Abstract
Bringing AI models from digital to real-world applications presents significant challenges due to the complexity and variability of physical environments, often leading to unexpected model behaviors. We propose a framework that learns to translate images into control actions by modeling multimodal real-time data and system dynamics. This end-to-end controller offers enhanced explainability and robustness, making it well suited for complex manufacturing processes. This end-to-end framework differs from traditional approaches that rely on manually engineered features by learning complex relationships directly from raw data. Labels are only required during training to define the observable feature to be optimized. This adaptability significantly reduces development time and enhances scalability across varying conditions. This approach was tested in the Directed Energy Deposition (L-DED) process, a laser-based metal additive manufacturing technique that produces near-net-shape parts with exceptional energy efficiency and flexibility in both geometry and material selection. L-DED is inherently complex, involving multiphysics interactions, multiscale phenomena, and dynamic behaviors, which make modeling and optimization difficult. Effective control is crucial to ensure part quality in this dynamic environment. To address these challenges, we introduce Joint Embedding Multimodal Alignment with Sparse Identification of Nonlinear Dynamics for control (JEMA-SINDYc). It combines an image-based JEMA monitoring model, which predicts the melt pool size using only the on-axis sensor with labels provided by the off-axis camera, and dynamic modeling using SINDYc, which acts as a World Model by capturing system dynamics within the embedding space. Together, these components enable the development of an advanced controller trained via Behavioral Cloning. This approach improves part quality by minimizing porosity and reducing deformation. Thin-walled cylindrical parts were produced to validate and compare this approach with other control strategies, including both open-loop and JEMA-PID. This framework improves the reliability of AI-driven manufacturing and enhances control of complex industrial processes, potentially enabling wider adoption of the process.

Abstract
Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.

Abstract
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to no attention has been given to date to the material reception process at the mill. Another factor that motivated this study was the formation of large waiting queues at the mill gates and docks. Queues increase the reception time and associated costs. This work presents the development of a scheduling and reception system for deliveries at a mill. The scheduling system is based on Trucking Appointment Systems (TAS), commonly used at maritime ports, and on revenue management concepts. The developed system allocates each delivery to a timeslot and to an unloading dock using revenue management concepts. Each delivery is segmented according to its priority. Higher-segment deliveries have priority when there are multiple candidates to be allocated for one timeslot. The developed scheduling system was tested on a set of 120 daily deliveries at a Portuguese paper pulp mill and led to a reduction of 66% in the daily reception cost when compared to a first-in, first-out (FIFO) allocation approach. The average waiting time was also significantly reduced, especially in the case of high-priority trucks.

Abstract
Magneto-optic surface plasmon resonances (MOSPRs) rely on the interaction of magnetic fields with surface plasmon polaritons (SPP) to modulate plasmonic bands with magnetic fields and enhance magneto-optical activity. In the present work, a study on the magnetoplasmonic behavior of Ag/Fe bilayers is carried out by VIS-NIR spectroscopy and backed with SQUID measurements, determining the thickness-dependent magnetization of thin-film samples. The MOSPR sensing properties of Ag/Fe planar bilayers are simulated using Berreman's matrix formalism, from which an optimized structure composed of 15 nm of Ag and 12.5 nm of Fe is obtained. The selected structure is fabricated and characterized for refractive index (RI) sensitivity, reaching 4946 RIU-1 and returning an effective enhancement of refractometric sensitivity after magneto-optical modulation. A new optimized and cobalt-free magnetoplasmonic Ag/Fe bilayer structure is studied, fabricated, and characterized for the first time towards refractometric sensing, to the best of our knowledge. This configuration exhibits potential for enhancing refractometric sensitivity via magneto-optical modulation, thus paving the way towards a simpler, more accessible, and safe type of RI sensor with potential applications in chemical sensors and biosensors.

Abstract

Abstract
Motorcycle accidents contribute significantly to road traffic fatalities worldwide. Advanced Rider Assistance Systems are systems designed to improve safety by providing real-time monitoring, collision detection, and adaptive control. However, implementing these systems on low-power hardware poses some challenges. This work examines the usage of object detection models on edge computing devices, with a focus on the Raspberry Pi 4. In this work are evaluated some models like SSD, and YOLO models using a custom dataset based on BDD100K dataset. All the models used in the project were converted to Open Neural Network Exchange format and tested at resolutions of 320×320 and 640×640 to assess their efficiency and real-time applicability. The results indicate that YOLO-based models, particularly YOLOv11, give the best balance between accuracy and inference speed, making them a candidate for Advanced Rider Assistance Systems applications. Despite these advancements, challenges such as real-time constraints and hardware limitations remain. Future research should focus on model quantization and hardware acceleration to improve deployment feasibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.