Abstract
The Acute Kidney Injury (AKI), is a disease that affects the kidneys and is characterized by the rapid deterioration of these organs, usually associated with a pre-existing critical illness. Being an acute disease, time is a key element in the prevention. By anticipating a patient's state transition, we are preventing future complications in his health, such as the development of a chronic disease or loss of an organ, in addition to decreasing the amount of money spent on the patient's care. The main goal of this paper is to address the problem of correctly predicting the illness path in various patients by studying different methodologies to predict this disease and propose new distinct approaches based on this idea of improving the performance of the classification. Through the comparison of five different approaches (Markov Chain Model ICU Specialists, Markov Chain Model Features, Markov Chain Model Conditional Features, Markov Chain Model and Random Forest), we came to the conclusion that the application of conditional probabilities to this problem produces a more accurate prediction, based on common inputs.

Abstract
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in hazefree and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had similar to 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

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 latest generation of high-contrast instruments dedicated to exoplanets and circumstellar disk imaging are equipped with extreme adaptive optics and coronagraphs to reach contrasts of up to 10 -4 at a few tenths of arcseconds in the near-infrared. The resulting image shows faint features, only revealed with this combination, such as the wind driven halo. The wind driven halo is due to the lag between the adaptive optics correction and the turbulence speed over the telescope pupil. However, we observe an asymmetry of this wind driven halo that was not expected when the instrument was designed. In this letter, we describe and demonstrate the physical origin of this asymmetry and support our explanation by simulating the asymmetry with an end-To-end approach. From this work, we find that the observed asymmetry is explained by the interference between the AO-lag error and scintillation effects, mainly originating from the fast jet stream layer located at about 12 km in altitude. Now identified and interpreted, this effect can be taken into account for further design of high-contrast imaging simulators, next generation or upgrade of high-contrast instruments, predictive control algorithms for adaptive optics, or image post-processing techniques.

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
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.