Abstract
Highlights What are the main findings? UAV multispectral data combined with machine learning enabled the estimation of grapevine biophysical parameters, including LAI, pruning wood biomass, and yield. Geometric features, such as canopy area and volume, improved model accuracy and reduced the number of predictors, while the integration of spectral and geometric data improved prediction robustness across different phenological stages. What is the implication of the main finding? UAV-based monitoring can be applied in different grapevine varieties without cultivar-specific calibration, providing a non-invasive tool for vineyard assessment. Identifying the most informative features and suitable acquisition periods supports more accurate decisions in vineyard management, including pruning, canopy control, and yield estimation.Highlights What are the main findings? UAV multispectral data combined with machine learning enabled the estimation of grapevine biophysical parameters, including LAI, pruning wood biomass, and yield. Geometric features, such as canopy area and volume, improved model accuracy and reduced the number of predictors, while the integration of spectral and geometric data improved prediction robustness across different phenological stages. What is the implication of the main finding? UAV-based monitoring can be applied in different grapevine varieties without cultivar-specific calibration, providing a non-invasive tool for vineyard assessment. Identifying the most informative features and suitable acquisition periods supports more accurate decisions in vineyard management, including pruning, canopy control, and yield estimation.Abstract The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R2 = 0.83) and yield (R2 = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R2 = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards.

Abstract
Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss and affect 1 in 3000 individuals worldwide. Their rarity and genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative models offer a promising solution by creating synthetic data to improve training datasets. This study carried out a systematic literature review to investigate the use of generative models to augment data in IRDs and assess their impact on the performance of classifiers for these diseases. Following PRISMA 2020 guidelines, searches in four databases identified 32 relevant studies, 2 focused on IRD and the rest on other retinal diseases. The results indicate that generative models effectively augment small datasets. Among the techniques identified, Deep Convolutional Adversarial Generative Networks (DCGAN) and the Style-Based Generator Architecture of Generative Adversarial Networks 2 (StyleGAN2) were the most widely used. These architectures generated highly realistic and diverse synthetic data, often indistinguishable from real data, even for experts. The results highlight the need for more research into data generation in IRD to develop robust diagnostic tools and improve genetic studies by creating more comprehensive genetic repositories.

Abstract
Polarization optical fiber sensors are based on modifications of fiber birefringence by an external measurand (e.g., strain, pressure, acoustic waves). Yet, this means that different input states of polarization will result in very distinct behaviors, which may or may not be optimal in terms of sensitivity and signal-to-noise ratio. To tackle this challenge, this manuscript presents an optimization technique for the input polarization state using the Fisher information formalism, which allows for achieving maximal precision for a statistically unbiased metric. By first measuring the variation of the Mueller matrix of the optical fiber in response to controlled acoustic perturbations induced by piezo speakers, we compute the corresponding Fisher information operator. Using maximal information states of the Fisher information, it was possible to observe a significant improvement in the performance of the sensor, increasing the signal-to-noise ratio from 4.3 to 37.6 dB, attaining an almost flat response from 1.5 kHz up to 15 kHz. As a proof-of-concept for dynamic audio signal detection, a broadband acoustic signal was also reconstructed with significant gain, demonstrating the usefulness of the introduced formalism for high-precision sensing with polarimetric fiber sensors. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

Abstract

Abstract
Industry 4.0 is transforming manufacturing environments, with robotics being a key technology that enhances various capabilities. The flexibility of Autonomous Mobile Robots has led to the rise of multi-robot systems in industrial settings. Considering the high cost of these robots, it is essential to determine the best fit of number and type before making any major investments. Simulation and modeling are valuable decision-support tools, allowing the simulation of different setups to address robot fleet sizing issues. This paper introduces a decision-support framework that combines a fleet manager software stack with the FlexSim simulator, helping decision-makers determine the most suitable mobile robots fleet size tailored to their needs. Unlike previous approaches, the developed solution integrates the same real robot coordination software in both simulation and actual deployment, ensuring that tested scenarios accurately reflect real-world conditions. A case study was conducted to evaluate the framework, involving multiple tasks of loading and unloading materials within a warehouse. Five different scenarios with varying fleet sizes were simulated, and their performances assessed. The analysis concluded that, for the case study under consideration, a fleet of three robots was the most suitable, considering relevant key performance indicators. The results confirmed that the developed solution is an effective alternative for addressing the problem and represents a novel technology with no prior state-of-the-art equivalents.

Abstract
Deploying renewable energy communities, self-consumption and local energy markets are one of the ways to contribute to the energy system decarbonization by increasing the renewable energy share in the production mix and contributing to a better local balancing. However, how collective self-consumption structures are regulated has a direct impact on the flexibility of the energy sharing mechanisms and business models that can be set up. This paper compares and discusses how the European Union directives on self-consumption have been transposed to the national regulations of Portugal, Spain and France, providing a detailed regulatory discussion on the definition of basic concepts such as individual and collective self-consumption and renewable energy communities, proximity rules among members, energy sharing mechanisms and energy allocation coefficients, how the energy surplus is managed in each case, or how the grid access tariffs are modified to account for the self-consumed energy. The study highlights that dynamic allocation coefficients provide significant advantages for collective self-consumption by improving energy allocation efficiency, enabling advanced business models, and facilitating the integration of local energy markets, as it is the case in Portugal and France, while their absence in Spain limits these opportunities. The work also highlights the trade-off between flexible energy sharing and implementation complexity, and the role of digital tools to operationalize energy communities. Suggestions on potential regulatory improvements for all countries are also proposed.