Abstract

Abstract
This paper presents a case study to illustrate the application of the directed qualitative content analysis (DQCA) technique to focus group transcriptions for data-driven qualitative persona creation, with broader applicability in human-computer interaction and software development. Using a case study from a project focused on creating an e-grocery marketplace for facilitating short agrifood supply chain trade in the Portuguese context, we demonstrate and validate how DQCA can systematically generate personas that reflect real user needs. For the focus group session, we involved one of the project's stakeholders: family farmers. Furthermore, we propose how these personas can be integrated into the Rational Unified Process software development methodology, guiding decision-making, user-centered design, and prioritization throughout all its phases. Despite being rooted in the e-grocery domain, this paper's methodological approach and insights into generating and integrating user-centered personas in software development processes apply to a broader range of industries and projects, offering guidelines for practitioners and researchers in diverse contexts.

Abstract

Abstract
Hydrogen is becoming a key energy carrier in the transition toward decarbonization, as electrolysis creates strong interdependencies between electricity and hydrogen markets. Accurately representing strategic behaviour in these coupled markets is essential, yet current models fail to capture price-responsive bidding. To address this, a joint hybrid Cournot-Linear Supply Function Equilibria (CLSFE) model is developed and reformulated as an equivalent optimization problem, enabling tractable large-scale analysis. The model is applied to the Iberian system for 2030 and compared with perfect competition and Cournot benchmarks. Results show that hydrogen prices are lowest under CLSFE, with a reduction of about 44% relative to perfect competition and 10% to Cournot, while hydrogen demand increases by up to 58%. Electrolytic hydrogen production rises up to 92%, displacing grey hydrogen and reducing hydrogen-sector emissions. However, renewable self-curtailment reaches 82 TWh, indicating increased market power. These results highlight cross-sector trade-offs and support market design and policy analysis.

Abstract
As connected and autonomous vehicles become more complex, protecting them against advanced cyber-attacks becomes more difficult. This paper proposes a hardware-based authentication mechanism to protect sensor data acquisition in vehicular networks and embedded systems. The scheme ensures that only authentic sensors and microcontrollers can communicate with the processing unit by adding trusted hardware components, such as trusted platform modules (TPM) and physical unclonable functions (PUF). The system uses lightweight embedded platforms, such as ESP32 and Raspberry Pi with ROS 2, which allow scalable and tamper-proof real-time communication. An exhaustive study of cryptography and hardware-based authentication systems is presented, from elliptic curve cryptography to chaotic map functions, to put the proposed solution in a proper perspective. The system is modularly extensible and adaptable to new technologies, such as blockchains and new encryption protocols, and provides a robust and future-proof solution for securing vehicles and sensor-based IoT sets.

Abstract
OpenMP offers a high-level, portable programming model that simplifies the expression of parallelism and offloading in heterogeneous platforms, making it an ideal framework for managing task execution across CPUs and GPUs. However, as applications increasingly require predictability and multi-GPU execution, the lack of predictability in current OpenMP scheduling mechanisms poses significant challenges. Irregular workloads, variable data transfers, and dynamic resource contention often lead to inconsistent response times and reduced timing determinism. This unpredictability limits system efficiency and reliability, particularly in performance-critical and real-time domains. To overcome the challenge mentioned above, this paper proposes an efficient task-to-accelerator mapping (i.e., assigning tasks to GPU devices for execution) for multi-GPU OpenMP applications. The mechanism includes an offloading manager that operates a global queue and multiple local queues, and different heuristic algorithms that first allocate tasks to the most appropriate queues and then select the most suitable tasks from each queue. The proposed heuristics aim to balance queue loads and enhance the work-conserving property of the mapping process to minimize response time and response-time variability. The paper also presents a thorough evaluation using both synthetic graphs and graphs representative of real-world applications, showing that the proposed mapping method outperforms the other relevant mapping methods in terms of application response time and response-time variability.