Abstract
Growing obesity has been a worldwide issue for several years. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. Today, people have a very fast-paced life and sometimes neglect food choices. In order to simplify the interpretation of the Nutri-score labeling this paper proposes a method capable of automatically reading food labels with this format. This method is intended to support users when choosing the products to buy based on the letter identification of the label. For this purpose, a dataset was created, and a prototype mobile application was developed using a deep learning network to recognize the Nutri-score information. Although the final solution is still in progress, the reading module, which includes the proposed method, achieved an encouraging and promising accuracy (above 90%). The upcoming developments of the model include information to the user about the nutritional value of the analyzed product combining it's Nutri-score label and composition.

Abstract
This study investigated the effectiveness of a coil-shaped optical fiber interferometric sensor, with a diameter of 13 mm, for measuring compression. The sensor's design utilizes the principles of interferometry to create a pattern that changes with applied pressure. This configuration significantly amplifies the sensor's sensitivity to compression due to the extended optical path length within the compact form factor. The experimental results demonstrated that even small compressive forces caused detectable alterations in the interference pattern, allowing for precise quantification of pressure changes. The 13 mm diameter proved to be particularly advantageous, providing a balance between sensitivity and practical integration into various systems, from structural health monitoring to biomedical devices. This study also highlights the sensor's robustness against electromagnetic interference and environmental variations, attributing this to the intrinsic properties of optical fiber. Overall, the findings suggest that coil-shaped optical fiber interferometric sensors are highly effective for accurate and reliable compression sensing, with potential for broad application across multiple industries. © The Authors.

Abstract
Hydropower currently generates more than all other renewable energies combined. Considering the challenges of climate change and the transition to green energy, it is expected to remain the world's largest source of renewable electricity generation. This paper proposes a tool for performance evaluation and benchmarking of hydropower generation to inform dispatching. Through them, strengths and weaknesses of asset operations can be set, identifying areas with the best performance, gathering insights from their strategies and best practices, and comprehending factors that lead to variations in performance levels. The results allow for optimising energy resource use by indicating the dispatching rules with maximum power production and minimum wearand-tear impact. This framework allows the formulation of practical guidelines for dispatching policies. The proposed methodology is applied to analyse two real-world case studies: the Vogelgr & uuml;n run of river hydropower plant (France) and the Frades 2 pump-storage powerplant (Portugal).

Abstract
Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.

Abstract
The GNAO facility is an upcoming adaptive optics (AO) system for the Gemini North Telescope. It will deliver both wide and narrow field AO capabilities to its first light instrument GIRMOS. GIRMOS is a multi-object AO (MOAO) instrument that houses four near infrared (NIR) IFU spectrographs and a NIR imager similar to GSAOI at Gemini South. The required sensitivity of the combined system is largely driven by rapid transient followup AO-corrected Imaging and the required sensitivity is in part driven by the performance of the AO system. Up until recently, the estimated AO performance feeding the combined GNAO+GIRMOS imaging system was derived from models using limited information on what the actual parameters will eventually be. However, the AO system (currently called the AO Bench, or AOB) recently underwent a competitive bidding process to derive an AO design that met or exceeded our AO requirements. This work summarizes the update to the combined GNAO+GIRMOS imaging system performance based on the newly designed AOB parameters. We discuss the impact due to the changes in performance, specifically with respect to key science cases of the GNAO+GIRMOS imaging system compared to the previous models of the AO system. We also discuss the largest hurdles in terms of parameters that affect performance, such as telescope vibrations and detector quantum efficiency and our plans for mitigation.