functioning of local public administration on the city's websites. It was concluded that all the smart cities analyzed presented some factors related with smart governance, but with different levels of development and application.

Abstract
We present PRIME (PSF Reconstruction and Identification for Multi-sources characterization Enhancement) as a novel hybrid concept to improve the PSF estimation based on Adaptive optics (AO) control loop data. PRIME uses both focal and pupil plane data to jointly estimate the model parameters, which are both the atmospheric (Cn2 (h), seeing), system (e.g. optical gains, residual low-order errors). The parametric model in use is flexible enough to be scaled with field location and wavelength, making it a proper choice for optimized on-axis and off-axis data-reduction across the spectrum. We review the methodology and on-sky validations on NIRC2 at Keck II. We also present applications of PSF model parameters retrieval using PRIME: (i) calibrate the PSF model for observations void of stars on the acquired images, i.e. optimize the PSF reconstruction process (ii) update the AO error breakdown mutually constrained by the telemetry and the images in order to speculate on the origin of the missing error terms and evaluate their magnitude (iii) measure photometry and astrometry in stellar fields.