Abstract
NADH:ubiquinone oxidoreductase, respiratory complex I, plays a central role in cellular energy metabolism. As a major source of reactive oxygen species (ROS) it affects ageing and mitochondrial dysfunction. The novel inhibitor NADH-OH specifically blocks NADH oxidation and ROS production by complex I in nanomolar concentrations. Attempts to elucidate its structure by NMR spectroscopy have failed. Here, by using X-ray crystallographic analysis, we report the structure of NADH-OH bound in the active site of a soluble fragment of complex I at 2.0 angstrom resolution. We have identified key amino acid residues that are specific and essential for binding NADH-OH. Furthermore, the structure sheds light on the specificity of NADH-OH towards the unique Rossmann-fold of complex I and indicates a regulatory role in mitochondrial ROS generation. In addition, NADH-OH acts as a lead-structure for the synthesis of a novel class of ROS suppressors.

Abstract
This work addresses the role of optical sensing within the new emerging paradigm Industry 4.0. It starts with some thoughts about complex systems and their inherent need of enlarged sensorial tools. Then, the principles of optical sensing are presented with identification of the two principal types. After summarizing what is meant by Industry 4.0, it is detailed how optical sensing can contribute to the raise up of this new industrial concept, focusing on vision, physical sensing, chemical sensing, and sensor multiplexing. Emphasis is given in fiber optic sensing and, when feasible, in fiber Bragg grating sensing technology. Finally, some final remarks are delivered.

Abstract
Ocean related activities are often supported by offshore equipment with particular power demands. These are usually deployed at remote locations and have limited space, thus small energy harvesting technologies, such as photovoltaic panels or wind turbines, are used to power their instruments. However, the inherent energy sources are intermittent and have lower density and predictability than an alternative source: wave energy. Here, we propose and critically assess triboelectric nanogenerators (TENGs) as a promising technology for integration into wave buoys. Three TENGs based on rolling-spheres were developed and their performance compared in both a "dry" bench testing system under rotating motions, and in a large-scale wave basin under realistic sea-states installed within a scaled navigation buoy. Both experiments show that the electrical outputs of these TENGs increase with decreasing wave periods and increasing wave amplitudes. However, the wave basin tests clearly demonstrated a significant dependency of the electrical outputs on the pitch degree of freedom and the need to take into account the full dynamics of the buoy, and not only that of TENGs, when subjected to the excitations of waves. This work opens new horizons and strategies to apply TENGs in marine applications, considering realistic hydrodynamic behaviors of floating bodies.

Abstract
In the last few years we have been seeing a drastic change in the way software is developed. Large-scale software projects are being assembled by a flexible composition of many (small) components possibly written in different programming languages and deployed anywhere in the cloud - the so-called microservice-based applications. The dramatic growth in popularity of microservice-based applications has pushed several companies to apply major refactorings to their software systems. However, this is a challenging task that may take several months or even years. We propose a methodology to automatically evolve a Java monolithic application into a microservice-based one. Our methodology receives the Java code and a proposition of microservices and refactors the original classes to make each microservice independent. Our methodology creates an API for each method call to classes that are in other services. The database entities are also refactored to be included in the corresponding service. The initial evaluation shows that our tool can successfully refactor 80% of the applications tested.

Abstract
The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.

Abstract
The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.