Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will operate an internal Single Conjugate Adaptive Optics (SCAO) system, which will mainly serve the science cases targeting exoplanets and disks around bright stars. The Extremely Large Telescope (ELT) is expected to have its first light in 2028, and the entire instrument recently passed its final design phase. The adaptive optics (AO) of METIS SCAO is designed to correct for atmospheric distortions, and is essential for diffraction-limited observations with METIS. The computational and data transfer requirements for these next generation ELT AO Real-Time Computers (RTCs) are enormous, and require advanced data processing and pipelining techniques. METIS SCAO will use a pyramid wavefront sensor (WFS), which captures incoming wavefronts at 1 kHz with a raw throughput of 148 MB/s. The RTC will ingest these WFS images on a frame-by-frame basis, compute the corrections and send them to the deformable mirror M4 and the tip/tilt mirror M5. The RTC is split up into two distinct systems: the Hard Real-Time Computer (HRTC) and the Soft Real-Time Computer (SRTC). The HRTC is responsible for computing the time sensitive wavefront control loop, while the SRTC is responsible for supervising and optimising the HRTC. A working prototype for the HRTC has been completed and operates with an RTC computation time of roughly 372 mu s. This computation is memory limited and runs on two NVIDIA A100 GPUs. This paper shows a breakdown of the HRTC on a CUDA kernel level, focusing on the tasks that run on the GPUs. We also present the performance of the HRTC and possible improvements for it.

Abstract
Pupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing opensource methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science TM, ScienceDirect (R), and Scopus (R) databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN) 1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.

Abstract

Abstract
Context. The deployment of meter-scale (hitherto pre-focal) adaptive deformable mirrors finds some prominent examples in the leading ground-based visible to near-infrared facilities (e.g. the Very Large Telescope (VLT), the Large Binocular Telescope (LBT), or the Magellan Telescope) and is being adopted by several others (e.g. the Multiple Mirror Telescope (MMT) or Subaru). Furthermore, two out of the three giant segmented-mirror telescopes now under design will feature them. In all these cases, the proprietary technology is based on voice-coils and is limited in force, stroke, and velocity. Aims. Because of the nature of their purpose, that is, adaptive wave-front correction, any kind of optimality relies on the control of a subset of principal wave-front components or eigenmodes, for short, a basis of functions in a mathematical sense. Here we provide algorithmic procedures for generating such eigenbases, also called Karhunen–Loève (KL) modes, that integrate force limitations in their definitions whilst maintaining standard orthonormality, statistical independence, and deformable mirror span. Methods. The double-diagonalisation method was revisited to build KL modes ranked by the force applied on the actuators. Results. We analysed this new KL basis for von Kármán turbulence statistics and present the fitting error and the distribution of positions and forces. We further illustrate their use in the case of the quaternary mirror control for the European Extremely Large Telescope, and we include the outer actuator minioning and force policy constraints. © The Authors 2024.

Abstract
This article introduces BetaMemo models, a set of advanced probabilistic forecasting models aimed at predicting the hourly power output of photovoltaic plants. By employing a semiparametric approach based on beta distributions and deterministic models, BetaMemo offers detailed forecasts, including point forecasts, variance, quantiles, uncertainty measures, and probabilities of power generation falling within specific intervals or exceeding predefined thresholds. BetaMemo models rely on input data derived from weather forecasts generated by a Numerical Weather Prediction model coupled with variables pertaining to solar positioning in the forthcoming hours. Eleven BetaMemo models were created, each using a unique combination of explanatory variables. These variables include data related to the location of the plant and spatiotemporal variables from weather forecasts across a broad area surrounding the plant. The models were validated using a real-life case study of a photovoltaic plant in Portugal, including comparisons of their performance with benchmark forecasting models. The results demonstrate the superior performance of the BetaMemo models, surpassing those of benchmark models in terms of forecasting accuracy. The BetaMemo model that integrates the most extensive set of spatiotemporal explanatory variables provides notably better forecasting results than simpler versions of the model that rely exclusively on the local plant information. This model improves the continuous ranked probability score by 13.89% and the reliability index by 45.66% compared to those obtained from a quantile random forest model using the same explanatory variables. The findings highlight the potential of BetaMemo models to enhance decision-making processes related to photovoltaic power bidding in electricity markets.

Abstract
Background: Diet and exercise interventions have been associated with improved body composition and physical fitness. However, evidence regarding their combined effects in older adults is scarce. This study aimed to investigate the impact of a combined 12-week Mediterranean diet-based sustainable healthy diet (SHD) and multicomponent training (MT) intervention on body composition, anthropometry, and physical fitness in older adults. Methods: Diet intervention groups received a weekly SHD food supply and four sessions, including a SHD culinary practical workshop. The exercise program included MT 50 min group session, three times a week, on non-consecutive days. Body composition and physical fitness variables were assessed through dual X-ray absorptiometry, anthropometric measurements, and senior fitness tests. Repeated measures ANOVA, with terms for group, time, and interaction, was performed. Results: Our results showed that a combined intervention significantly lowered BMI and total fat. Also, significant differences between assessments in all physical fitness tests, except for aerobic endurance, were observed. Adjusted models show significant differences in BMI (p = 0.049) and WHR (p = 0.037) between groups and in total fat (p = 0.030) for the interaction term. Body strength (p < 0.001), balance tests (p < 0.001), and aerobic endurance (p = 0.005) had significant differences amongst groups. Considering the interaction term, differences were observed for upper body strength (p = 0.046) and flexibility tests (p = 0.004 sit and reach, p = 0.048 back scratch). Conclusions: Our intervention study demonstrates the potential of implementing healthy lifestyle and sustainable models to promote healthy and active aging.