Abstract
The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

Abstract
In this paper, the modeling of a system based on a DC Motor with Worm Gearbox is presented. Worm gearboxes are typically applied when its compactness is an important factor, as well as an orthogonal redirectioning is required. One of the greatest advantages of worm gears is its unique self-locking characteristic. This means that the gear can only rotate by its input side, and cannot be actuated through the load side. Using a DC motor with a worm gearbox is a solution that guarantees that, for instance, in a robotic manipulator, when the arm’s joint reaches a desired angle, it does not move until a next required setpoint. Modeling accurately this system is crucial in order to develop its control in a more efficient way. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Wireless, sensor and actuator technologies are often central to sensing or communication critical systems [...]

Abstract
Labor flexibility is widely recognized as a critical component of manufacturing flexibility to achieve a competitive advantage in the marketplace. This becomes more important in the context of Industry 4.0, where machines, products, components, Information, and communications technology (ICT) systems and workers collaborate to create an intelligent network. Several studies address the benefits of industry 4.0 technologies for workers. However, there is a concern about the future of work organization with the increased use of digital technologies and the extent to which these technologies allow flexibility or make workers' activities more rigid. Thus, this study aims to identify the advantages and barriers perceived by the operator in the implementation of these Industry 4.0 technologies. To achieve this objective, a literature review was performed. The results obtained show that Augmented Reality (AR) and Virtual Reality (VR) technologies increase flexibility since it provides the necessary information to carry out the work task and train operators, thus reducing the occurrence of errors and task duration. However, operator flexibility can also be affected, due to ergonomic aspects, the design of the system interface, as well as how these technologies are implemented. Collaborative Robots (CR) are an important instrument for flexibility, because the robot replaces the operator in the most demanding activities, leaving him available to perform other types of tasks and thus increasing his versatility. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
The high incidence of cervical cancer in women has prompted the research of automatic screening methods. This work focuses on two of the steps present in such systems, more precisely, the identification of cervical lesions and their respective classification. The development of automatic methods for these tasks is associated with some shortcomings, such as acquiring sufficient and representative clinical data. These limitations are addressed through a hybrid pipeline based on a deep learning model (RetinaNet) for the detection of abnormal regions, combined with random forest and SVM classifiers for their categorization, and complemented by the use of domain knowledge in its design. Additionally, the nuclei in each detected region are segmented, providing a set of nuclei-specific features whose impact on the classification result is also studied. Each module is individually assessed in addition to the complete system, with the latter achieving a precision, recall and F1 score of 0.04, 0.20 and 0.07, respectively. Despite the low precision, the system demonstrates potential as an analysis support tool with the capability of increasing the overall sensitivity of the human examination process.

Abstract
At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.