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.

Abstract
Background: The development of ulcers in the plantar region of the diabetic foot originates mainly from sites subjected to high pressure. The monitoring of these events using maximum allowable pressure thresholds is a fundamental procedure in the prevention of ulceration and its recurrence. Objective: The aim of this review was to identify data in the literature that reveal an objective threshold of plantar pressure in the diabetic foot, where pressure is classified as promoting ulceration. The aim is not to determine the best and only pressure threshold for ulceration, but rather to clarify the threshold values most used in clinical practice and research, also considering the devices used and possible applications for offloading plantar pressure. Design: A systematic review. Methods: The search was performed in three electronic databases, by the PRISMA methodology, for studies that used a pressure threshold to minimize the risk of ulceration in the diabetic foot. The selected studies were subjected to eligibility criteria. Results: Twenty-six studies were included in this review. Seven thresholds were identified, five of which are intended for the inside of the shoe: a threshold of average peak pressure of 200 kPa; 25 % and 40-80 % reduction from initial baseline pressure; 32-35 mm Hg for a capillary perfusion pressure; and a matrix of thresholds based on patient risk, shoe size and foot region. Two other thresholds are intended for the barefoot, 450 and 750 kPa. The threshold of 200 kPa of pressure inside the shoe is the most agreed upon among the studies. Regarding the prevention of ulceration and its recurrence, the efficacy of the proposed threshold matrix and the threshold of reducing baseline pressure by 40-80 % has not yet been evaluated, and the evidence for the remaining thresholds still needs further studies. Conclusions: Some heterogeneity was found in the studies, especially regarding the measurement systems used, the number of regions of interest and the number of steps to be considered for the threshold. Even so, this review reveals the way forward to obtain a threshold indicative of an effective steppingstone in the prevention of diabetic foot ulcer.

Abstract
Organizations can transform their businesses and create more value by adopting Industry 4.0 initiatives. During evaluating these projects, the decision-maker must assess significant uncertainties (risks) resulting from socio-technical, economic, and financial factors. One of the main objectives of this study was to identify the necessary building blocks to develop a framework for project implementation in high-risk scenarios, as in the case of Industry 4.0. A multi-criteria framework divided into three stages was proposed, integrating knowledge from Front-End-Innovation (FEI), Innovation Decision Process (IDP), Traditional Project Evaluation Methods, and Real Options Valuation (ROV). The first step is to identify an investment opportunity. The second step is the definition of a business model. The third step is the simulation of different implementation strategies to give managerial flexibility to decision-makers to decide the best strategy to mitigate risks. A real case study was used to test the framework. According to the results, managers can use this framework to create different project implementation scenarios and determine the best strategy to mitigate risks. However, we must still understand whether uncertainties behave discretely, dynamically, or both, the interactions between elements, and how to calculate them to improve our model.

Abstract
Enlarged excavation of the optic papilla, caused by the loss of fibres that originate in the retina and transmit electrical stimuli to the visual cortex, is a critical indicator in the early detection of glaucoma, a disease that can lead to irreversible blindness. As the optic papilla shows morphological variations in the population, its identification can be a challenge. Methods based on deep learning have shown promise in helping doctors analyse these images more accurately. Recently, models such as Vision Transformers (ViT) have shown significant results in various medical applications, including glaucoma detection. However, the scarcity of quality data remains a major obstacle to training these models. This study evaluated the performance of the Swin Transformer, DeiT and Linformer models in detecting optic papilla excavation, using the new Brazilian Multilabel Ophthalmological Dataset (BRSET). The results showed that the DeiT model obtained the best accuracy, with 0.94, followed by the Swin Transformer, with 0.88, and the Linformer, with 0.85. The findings of this study suggest that ViT models can not only significantly improve the detection of glaucomatous papillary excavation, but also strengthen Human-Machine Collaboration, promoting more effective interaction between doctors and automated systems in medical diagnosis.