Abstract
Thermoelectric Generators (TEGs) are devices that have the ability to directly convert heat into electrical power, or vice-versa, and are being envisaged as one off-the-grid power source. Furthermore, carbon-based materials have been used as a conducting filler to improve several properties in thermoelectric materials. The present work studied the influence on the thermoelectric performance of Bi2Te3 bulk materials by incorporating different concentrations of Multi-Walled Carbon Nanotubes (MWCNT). In order to control and understand the influence of MWCNT dispersion in the nanocomposite, two different production methods (manual grinding and ultrasonication) were carried out and compared. It was verified that a larger dispersion leads to a better outcome for thermoelectric performance. The achieved Seebeck coefficient was up to-162 µV K-1 with a Power Factor of 0.50 µW K-2 m-1, for the nanocomposite produced with 11.8 %V of MWCNT. This result demonstrates the ability to increase the thermoelectric performance of Bi2Te3 throughout the addition of MWCNT. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.

Abstract
Thin films of titanium dioxide (TiO2) and titanium (Ti) were deposited onto glass and optical fiber supports through DC magnetron sputtering, and their transmission was characterized with regard to their use in optical fiber-based sensors. Deposition parameters such as oxygen partial pressure, working pressure, and sputtering power were optimized to attain films with a high reflectance. The films deposited on glass supports were characterized by UV-Vis spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Regarding the deposition parameters, all three parameters were tested simultaneously, changing the working pressure, the sputtering power, and the oxygen percentage. It was possible to conclude that a lower working pressure and higher applied power lead to films with a higher reflectance. Through the analysis of the as-sputtered thin films using X-ray diffraction, the deposition of both Ti and TiO2 films was confirmed. To study the applicability of TiO2 and Ti in fiber sensing, several thin films were deposited in single mode fibers (SMFs) using the sputtering conditions that revealed the most promising results in the glass supports. The sputtered TiO2 and Ti thin films were used as mirrors to increase the visibility of a low-finesse Fabry-Perot cavity and the possible sensing applications were studied.

Abstract
TCP is typically the default transport protocol of choice for its supposed reliability, even for message-oriented middleware (e.g., ZeroMQ) or inter-actor communication (e.g., distributed Erlang). However, under network issues, TCP connections can fail, which requires ensuring both at-least-once and at-most-once delivery at the upper middleware layer. Moreover, the use of TCP at scale, in highly concurrent systems, can lead to drastic performance loss due to the need for TCP connection multiplexing and the resulting head-of-line blocking. This paper introduces Exon, an oblivious exactly-once messaging protocol, and a corresponding lightweight library implementation. Exon uses a novel strategy of a per-message four-way protocol to ensure oblivious exactly-once messaging, with on-demand protocol-level soft half-connections that are established when needed and safely discarded. This achieves correctness, obliviousness, and performance, through merging and pipelining basic protocol messages. The empirical evaluation of Exon demonstrates significant improvements in throughput and latency under packet loss, while maintaining a negligible overhead over TCP in healthy networks.

Abstract
Technological evolution has allowed that tasks, usually performed by humans, can now be performed accurately by automated systems, often with superior performance. The healthcare area has been paradigmatic in the automation of processes, as the need to optimize costs, ensuring the provision of quality care, is crucial for the success of organizations. Diabetes, whose prevalence has increased significantly in the last decade, could be a case of application of several technologies that facilitate diagnosis, tracking and monitoring. Such tasks demand a great effort from health systems, requiring the allocation of material, human and financial resources, under penalty of worsening symptoms and emergence of serious complications. In this chapter the authors will present and explore how different technologies can be integrated to provide better healthcare, ensuring quality and safety standards, with reference to the case of diabetes.

Abstract
This paper presents a model predictive control (MPC) framework for battery energy storage systems (BESS) management considering models for battery degradation, system efficiency and V-I characteristics. The optimization framework has been tested for microgrids with different renewable generation and load mix considering several operation strategies. A comparison for one-year simulations between the proposed model and a naïve BESS model, show an increase in computation times that still allows the application of the framework for real-time control. Furthermore, a trade-off between financial revenue and reduced BESS degradation was evaluated for the yearly simulation, considering the degradation model proposed. Results show that a conservative BESS usage strategy can have a high impact on the asset's lifetime and on the expected system revenues, depending on factors such as the objective function and the degradation threshold considered.