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.

Abstract
This paper proposes a method to avoid using a CORDIC or external memory to process the steering vectors to calculate the pseudospectrum of correlation-based beamforming algorithms. We show that if we decompose the steering vector equation, the size of the matrix to be saved in memory becomes independent of the antenna array size. Besides, the amount of data needed is small enough to be saved in the internal block RAMs of the FPGA SoC. Besides, this method greatly reduces the number of memory accesses, by offloading some processing to hardware, while keeping the frequency at 300MHz with a precision of 0.25 degrees. Finally, we show that this approach is scalable since the complexity grows logarithmically for bigger arrays, and the symmetry in the matrices obtained allows even more compact data.

Abstract
Today, logistics activities are driven by the pressing need to simultaneously increase efficiency, reduce costs, and promote sustainability. In our research, we tackle this challenge by adapting a general vehicle routing problem with deliveries and pickups to accommodate different types of customers. Customers requiring both delivery and pickup services are mandatory, while those needing only a pickup service (backhaul customers) are optional and are only visited if profitable. A mixed-integer linear programming model is formulated to minimize fuel consumption. This model can address various scenarios, such as allowing mandatory customers to be served with combined or separate delivery or pickup visits, and visiting optional customers either during or only after mandatory customer visits. An adaptive large neighborhood search is developed to solve instances adapted from the literature as well as to solve a real-case study of a beverage distributor. The results show the effectiveness of our approach, demonstrating the potential to utilize the available capacity on vehicles returning to the depot to create profitable and environmentally friendly routes, and so enhancing efficient, cost-effective, and sustainable logistics activities. © 2024 International Federation of Operational Research Societies.

Abstract

Abstract
Robotics competitions have been increasing in the last years since they bring several impacts on students education, such as technical skill development, teamwork, resilience and decision making withing the STEM skills. The article highlights the significance of robotics competitions as platforms for fostering innovation and driving advancements in the field of robotics. This article primarily focuses on the development of a robot in the Dragster category for the 2023 Portuguese Robotics Open. It outlines the strategies devised to tackle the competition's challenges and discusses the obstacles encountered along with the corresponding solutions employed. The article delves into the specific details of the challenges faced and the iterative processes undertaken to enhance the robot's performance and functionalities. By sharing the insights gained from the project, future proposals for iterations of the robot will be presented, aiming to further augment its features and overall performance while sharing knowledge with other teams and community.