Abstract
General-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.

Abstract
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be available. Moreover, global models may fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of time series datasets. The main contribution of this work is a novel method for generating univariate time series synthetic samples. Our approach stems from the insight that the observations concerning a particular time series of interest represent only a small fraction of all observations. In this context, we frame the problem of training a forecasting model as an imbalanced learning task. Oversampling strategies are popular approaches used to handle the imbalance problem in machine learning. We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches.

Abstract
Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions.

Abstract
Traffic state prediction is critical to decision-making in various traffic management applications. Despite significant advancements in Deep Learning (DL) models, such as Long Short-Term Memory (LSTM), Graph Neural Networks (GNN), and attention-based transformer models, multi-step predictions remain challenging. The state-of-the-art models face a common limitation: the predictions' accuracy decreases as the prediction horizon increases, a phenomenon known as error accumulation. In addition, with the arrival of non-recurrent events and external noise, the models fail to maintain good prediction accuracy. Deep Reinforcement Learning (DRL) has been widely applied to diverse tasks, including optimising intersection traffic signal control. However, its potential to address multi-step traffic prediction challenges remains underexplored. This study introduces an Actor-Critic-based adapted DRL method to explore the solution to the challenges associated with multi-step prediction. The Actor network makes predictions by capturing the temporal correlations of the data sequence, and the Critic network optimises the Actor by evaluating the prediction quality using Q-values. This novel combination of Supervised Learning and Reinforcement Learning (RL) paradigms, along with non-autoregressive modelling, helps the model to mitigate the error accumulation problem and increase its robustness to the arrival of non-recurrent events. It also introduces a Denoising Autoencoder to deal with external noise effectively. The proposed model was trained and evaluated on three benchmark traffic flow and speed datasets. Baseline multi-step prediction models were implemented for comparison based on performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal that the proposed method outperforms the baselines by achieving average improvements of 0.26 to 21.29% in terms of MAE and RMSE for up to 24 time steps of prediction length on the three used datasets, at the expense of relatively higher computational costs. On top of that, this adapted DRL approach outperforms traditional DRL models, such as Deep Deterministic Policy Gradient (DDPG), in accuracy and computational efficiency.

Abstract
This study explores the application of the Lean Inception methodology in developing “EcoRider: Green Adventure,” an educational game aimed at enhancing motorcycle safety and promoting environmental awareness. Funded by the A-MoVeR project under the European Recovery and Resilience Facility, the game educates players on advanced safety technologies such as radars, cameras, LiDAR, and artificial intelligence (AI) algorithms. Players navigate complex urban scenarios, learning to manage potential hazards and promoting ecofriendly urban mobility. Using a qualitative case study approach, the research evaluates the effectiveness of integrating these technologies into the game’s design and gameplay. The game features multiple levels with increasing difficulty, requiring players to strategically place sensors and use AI models to overcome challenges. The application of the Lean Inception methodology has been essential in aligning the development team’s efforts, ensuring a cohesive approach to delivering a minimum viable product that satisfies both educational and technological objectives. Future work will be on refining the game, expanding its scope and exploring additional applications in the wider context of sustainable and safe mobility. © 2025 Elsevier B.V., All rights reserved.

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.