Abstract
Zero Defects is one of the ultimate targets for manufacturing quality control and assurance. Such systems are becoming common in advanced manufacturing industries but are at an initial stage in more traditional industrial sectors, such as wood panels, laminates production, pulp and paper processing and composite panels production. This paper proposes the PREFAB framework, applied to the wood based panels industry, to minimize rejected products using AI, machine learning and IoT devices. The framework was built through action research with a Portuguese wood-based panel manufacturing. This framework delivered an innovative decision support system that provides relevant and timely recommendations for shopfloor decision making and to support process/product engineering. Copyright (C) 2021 The Authors.

Abstract
Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an Fl-score of 87.89% in the SWaT dataset.

Abstract
Recently, Mobility as a Service (MaaS) concept and its main theoretical approaches have been under discussion, to positively influence the future of mobility. Namely, by contextualizing MaaS’s role in modern societies explaining its main functions, characteristics, and attributes, as well as identifying all the stakeholders involved in this comprehensive challenge towards ensuring its widespread implementation. The environmental, societal, technological and cultural changes needed to ensure a sustainable mobility ecosystem are an utmost challenge that requires an intense effort and involvement of all different types of stakeholders within their perspectives, roles, responsibilities and contributions to the mobility system overall behavior and performance. Notwithstanding, the global tendency of digital transformation, also referred as digitization, in society and businesses are upbringing a new technological evolution that will lead to a new mobility paradigm bringing together MaaS and the internet of Mobility (IoM), thus creating what we call the Internet of Mobility as a Service (IoMaaS). The future trends of mobility will have to be ‘human-centric’, to properly balance the amount of technology requested into the ecosystem to ensure the whole system’s universality, to be inclusive, as well as developing the appropriate amount of technology, accordingly to the different users’ technological skills. Furthermore, different types of incentives and penalties need to be included in supporting a broad cultural shift regarding citizen’s mobility routines habits. This will be of great importance to ensure the sustainability of this new mobility paradigm as well as of the ability to attain all its benefits. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The discovery of new biological interactions, such as interactions between drugs and cell lines, can improve the way drugs are developed. Recently, there has been important interest for predicting interactions between drugs and targets using recommender systems; and more specifically, using recommender systems to predict drug activity on cellular lines. In this work, we present a simple and straightforward approach for the discovery of interactions between drugs and cellular lines using collaborative filtering. We represent cellular lines by their drug affinity profile, and correspondingly, represent drugs by their cell line affinity profile in a single interaction matrix. Using simple matrix factorization, we predicted previously unknown values, minimizing the regularized squared error. We build a comprehensive dataset with information from the ChEMBL database. Our dataset comprises 300,000+ molecules, 1,200+ cellular lines, and 3,000,000+ reported activities. We have been able to successfully predict drug activity, and evaluate the performance of our model via utility, achieving an Area Under ROC Curve (AUROC) of near 0.9.

Abstract

Abstract