Abstract

Abstract
This research project consists of bringing innovation to the shop floor in such a way that it will allow its approach to the Industry 4.0 concept. The main aim includes integrating the present installed systems in order to provide its user with data as if it was a unique system. More concretely, this study intends to unify the information that comes from different systems: Manufacturing Execution System (MES); Enterprise Resource Planning (ERP); Supervisory Control and Data Acquisition (SCADA); Product Lifecycle Management (PLM); Computerized Maintenance Management Systems (CMMS); Quality Management System (QMS). Integrating this data will enable the creation of automatic procedures which can eliminate the existing gaps within the communication among the different systems. Furthermore, this will allow a real-time view of the whole plant so that immediate decisions can be made in case of any occurrence. In order to provide data fusion from the distinct systems previously mentioned, machine learning (ML) methodology will be applied. This document presents the research done and the reviewed literature, as well as the technologies and methodologies needed in this project. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Compared to rodents, sheep offer several attractive features as an experimental model for testing different medical and surgical interventions related to pathological gait caused by neurological diseases and injuries. To use sheep for development of novel treatment strategies in the field of neuroscience, it is key to establish the relevant kinematic features of locomotion in this species. To use sheep for development of novel treatment strategies in the field of neuroscience, it is crucial to understand fundamental baseline characteristics of locomotion in this species. Despite their relevance for medical research, little is known about the locomotion in the ovine model, and next to nothing about the three-dimensional (3D) kinematics of the hindlimb. This study is the first to perform and compare two-dimensional (2D) and 3D hindlimb kinematics of the sagittal motion during treadmill walking in the ovine model. Our results show that the most significant differences took place throughout the swing phase of the gait cycle were for the distal joints, ankle and metatarsophalangeal joint, whereas the hip and knee joints were much less affected. The results provide evidence of the inadequacy of a 2D approach to the computation of joint kinematics in clinically normal sheep during treadmill walking when the interest is centered on the hoof's joints. The findings from the present investigation are likely to be useful for an accurate, quantitative and objective assessment of functionally altered gait and its underlying neuronal mechanisms and biomechanical consequences.

Abstract
Customer relationship management is critical for organizations. Public institutions, in particular municipalities, are no exception to this. Since the process of implementing a CRM system is not risk-free, it is important to know the factors that influence its success. From studies conducted, it was possible to verify that there is a gap in the literature regarding the influential factors of the successful adoption of CRM systems in public institutions (CzRM). Also, through interviews conducted in some municipalities and CRM suppliers, it was possible to identify the relevant factors for the adoption of CRM systems. The purpose of this article is to present the influence factors of the success of the implementation of CzRM systems. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Nas últimas décadas a utilização da inteligência artificial tem sido frequente no desenvolvimento de aplicações computacionais. Mais recentemente a aprendizagem automática, especialmente pelo uso da aprendizagem profunda (deep learning), tem impulsionado o crescimento e ampliado o desenvolvimento de sistemas inteligentes para diferentes domínios. No cenário atual de crescimento tecnológico estão a surgir com maior frequência os sistemas de recomendação (recommender systems) com diferentes técnicas para a filtragem de informações em grandes bases de dados. Um desafio é prover a recomendação adaptativa para mitigar a sobrecarga de informações em ambientes on-line. Este artigo revisa trabalhos anteriores e aborda alguns dos aspectos teórico-conceptuais e teórico-práticos que constituem os sistemas de recomendação, caracterizando o emprego de redes neuronais profundas (Deep Neural Network – DNN) para prover a recomendação sequencial apoiada pela recomendação baseada em sessão.;In recent decades, artificial intelligence use has been frequent in the computational applications development. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the intelligent systems development for different domains. In the current scenario of technological growth, the recommender systems appear with increasing frequency through their different techniques for information filtering in large datasets. It is a challenge to provide adaptive recommendation to mitigate information overload in online environments. This article reviews previous works and addresses some of the theoretical-conceptual and theoretical-practical aspects that constitute the recommender systems, characterizing the use of deep neural network (DNN) to provide sequential recommendation supported by session-based recommendation.

Abstract
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human-drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.