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.

Abstract
Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

Abstract
Widespread adoption of distributed energy resources led to changes in low -voltage power grids, turning prosumers into active members of distribution networks. This incentivized the development of consumercentric energy markets. These markets enable trades between peers without third -party involvement. However, violations in network technical constraints during trades challenges integration of market and grid. The methodology used in this work employs batteries to prevent network violations and improve social welfare in communities. The method uses sequential simulations of market optimization and distribution network power flows, installing batteries if violations are identified. Simulation solves nonlinear deterministic optimization for market trades and results are used in power flow analysis. The main contribution is assessing battery participation in energy markets to solve distribution network violations. Case studies use realistic data from distribution grids in Costa Rica neighborhoods. Results indicate potential gains in social welfare when using batteries, and case -by -case analysis for prevention of network violations.

Abstract
Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will be one of the first instruments to be used at ESO's 39m Extremely Large Telescope (ELT), that is currently under construction. With that, a number of firsts are to be addressed in the development of METIS' single-conjugate Adaptive Optics (SCAO) system: the size of the telescope and the associated complexity of the wavefront control tasks, the unique scientific capabilities of METIS, including high contrast imaging, the interaction with the newly established, integrated wavefront control infrastructure of the ELT, the integration of the near-infrared Pyramid Wavefront Sensor and other key Adaptive Optics (AO) hardware embedded within a large, fully cryogenic instrument. METIS and it's AO system have passed the final design review and are now in the manufacturing, assembly, integration and testing phase. The firsts are approached through a compact hard- and software design and an extensive test program to mature METIS SCAO before it is deployed at the telescope. This program includes significant investments in test setups that allow to mimic conditions at the ELT. A dedicated cryo-test facility allows for subsystem testing independent of the METIS infrastructure. A telescope simulator is being set up for end-to-end laboratory tests of the AO control system together with the final SCAO hardware. Specific control algorithm prototypes will be tested on sky. In this contribution, we present the progress of METIS SCAO with an emphasis on the preparation for the test activities foreseen to enable a successful future deployment of METIS SCAO at the ELT.