Abstract
During the process of software architectural design, numerous questions arise which must be answered. These questions may be about requirements on the proposed system (the problem space) or about how the system should be designed and developed (the solution space). As questions arise they may be answered immediately, deferred until later, or provisionally answered with an assumption about the answer. The objective of this work was to explore the nature of questions that arise during architecture. We explored the types of questions, how they are organized, how they are tracked, and how and when they are answered. We started by surveying highly experienced architects about their practices with respect to architectural questions. We also performed a controlled experiment with master students about organizing architectural questions that clarified and substantiated the survey data. We learned that architectural questions include slightly more questions about the problem space than the solution space, as well as a minority of questions related to the managing of the project. We found that architects often use ad hoc methods to organize and track them, although they typically organize them along more than one dimension. We learned also that, about a third of the time, architects make assumptions about the answers to architectural questions in order to make progress on the architecture. This suggests that some projects may have risks of incorrect design or later costly rework due to inadequate tracking or incorrectly answered architectural questions.

Abstract
RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the three-dimensional (3D) position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the three-dimensional (3D) objects’ position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE), powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

Abstract
There is evidence that some outdoor events may have contributed to the spread of COVID-19. We updated an empirical methodology based on regression modeling and hypothesis testing to analyze the potential impact of a demonstration that took place in Lisbon, within the scope of the ’Black Lives Matter’ context, on the contagion pattern in the region where this event occurred. We find that in the post-impact period there was no acceleration in the number of cases in the region, unlike in a prior event in the region. The proportion of counties where there was a potential impact of the event is not statistically significant. This result demonstrates that not all outdoor events contributed to the spread of COVID-19 and exemplifies how to apply the selected empirical methodology.

Abstract
This paper introduces a novel approach to autonomous vehicle control using an end-to-end learning framework. While existing solutions in the field often rely on computationally expensive architectures, our proposed lightweight model achieves comparable efficiency. We leveraged the Car Learning to Act (CARLA) simulator to generate training data by recording sensor inputs and corresponding control actions during simulated driving. The Mean Squared Error (MSE) loss function served as a performance metric during model training. Our end-to-end learning architecture demonstrates promising results in predicting steering angle and throttle, offering a practical and accessible solution for autonomous driving. Results of the experiment showed that our proposed network is approximate to 5.4 times lighter than Nvidia's PilotNet and had a slightly lower testing loss. We showed that our network is offering a balance between performance and computational efficiency. By eliminating the need for handcrafted feature engineering, our approach simplifies the control process and reduces computational demands. Experimental evaluation on a testing map showcases the model's effectiveness in real-world scenarios whilst being competitive with other existing models.

Abstract
The footwear industry is known for its longstanding traditional production methods that require intense manual labor. Roughing, for example, is regarded as one of the significant and critical operations in shoe manufacturing and consists of using abrasive tools to remove a thin layer of the shoe's surface, creating a slightly roughened texture that provides a better surface area for adhesion. As such, workers are typically subjected to hazardous substances (i.e., dust, chromium), repetitive strain injuries, and ergonomic challenges. Although robots can automate repetitive tasks and perform with high precision and consistency, the footwear industry is usually reluctant to employ industrial robots due to the need for restructuring. This paper addresses the challenge of re-designing the lateral roughing of uppers to allow robot-assisted manufacturing with minimal modifications in the manufacturing process. The proposed innovative system employs a robotic manipulator to perform roughing based on data collected from preceding manufacturing steps. Workers marking the mesh line of each sole-upper pair can simultaneously teach the manipulator path for that same pair, using a programming-by-demonstration approach. Multiple paths were collected by outlining a piece of footwear, converted into robot instructions, and deployed on a simulated and real industrial manipulator. The key findings of this research showcase the capability of the proposed solution to replicate collected paths accurately, indicating potential applications not only in roughing processes but also in similar tasks like primer and adhesive application.

Abstract
This paper proposes an original framework for a flexibility-centric value chain and describes the pre-specification of the Grid Data and Business Network (GDBN), a digital platform to provide support to the flexibility value chain activities. First, it outlines the structure of the value chain with the most important tasks and actors in each activity. Next, it describes the GDBN concept, including stakeholders' engagement and conceptual architecture. It presents the main GDBN services to support the flexibility value chain, including, matching consumers and assets and service providers, assets installation and operationalization to provide flexibility, services for energy communities and services, for consumers, aggregators, and distribution systems operators, to participate in flexibility markets. At last, it details the workflow and life cycle management of this platform and discusses candidate business models that could support its implementation in real-life scenarios.