Abstract
Driving simulators are essential tools for training, education, research, and scientific experimentation. However, the diversity and quality of virtual environments in simulations is limited by the specialized human resources availability for authoring the content, leading to repetitive scenarios and low complexity of real-world scenes. This work introduces a pipeline that can process text-based narratives outlining driving experiments to procedurally generate dynamic traffic simulation scenarios. The solution uses Retrieval-Augmented Generation alongside local open-source Large Language Models to analyse unstructured textual information and produce a knowledge graph that encapsulates the world scene described in the experiment. Additionally, a context-based formal grammar is generated through inverse procedural modelling, reflecting the game mechanics related to the interactions among the world entities in the virtual environment supported by CARLA driving simulator. The proposed pipeline aims to simplify the generation of virtual environments for traffic simulation based on descriptions from scientific experiment, even for users without expertise in computer graphics. © 2025 by SCITEPRESS–Science and Technology Publications, Lda.

Abstract
Heterogeneous CPU-FPGA C/C++ applications may rely on High-level Synthesis (HLS) tools to generate hardware for critical code regions. As typical HLS tools have several restrictions in terms of supported language features, to increase the size and variety of offloaded regions, we propose several code transformations to improve synthesizability. Such code transformations include: struct and array flattening; moving dynamic memory allocations out of a region; transforming dynamic memory allocations into static; and asynchronously executing host functions, e.g., printf(). We evaluate the impact of these transformations on code region size using three realworld applications whose critical regions are limited by nonsynthesizable C/C++ language features.

Abstract
Efficient procurement processes are pivotal for strategic performance in digital organizations, requiring continuous refinement driven by automation, integration, and performance monitoring. This research investigates and demonstrates the potential for synergies between RPA and BPM in procurement processes. The primary objective is to analyze and evaluate a manual procurement-intensive process to enhance efficiency, reduce time-consuming interventions, and ultimately diminish costs and cycle time. Employing Design Science Research Methodology, this research yields a practical artifact designed to streamline procurement processes. An artifact was created using BPM methods and RPA tools. The RPA was developed after applying BPM Redesign Heuristics to the current process. A mixed-methods approach was employed for its evaluation, combining quantitative analysis on cycle time reduction with a qualitative Confirmatory Focus Group of department experts. The analysis revealed that the synergy between BPM and RPAs can leverage procurement processes, decreasing cycle times and workload on intensive manual tasks and allowing employees time to focus on other functions. This research contributes valuable insights for organizations seeking to harness automation technologies for enhanced procurement operations, with the findings suggesting promising enduring benefits for both efficiency and accuracy in the procurement lifecycle.

Abstract
Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. This has led smartphones' Operating Systems (OSs) to strongly tighten access to location information in the last few years. The same tightening has, however, not yet happened when it comes to our second most carried around device: the laptop. In this work, we demonstrate the privacy risks resulting from the fact that major laptop OSs still expose WiFi data to installed software, thus enabling to infer location information from WiFi Access Points (APs). Using data collected in a real-world experiment, we show that laptops are often carried along with smartphones and that a large fraction of our mobility profile can be inferred from WiFi APs accessed on laptops, thus concluding on the need to protect the access to WiFi data on laptops.

Abstract
This study focuses on improving the picking processes within a Picking-by-Line (PBL) warehouse through the development of a simulation model to assess different layouts and new operational rules. Utilizing a combination of Discrete Event Simulation (DES) and Agent-Based Modeling (ABS) in AnyLogic, the simulation model was validated against real-world Key Performance Indicators (KPIs) to ensure accuracy. The study identified three primary improvement opportunities. To address these opportunities, four scenarios were tested. The results showed varying impacts on productivity, with three of the four scenarios yielding improvements in picking productivity. Pilot testing confirmed the simulation model's predictions. The findings indicate that balancing travel distance reduction with congestion management is key to increasing picking productivity. This study reaffirms the value of simulation modeling in warehouse management, providing a robust framework for free-risk testing. © 2025 Elsevier B.V., All rights reserved.

Abstract
The number of electric vehicles has been increasing significantly due to various factors, such as the higher prices of fossil fuels, concerns about the increasing pollution, and the resulting incentive to use energy from renewable sources. There are currently a few charging facilities, which are still quite scattered, and several are still experimental, requiring appropriate planning of this infrastructure in order to support the growing number of electric vehicles adequately. Thus, optimising the location of charging stations becomes a critical issue, which can be achieved through the application of mathematical models and data analysis tools. An example is genetic algorithms, which have demonstrated their versatility in solving complex optimisation problems, especially those involving multiple variables. This work presents a proposal for a more comprehensive genetic algorithm model that encompasses all variables from the perspectives of all entities involved. Its experimentation was conducted using real data, with the aim of finding the best combination of locations, minimising the total number of stations and maximising the coverage of the area under study. Thus, it is essential to carefully consider user preferences, accessibility, energy demand, and existing electrical infrastructure to ensure an effective and sustainable installation. The findings highlight the crucial role of these computing tools in addressing complex problems from various viewpoints, leading to solutions that cater to the needs of all parties involved. While not necessarily perfect, these solutions represent a balanced compromise across multiple dimensions of the problem.