Abstract
The energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG), CO2 and other air pollutants emissions. The home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances. Therefore, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper a comparison is made between a Model Predictive Control (MPC) model, the ON/OFF and proportional-integral-derivative (PID) control models of an air conditioning unit AC system controlling the temperature of a room. The model of the house with local Photovoltaic (PV) solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and electricity tariff of a summer day.

Abstract
A crucial factor for the sustainable development of human society is access to electricity. This fact has motivated the development of renewable energy systems isolated or connected to the electric distribution network. Evaluation of autonomous hybrid energy systems from a technical and economic perspective is a difficult problem that requires using complex mathematical models of renewable sources and generators, such as photovoltaic (PV) panels and wind turbines, and the implementation of optimization techniques in order to obtain an economically successful design. This chapter describes and analyzes traditional isolated energy systems powered by solar PV and wind energies provided with a battery energy storage system. Simulation and optimization are illustrated through the analysis of a rural electrification project in Tangiers (Morocco) in order to provide electricity to rural clinic. Optimization analysis suggests the installation of a PV/BESS system due to the magnitude of the load to be supplied, operating costs, and environmental conditions.

Abstract
A Fabry-Perot based sensor with two coupled hollow microspheres is presented. The sensor was fabricated using fusion splicing techniques, enabling a low-cost, highly reproducible, production. The coupling of the two microspheres gives rise to a highly sensitive strain sensor, reaching a sensitivity of 4.07 pm/mu epsilon. The allsilica composition leads to a low thermal sensitivity, making the proposed structure suitable applications in environments with varying external conditions.

Abstract
Although MNEs are important players in the present global world, there has been a debate regarding, on one hand, how MNEs contribute to the development of indigenous firms in host countries, and on the other hand, how indigenous suppliers are able to cope with their international technology demanding clients. This chapter analyzes the patterns of technology acquisition of 40 firms that supply eight multinational firms that belong to four different industries. It is possible to conclude that there are certain differences among foreign and indigenous suppliers as well across the industries they belong to. These differences are the result of a cumulative process over time, which reflect the different performances of the companies and their relationships with the environment.

Abstract
There are many open issues and challenges in the multi-agent reward-based learning field. Theoretical convergence guarantees are lost, and the complexity of the action-space is also exponential to the amount of agents calculating their optimal joint-action. Function approximators, such as deep neural networks, have successfully been used in singleagent environments with high dimensional state-spaces. We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi-agent Q-learning are used to formally describe our proposal, and are tested in a Foraging Task and a Pursuit Game. We also demonstrate how they can generalize to similar tasks and to larger teams, due to the strength of deep-learning techniques, and their viability for transfer learning approaches. With only a small fraction of the initial task's training, we adapt to longer tasks, and we accelerate the task completion by increasing the team size, thus empirically demonstrating a solution to the complexity issues of the multi-agent field.

Abstract