Abstract
To study the magnetostriction of Co66Fe34 thin films, amorphous silicon microcantilevers were prepared by surface micromachining, and the 136 nm-thick magnetostrictive film was deposited by electron beam physical vapor deposition and patterned on top of the microcantilever structure. The magnetostriction of the Co66Fe34 films was confirmed by measuring the deflection of the cantilevers under a varying magnetic field, reaching displacements up to 8 nm. The configuration was simulated using COMSOL software, yielding a similar deflection behavior as a function of the magnetic field, with a film with a magneto strictive coefficient of ? S ~ 55 p.p.m. The experimental configuration uses a laser and a position sensitive detector to measure the displacement, based on an optical lever configuration, and a piezoelectric stage to calibrate the system.

Abstract
Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ° C.

Abstract

Abstract
Cloud computing is omnipresent and plays an important role in today’s world of Internet of Things (IoT). Several IoT devices and their applications already run and communicate through the cloud, easing the configuration burden for their users. With the expected exponential growth on the number of connected IoT devices this centralized approach raises latency, privacy and scalability concerns. This paper proposes the use of fog computing to overcome those concerns. It presents an architecture intended to distribute the communication, computation and storage loads to small gateways, close to the edge of the network, in charge of a group of IoT devices. This approach saves battery on end devices, enables local sensor fusion and fast response to urgent situations while improving user privacy. This architecture was implemented and tested on a project to monitor the level of used cooking oil, stored in barrels, in some restaurants where low cost, battery powered end devices are periodically reporting sensor data. Results show a 93% improvement in end device battery life (by reducing their communication time) and a 75% saving on cloud storage (by processing raw data on the fog device). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
The circular economy contrasts with the traditional linear economy since it presents a sustainable way both to produce goods and services and to contribute to the development of economies. This paper aims to contribute to a better knowledge of the efficiency of resources productivity, a common indicator to compare how circular economies are, through the estimation of the main determinants for the circular economy in Europe. A systematic analysis and comparison of the performance of all the European Union countries was performed to get further insight into their root causes and to help designing future policies towards a more circular European Union economy. With this purpose, a set of determinant factors for a circular economy in Europe were analysed, under the period between 2000 and 2016. A cluster analysis was applied and complemented with three econometric estimation methods: panel unit root tests, panel cointegration tests and vector autoregression model. The main findings allowed to cluster European countries into three different groups according to the growth rate of their resources productivity and to explain them according to the selected exploratory factors. Special efforts were made to explain the highest productivity growth group, as a way to find relevant drivers towards sustainable productivity growths.

Abstract
This paper presents an experiment on newsreaders' behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user's interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers' perceived satisfaction.