Abstract
Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape metal parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite advantages, these processes are inherently, as they are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 16 relevant articles sourced from Scopus, IEEE Xplore, and Web of Science databases. The primary objective of this work is to contribute to the advancement of autonomous manufacturing systems capable of self-monitoring and self-correction, ensuring optimal part quality, enhanced efficiency, and reduced human intervention. Our findings indicate that 62.5 % of the 16 analyzed studies have deployed AI-driven controllers in real-world scenarios, with over 56 % using AI for control strategies, such as Reinforcement Learning. Furthermore, 62.5 % of the studies employed AI for process modeling or monitoring, which was integral to the development or data pipelines of the controllers. By defining a groundwork for future developments, this review not only highlights current advancements but also hints future innovations that will likely include AI-based controllers.

Abstract
We describe RebeCaos, a user-friendly web-based front-end tool for the Rebeca language, based on the Caos library for Scala. RebeCaos can simulate different operational semantics of (timed) Rebeca, thus facilitating the dissemination and awareness of Rebeca, providing insights into the differences among existing semantics for Rebeca, and supporting quick experimentation of new Rebeca variants (e.g., when the order of received messages is preserved). The tool also comes with initial reachability analyses for Rebeca models (e.g., the possibility of reaching deadlocks or desirable states). We illustrate the RebeCaos tool by means of a ticket service use case from the timed Rebeca literature.

Abstract
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Abstract
Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.

Abstract
A comparative analysis of Ka and D-band unit cells is presented using a Waveguide Simulator and infinite array models with a Floquet port. Initially, a single-unit cell design is employed with a tapered transition section. Subsequently, a 1 x 2-unit cell is designed and integrated into standard rectangular waveguides WR-34 and WR-7. For the Ka-band, the results obtained from both models exhibit excellent agreement in terms of magnitude and phase. In the D-band, the 1 x 2-unit cell demonstrated low loss for both techniques, and the phase responses were reasonably accurate with differences of less than 40 degrees. At such high frequencies (145-175 GHz), the Waveguide Simulator offers a viable solution for assessing the behavior of the unit cell without the need for a full array.

Abstract
Rebeca is 20+ years old. Introduced by Marjan Sirjani and colleagues for modelling and analysing actor-based systems, it comes with a variety of tool support, including dedicated model checkers, simulators, and code generators. When encountering Rebeca for the first time, either as a student, as a researcher, or as a practitioner from industry, one needs to grasp the subtleties of Rebeca ’s semantics, which includes variants with probabilities and time. This paper presents a user-friendly web-based front-end, based on the Caos library for Scala, to animate different operational semantics of (timed) Rebeca. This can facilitate the dissemination and awareness of Rebeca, provide insights into the differences among existing semantics, and support quick experimentation of new variants (e.g., when the order of received messages is preserved). The tool is illustrated by means of a ticket service use case from the literature. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.