Abstract
The fourth industrial revolution, commonly known as Industry 4.0, germinated in Germany as an industrial program of the government to reinvigorate the manufacturing sector. Nowadays, this change in the industrial paradigm has reached a global scale and is proposing to transform the traditional factories into more competitive, efficient and productive industries. In order to accomplish this goal it is necessary to establish methodologies to migrate from the traditional systems to innovative systems, namely those applying the Cyber-Physical Production Systems (CPPS) concepts. This paper proposes a methodology based on the Petri nets formalism for the modelling, analysis, validation and simulation of the migration process during the design phase and the control and monitoring of such processes during the implementation phase. © 2018 IEEE.

Abstract
The growing economic interest in the exploitation of mineral resources on deep-ocean beds, including those in the vicinity of sensitive-rich habitats such as hydrothermal vents, raise amounting concern about the damage that such actions might originate to these poorly-know ecosystems, which represent millions of years of evolution and adaptations to extreme environmental conditions. It has been suggested that mining may cause a major impact on vent ecosystems and other deep-sea areas. Yet, the scale and the nature of such impacts are unknown at present. Hence, building upon currently available scientific information it is crucial to develop new cost-effective technologies embedded into rigorous operating frameworks. The forward-thinking provided here will assist in the development of new technologies and tools to address the major challenges associated with deep sea-mining; technologies for in situ and ex situ observation and data acquisition, biogeochemical processes, hazard assessment of deep-sea mining to marine organisms and development of modeling tools in support of risk assessment scenarios. These technological developments are vital to validate a responsible and sustainable exploitation of the deep-sea mineral resources, based on the precautionary principle.

Abstract
A reflective fiber optic sensor based on multimode interference for the measurement of glucose aqueous solutions is proposed. A maximum experimental resolution of 0.04 wt.% of glucose is achieved. © OSA 2018 © 2018 The Author(s)

Abstract
Alloy is a lightweight formal specification language, supported by an IDE, which has proven well-suited for reasoning about software design in early development stages. The IDE provides a visualizer that produces graphical representations of analysis results, which is essential for the proper validation of the model. Alloy is a rich language but inherently static, so behavior needs to be explicitly encoded and reasoned about. Even though this is a common scenario, the visualizer presents limitations when dealing with such models. The main contribution of this paper is a principled approach to generate instance visualizations, which improves the current Alloy Visualizer, focusing on the representation of behavior.

Abstract
This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3channel RGB image. The challenge was divided into 2 tracks: the "Clean" track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the "Real World" track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+ 10 additional images for validation and testing, respectively. The "Clean" and "Real World" tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.

Abstract
The introduction of Industry 4.0 to modernize the existing industrial manufacturing systems has created a wave of disruption all over the world, that changed not only the industry paradigm, but also the research directions in the intelligent manufacturing systems field. After five years since its presentation, similar initiatives have fostered in several countries worldwide to promote the adoption of Industry 4.0 principles. This paper aims to analyze and discuss the current state of adoption of this initiative and to verify the way it changed the research directions, particularly at the level of applying artificial intelligence and ICT technologies in the cyber-physical systems context. For this purpose, an analytical study of the scientific publications related to Industry 4.0 domain has been performed, considering a dataset of scientific publications retrieved from the IEEE Xplore database. This dataset considers two distinct time periods, separated by the introduction of Industry 4.0 as the digitalization threshold: before digital era (B.D.) and after digital era (A.D.). © 2018 IEEE.