Abstract
Quadruped robots are gaining attention in the research community because of their superior mobility and versatility in a wide range of applications. However, they are restricted to procedures that do not need precise object interaction. With the addition of a robotic arm, they can overcome these drawbacks and be used in a new set of tasks. Combining a legged robot's dextrous movement with a robotic arm's maneuverability allows the emergence of a highly flexible system, but with the disadvantage of higher complexity of motion planning and control methods. This paper gives an overview of the existing quadruped systems capable of manipulation, with a particular interest in systems with high movement flexibility. The main topics discussed are the motion planning approaches and the selected kinematic configuration. This review concludes that the most followed research path is to add a robotic arm on the quadrupedal base and that the motion planning approach used depends on the desired application. For simple tasks, the arm can be seen as an independent system, which is simpler to implement. For more complex jobs the coupling effects between the arm and quadruped robot must be considered.

Abstract
Deep learning approaches have become popular in sentiment analysis because of their competitive performance. The downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. Previous approaches that used sentiment lexicons for sentiment analysis can do that, but their performance is lower than deep learning approaches. Therefore, it is natural to wonder if the two approaches can be combined to exploit their advantages. In this chapter, we present a neuro-symbolic approach that combines both symbolic and deep learning approaches for sentiment analysis tasks. The symbolic approach exploits sentiment lexicon and shifter patterns-which cover the operations of inversion/reversal, intensification, and attenuation/downtoning. The deep learning approach used a pre-trained language model (PLM) to construct sentiment lexicon. Our experimental result shows that the proposed approach leads to promising results, substantially better than the results of a pure lexicon-based approach. Although the results did not reach the level of the deep learning approach, a great advantage is that sentiment prediction can be accompanied by understandable explanations. For some users, it is very important to see how sentiment is derived, even if performance is a little lower.

Abstract
Metrics are key elements that can give us valuable information about the effectiveness of agile software development processes, particularly considering the Scrum environment. This study aims to learn about the metrics adopted to assess agile development processes and explore the impact of how the role performed by each member in Scrum contributed to increasing/reducing the perception of the importance of these metrics. The impact of years of experience in Scrum on this perception was also explored. To this end, a quantitative study was conducted with 191 Scrum professionals in companies based in Portugal. The results show that the Scrum role is not a determining factor, while individuals with more years of experience have a higher perception of the importance of metrics related to team performance. The same conclusion is observed for the business value metric of the product backlog and the percentage of test automation in the testing phase. The findings allow for extending the knowledge about Scrum project management processes and their teams, in addition to offering important insights into the implementation of metrics for software engineering companies that adopt Scrum.

Abstract
Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters' privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results' privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems' security and transparency shortcomings while also delivering a high level of integrity and traceability.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced.

Abstract
The use of graphene oxide (GO) as a saturable absorber for short pulses generation in an Erbium-doped fiber laser was studied and demonstrated. The saturable absorber consisted of a thin GO film, with a high concentration of monolayer GO flakes, spray-coated on the end face of a ferrule-connected fiber. By including the saturable absorber in the laser cavity and controlling the intra-cavity polarization, the generation of shortpulsed light was achieved under mode-locking and Q-switching operations. Under mode-locking operation, it was observed a pulse train with a fundamental repetition rate of 1.48 MHz, with a working wavelength centered at 1564.4 nm. In the Q-switch operation, a pulse train with a 12.7 kHz repetition rate and a 14.3 µs pulse duration was attained for a 230-mA pump current. Further investigation showed a linear dependence of the repetition rate with the pump power, attaining frequencies between 12.7 and 14.4 kHz.